In this post, User Experience team intern Hannah Watson shares her work over the summer researching digital content accessibility with the EdWeb 2 publishing community.

Introduction

As part of my internship with the User Experience Service, I have been investigating digital accessibility at the content level across University web pages, particularly on EdWeb 2 sites. Digital accessibility at the content level is about applying principles of accessibility to how web content is written and formatted. This includes, but is not limited to, content features such as heading levels, links, and alt text. It does not include anything that is not involved with content design or that is controlled at a higher level by the Content Management System (CMS), such as font, font size, or colour contrast.

Research aims and scope

The specific research questions for this project were:

• How do web publishers learn about digital content accessibility?
• What do web publishers know about digital content accessibility?
• How do web publishers implement digital accessibility requirements and principles in their content?
• What challenges do web publishers face in creating digitally accessible content?

More comprehensively, I was investigating the accessibility of:

• Heading levels
  • Ensuring that heading levels are used correctly
  • Not using heading levels for emphasis
• Links
  • Clear and concise link text which describes the linked destination
  • Link text which makes sense on its own
  • Avoiding URLs on web pages
• Lists
• Alt text
  • Clear link text that describes the image
• Images
  • Ensuring that all images have appropriate alt text
  • Avoiding images of text
• Videos
  • Including human-corrected captions with any videos uploaded to web pages
  • Making sure that transcripts are available for all videos uploaded to web pages
• Italic, bold, and underlined text

For more detailed guidance on these topics, please refer to the University of Edinburgh editorial style guide.

Editorial style guide | Information Services

This research has been necessary for the User Experience team as it allows us to identify which areas of content accessibility are challenging for web publishers. From this, the team can adapt guidance and training to provide extra support on these more challenging areas where possible.

Research methods

Interviews with web publishers

I started my research by setting up short, informal interviews with six University web publishers. In these interviews, I asked the publishers about their experiences of creating digitally accessible content. The aim of these conversations was to understand how publishers learned about digital accessibility and what challenges they face when making content as accessible as it can be. During the interviews, we referred to web pages that these publishers work on to get concrete examples that illustrate the topics we discussed. Through these interviews, I was able to identify a number of trends, particularly in the challenges that the interviewees and their colleagues face.

Survey of EdWeb 2 publishers and the Web Accessibility Special Interest Group

Following these interviews, I created a survey to further investigate the findings and collate more supporting evidence for these findings. The survey consisted of 10 questions, three of which were demographic based as a filter, with a final question asking permission to follow up with those who responded.

The other six questions asked about:

• which actions the participants took to make their content digitally accessible
• what resources they used to do so
• what challenges they face in doing so

The findings from the survey were effective in making the information gathered in interviews more robust and provided further evidence for some trends that were identified previously.

To publicise this survey, I sent a brief statement explaining my research into two Teams channels, the Web Accessibility Special Interest Group and the EdWeb 2 Community. Overall, the survey received five responses, and while this is a limited number, I found that it helped to support the findings from the interviews.

Analysis of Effective Digital Content workbooks

The final method of research that I used to learn about the digital accessibility of content on University web pages was by looking at pages that were submitted within Effective Digital Content workbooks as part of the course. The course requires learners to choose pages from a University website and assess the effectiveness of the content. By looking at the pages that learners selected, I was able to use active examples and make note of content accessibility issues that were present on live pages. This process also highlighted a number of trends.

This method of research was also useful in that it provided two separate sources of data. Firstly, the answers in the workbook helped me to gauge learners’ understanding of the principles covered in the course. Secondly, the web pages linked by those taking the course allowed me to have live examples of content to assess against content accessibility principles. While there was not necessarily overlap between the answers in the workbook and the pages submitted as part of the workbook (as some people may not have been fully or at all responsible for the content on those pages, or the content could have been updated since the workbook was submitted), it was useful to see these things separately.

Findings about accessibility

Combining findings from all areas of research for this project, I have identified a number of trends in the accessibility of digital content.

Headings, alt text, and links are the principles most often put into practice by participants

In the interviews and the survey, participants were asked what principles of accessible digital content they actively used when designing content. More than half of participants mentioned three principles in particular, with all participants mentioning at least two, which were:

• using the correct heading levels
• adding meaningful alt text to an image
• writing clear and descriptive link text

Interestingly, while these principles were mentioned frequently by participants, and evidenced by the websites that we discussed in interviews, they are also principles that are often missed on University web pages. This is discussed in more detail in the corresponding sections further on in this post.

PDFs are hard to avoid

One standout finding from the interviews was that web publishers sometimes struggle to find an effective alternative to PDFs. While PDFs are not necessarily accessible, they do have benefits which make them useful for publishers. For example, they are downloadable, searchable, and cannot be easily edited without permission from the owner. There are ways to make PDFs more accessible, such as avoiding decorative images, adhering to accessible content design principles within the PDF, and checking colour contrast. However, the interviewees were more in favour of finding a way to turn their content into a webpage as this is more likely to result in accessible content.

Out of the survey responses, three also mentioned that they had recently chosen to publish content as a web page rather than as a PDF, highlighting their knowledge that a web page is preferable in terms of accessibility. However, their personal preferences or how difficult they found this is unknown as I was unable to follow up with these participants.

Heading levels are often skipped and headings vague

Across web pages that I assessed for correct heading levels, there were several which skipped heading levels throughout the page content, such as going straight to a heading 3 without that heading being nested within a heading 2 section. Additionally, headings on the University web pages that I investigated were often generic, instead of being specific about what a page or section will contain, which is the recommended approach.

The fact that the web publishers who were interviewed and surveyed were aware of the importance of correct headings levels and specific headings, and that the majority of these publishers have attended a staff training or used the editorial style guide, suggests that the guidance provided is accurate and useful for publishers.

To increase the use of correct heading levels, as well as clear and descriptive headings on web pages, participant responses suggest that increasing the reach and engagement of existing training and resources involving headings would be effective. This includes training provided by the User Experience Service, such as Effective Digital Content or Content Improvement Club, and the University of Edinburgh editorial style guide.

Alt text is well written but sometimes missed

In three of the interviews, and in two survey responses, participants mentioned that they struggled to find the time to add alt text to images on their web pages. However, participants in the interviews also stated that they understood the importance of alt text, and what writing meaningful and clear alt text involves. This is reflected in the answers in Effective Digital Content workbooks. The course contains a question asking learners to write meaningful alt text for two images, and this question is often answered well. This suggests that web publishers understand how to write alt text, and that the obstacle in doing so is more likely to be related to time and resource.

One way that time constraints on adding alt text could be improved is to reduce the number of images on a web page, which will not only make this task more manageable but also is also more sustainable.

Explaining acronyms and abbreviations is common

All five responses to the survey stated that they had explained an acronym or abbreviation recently. Although this did not come up in the interviews often – only once – the survey responses suggest that this is common practice for web publishers who are familiar with digital accessibility principles.

Link text is frequently inline or not descriptive

Participants stated that they understand the importance of clear link text as a principle and make effort to implement this into their content. However, similar to headings, this sentiment is not reflected across numerous University web pages. In some of the pages submitted as part of the Effective Digital Content workbooks that I investigated, there were frequent occurrences of inline link text, or link text that does not clearly describe the linked destination.

To increase the writing of link text on a separate line, as well as clear and descriptive link text, participant responses suggest that increasing the reach and engagement of existing training and resources involving links would be effective. This includes training provided by the User Experience Service, such as Effective Digital Content or Content Improvement Club, and the University of Edinburgh editorial style guide.

Findings about staff experience and engagement

A trend from both the interviews and survey responses is that for many of the people involved with this research, their knowledge of digital accessibility started with a personal interest. Specific examples of this that participants mentioned include learning about accessibility as a student (and then going on to be an accessibility advocate for a student society) and working with disabled students and learning through experience.

During the interviews, multiple staff members mentioned that they believe, through their experiences, that a large part of issues with creating accessible digital content at the University surrounds communication. A combination of factors was discussed that had communication at the centre, including:

• the importance of digital accessibility not being widespread enough.
• consistency about expectations between schools or areas of the University, such as one page being edited by multiple schools or areas and having different standards.

Job-based constraints were also mentioned frequently, such as limited time to add alt text to all images on a web page and working on a page where the lead publisher takes a design forward approach which can sometimes clash with accessible content principles. These answers were also reflected in the survey responses, with all of the responses mentioning either one or both of these problems.

What resources do web publishers use for learning about and developing their digital accessibility skills?

The primary resource that participants mentioned using to learn about digital accessibility and develop their skills was University-provided staff training, with 10 of 11 participants mentioning this. Effective Digital Content was specifically mentioned twice, and Content Improvement Club three times. In the interviews, two participants mentioned more general staff training, with one survey response saying the same. In the survey responses, three participants also said that they used training provided by the Disability Information Team.

The Web Content Accessibility Guidelines 2.2 (WCAG) is another resource that was frequently mentioned by participants, with six in total saying that this is a resource that they use as guidance on accessibility.

The University of Edinburgh editorial style guide was mentioned by five participants as a resource that they used to provide guidance on digital accessibility, in which the guidance reflects what publishers learn in training courses, meaning that information they take from the style guide is in line with accessibility and content design training.

What I learned from researching content accessibility approaches in EdWeb

I learned a lot during my time researching how web publishers at the University of Edinburgh approach creating accessible digital content. I thoroughly enjoyed the opportunity to work with staff from a variety of different areas of the University. This helped me to learn how to identify commonalities in interview and survey responses despite the areas of work being distinct. I also enjoyed this because I was able to learn much more about the work that goes on across the University and what the work of other teams involved.

I also particularly enjoyed the format of my internship being a combination of individual work and working with other members of my team and the Disability Information team. This balance allowed me to set my own goals while still getting the opportunity to work and learn collaboratively as part of a team, prioritising my tasks between my own and those that I was working on with others.

A limitation of my work was that it was much easier to contact and work with staff who have a genuine interest in the subject area of accessibility, which does not lead to research that is representative of how University staff approach digital accessibility as a whole. Having done the research that I have so far, a continuation of the research would be most beneficial if it focused on the experiences and approaches of staff who are less familiar with digital accessibility requirements and principles, as this would create a more well-rounded understanding of web publisher accessibility approaches at the content level.

Furthermore, another limitation of the work was the time constraints which have restricted my ability to develop solutions to issues identified during the research period. This was expected to a degree, as the solutions depended on the outcomes of the research, which has taken the majority of the 12-week period. The positive outcome of this is that the research and findings will provide the User Experience service with information that allows for both further research and for adaptations to guidance and training if it is deemed necessary.

Conclusion

This research has helped the User Experience Service to assess their training and the resources that are available for publishers to learn more about digital content accessibility. Keeping in touch with the publishing community through projects like this helps the team to direct their effort to real challenges that publishers face on a day to day basis.

The research completed throughout the duration of this project will be supported and advanced by further investigation, particularly by communicating with web publishers who are less involved with digital accessibility as a whole. This will likely help in providing more concrete solutions to digital content accessibility issues across EdWeb 2 pages.

Content audits are a long-time staple of the user-centred website toolkit, but they take time and effort to complete, which many website owners struggle to find. In my ongoing AI experimentation, I tried using AI tools to help me with auditing web content.

Every day brings a raft of new AI developments. When choosing how to use AI, I’m less sold on getting it to do creative tasks for me because I like doing those myself. Instead, in pursuit of freeing up my time, I prefer to use AI to help me with the tedious tasks I never get around to, the ones I know will take me longer than I think, the ones I put off again and again. Here’s looking at you, content auditing.

Websites are easy to grow but difficult to keep in check

When people come to the UX Service for help with their websites, they tend to use several phrases to describe their site. These have included:  ‘It’s a mess!’ ‘It’s a bit out of control’. ‘It’s grown arms and legs’. None of this is unusual when you consider the lifecycle of websites. Over time, websites change hands. Pages once carefully crafted are easily forgotten as new editors take over. New content is created without knowing what’s already there. Very often, content needs to be published to a deadline and it’s quicker to publish a new page than seek out and amend an existing one. Seldomly do publishers want to get rid content that’s taken time and effort to produce.

Read some thoughts on content housekeeping by UX Service team members (current and past):

Digital housekeeping – applying content management practices to improve digital sustainability by me

Be a gardener by Ari Cass-Maran

Content Audit Findings and the 100k Challenge by Milo McLaughlin

How to get a grip of your website (and then keep hold) by Neil Allison

A content audit is the best place to start improving a website (not the homepage)

When people seek to bring order to disarray on a website it’s difficult know where to start, so typically, they start on the homepage with ideas to change images or add new content. While this gives sites a superficial makeover, it doesn’t really help site audiences in search of content. Fewer and fewer website audiences are starting out on homepages, instead they’re parachuting directly into site content from Google search and increasingly, from AI summaries. What does this mean? It’s more important than ever to make sure your web content is up-to-date, accurate, useful and relevant for your website users. Here’s looking at you, content auditing.

Read more about getting your content ready for AI from a recent Content Improvement Club session:

Auditing a website needs a methodical approach (which is where AI can help)

If audits are so important, why are they so easy to put off? Short answer, they can take prolonged amounts of time and concentrated effort. The larger and more complex your site is, it’s likely that there will be more content to review, and the more difficult it may be to maintain a consistent auditing approach. Going through multiple, long pages of web content, it can be easy to run out of the time you’ve allocated for your audit. Breaking the audit into chunks may lead to different results from different sessions as biases creep in. Bringing colleagues in to help can share the load, but can also introduce new perspectives and contradictory auditing decisions. There’s a need to maintain ruthless objectivity, to counter human subjectivity and avoid errors infiltrating. Here’s looking at you, AI.

If you can plan and describe a website content audit, you can ask AI to help you do it

Website content audits can be done for many purposes, but commonly, doing a content audit involves making decisions about what content to keep, what needs to go and what needs to be changed – all in pursuit of reaching a future state of your website. A good way to decide what content to keep is to consider who it’s for and the purposes it serves. If a piece of content doesn’t meet audience needs and doesn’t align with the site purpose, then it’s probably a candidate for deletion. A good content audit of a website therefore typically requires several things:

1. An inventory of every piece of content in the site. Typically arranged in a spreadsheet, with one row per webpage. Can be organised into sections or content types, or grouped by root URL
2. A note of the site audiences, ideally in order of priority (answering the question ‘Who is this site primarily for?)
3. A list of the main reasons those audiences visit the site (answering the question ‘What tasks do people complete on this site?’) together with a list of the main goals of your site (answering the question ‘What do we want people to do on this site?’)

Considering each of these things as different pieces of data, where item 1 is data to be audited, items 2 and 3 are factors or auditing criteria used to decide what happens to those data (typically one of three outcomes: ‘Keep’, ‘Delete’ or ‘Modify’), I reasoned that I could give AI these data, tell it what a content audit was and and ask it to complete this process for a given website.

I experimented with ELM to help me do a content audit of a website

The University’s AI platform, ELM was the natural place to start experimenting with applying AI to aid content auditing. The ELM interface allows you to input a prompt, as well as add additional files and data sources. It also allows you to select different models to handle queries. I was curious to see the differences between the different models – both non-reasoning ones and reasoning ones so I needed to design a prompt that would work for both.

Read more about ELM and its models

ELM website

ELM new model guides (University log in required)

Being mindful of the environmental cost, I wanted to start with a small AI model (Llama 3.3)

AI comes with a significant environmental cost, which is highly dependent on the LLMs you use. Powerful models with more reasoning power are especially resource-intensive to run, so to avoid unnecessary wastage of tokens and other resources I’ve found it’s sometimes best to start with a smaller model and size up accordingly, as the need arises. The trade-off is that smaller models have limited reasoning power, however, so the responses they provide and the tasks they are able to complete can be limited.

Based on my previous experience of using AI and thinking about its potential application to a typical content auditing process, I had a hunch that providing a small model with something like a site inventory (such as a XML sitemap) and detailed data about website audiences and purposes could overload its context window and produce inferior results.

I therefore decided to begin with a lightweight prompt ‘Can you help me do a content audit of this website? (with a link to the website)’ to give a small model something manageable and learn how it approached handling the query. I chose a website that the UX team had recently worked on, so I was familiar with its content, and I entered the prompt, starting with the Llama 3.3 model – an open weights model (costed on computing power rather than per-token usage) which is hosted in University data centres (instead of being cloud-based). I toggled on the option to include web search so ELM was able to access the website.

The same prompt to different ELM models produced varied results

Having starting with Llama 3.3, I moved on to other models: Open AI’s GPT 5 and then Anthropic’s Sonnet 4.6. To assess and critique responses from each, I adopted the mindset of a person reluctant to begin a content audit and looking for AI help to kick-start action on this, and used this as a roleplay to review and compare the responses.

The response from Llama 3.3. was too generic to convince me to start auditing

Despite a glitch in formatting the response, Llama 3.3. provided some general information about content auditing. Its response included an initial assessment of the site, which made objective judgements of the homepage (assessing it as ‘well-structured, with a clear introduction’), the navigation menu (labelling it ‘easy to use with links to key sections’), content quality (noting it as ‘high-quality, informative and engaging’) and accessibility (pulling out that the site had an accessibility statement).

The response then acknowledged that in order to do a thorough content audit on the site in question there was a need to provide guidance on the aspects to focus on, and rounding off, the response noted some of the aspects a typical content audit would examine: content quantity, content quality, accessibility and consistency across different parts of a site.

Reviewing this response, I felt that if I was a site owner looking to improve my site, this response could very easily persuade me that I didn’t need to bother auditing it at all – and I wasn’t left any wiser about how to meaningfully provide guidance on the aspects to focus on if I did decide to proceed with an audit.

Likelihood to convince me to audit: 3/10

Open AI’s GPT 5’s response was detailed and thorough but a bit overwhelming

As would be expected from a reasoning model, the response when I used GPT 5 was more detailed than Llama 3.3’s. Arranged in two sections, the first part included preliminary observations using the site URL provided – which were split into the following categories:

• Information architecture and navigation – commending the site’s positioning on user intents, yet suggesting improvements with more explicit audience pathways and call-to-action signposting
• Content coverage and freshness – using the site’s range of content types to assess content structure and assessing freshness based on recency of news items
• Call to actions – acknowledging that the site had were multiple calls-to-action which could be combined into primary CTAs for each audience
• Accessibility and usability – calling out the need for more meaningful and less duplicated alt text on the site images
• Trust and governance signals – recognising the role of the site’s strong branding to demonstrate authenticity
• SEO – suggesting the site’s page titles and meta descriptions were reviewed for relevance

The second part of the response outlined a plan for a full audit. The plan began by posing three questions to be answered:

1. What are the primary goals? e.g. increase visitors, boost traffic, showcase outputs
2. Who are the priority audiences? (with suggestions of the different groups)
3. What’s in scope? (suggesting either the entire site or specific sections)

The response then provided a suggested series of 7 steps to follow to complete the audit:

1. Inventory and crawl (with detail of the inventory to start with and the tools to complete the crawl)
2. Editorial quality review (with detail of criteria to score each page – such as inclusivity, accuracy, audience-fit etc)
3. UX and IA review (with suggestions to evaluate navigation labels and connections between content and pathways to achieve top tasks)
4. Accessibility (with suggestions of accessibility checking tools to use as well as manual checks to complete)
5. SEO and technical (with suggestions to check data points like metatags, internal links, robots files and performance)
6. Analytics (with suggestions to use analytic data such as bounce rates, page views, site searches etc)
7. Recommendations and roadmap (detailing prioritisation measures to make the identified site changes)

It rounded off suggesting a template structure for the content inventory (and offering to create this template as a Google Sheet), an idea for a scoring rubric, and reiterating different tools to use (naming products like Screaming Frog, Google Analytics and SEMrush). To conclude, it offered to do the audit, requesting answers to the three initial questions and asking for access to a sitemap, external tools and governance documents (such as style guide and brand voice guidelines).

Reviewing the response, which was presented as text content, I found it a bit too much to scan and digest, and putting myself in the place of someone facing content auditing with some reluctance, it felt that this large amount of detail could be off-putting to even start the process. I could appreciate the relevance of suggesting of external tools, but knowing that each of those would require setting up accounts and logins, as well as going through a process to integrate with Open AI, it had the effect of making me mentally push content auditing to the bottom of a to-do list.

Likelihood to convince me to audit: 6/10

Anthropic’s Sonnet 4.6’s response gave structured guidance which was easier to follow

The output from the Anthropic model stood out compared to the others from ELM since it was not just plain text, instead, it had been structured with headings, tables and icons. This made scanning and digesting this response much easier than the others.

The response was structured into 10 sections, as follows:

1. Site overview – table summarising the site name, owner, primary purposes and date of confirmed content
2. Information architecture – list of the top-level navigation elements with an assessment of what worked well and issues identified in a bulleted list
3. Content inventory – table of pages, with noted audiences, details of last update (if known) and inferred status (on a red, green and amber scale)
4. Calls to action – list of CTAs on the homepage, with assessments and recommendations
5. Content freshness – table containing list of most recent items in each section and related assessment
6. Accessibility – list of accessible features that were present, table of items that needed checking and recommended accessibility tools to make the assessment
7. SEO and technical – table containing an SEO assessment, considering elements like meta descriptions, structured data, mobile responsiveness – each with an allocated status, associated notes and combined recommendations
8. Content quality and tone – list of observations and recommendations
9. Priority recommendations summary – table of priority actions with an effort/impact score
10. To complete the full audit I need – list of requirements to complete the full audit (including sitemap, CMS page list, Google Analytics data, etc.)

Content within each of these sections was comparable to that included in the GPT 5 response, but in the Sonnet 4.6 response, these sections referred directly to information from the site in question – for example, instead of making general assessments of site aspects like navigation and content coverage, it contained direct detail about the site itself with mention of specific pieces of content, noting what was already known (for example, referring to issues noted in the accessibility statement). Arranging this in tables with use of icons and red, green and amber priority indicators made it easy for me to pinpoint what to address and with what urgency, and having this consolidated in the final table was easier to parse than reading it in text format (as in the GPT 5’s response).

Reviewing this response from Sonnet 4.6, it was the best of the bunch from ELM, it contained enough detail to help me get started but not so much that it was overwhelming. Putting myself in the position of a time-poor site owner unsure where to start, I had clear pointers of the priority areas and a mix of quick wins and bigger tasks to choose from.

Likelihood to convince me to audit: 7/10

As well as ELM, I experimented with Chat GPT and Claude to provide content audit help

Models within ELM can also be accessed through interfaces provided by AI products. Two of the best-known AI products are Chat GPT (by Open AI) and Claude (by Anthropic). I was curious to see the responses each of these products would provide in response to my query asking for content auditing help so I prompted each of them in the same way. In both cases I used the free versions.

Chat GPT gave a suggested approach and produced initial findings

The response from Chat GPT was comparable to the output from ELM choosing an Open AI model, but the response was structured and annotated with tables and icons, in a style similar to the Anthropic response. It began with a suggested audit approach which set out different areas to consider and related questions. In the next section (headed ‘Initial observations’) it contained an analysis of the site itself, which included site strengths and highlighted the following six areas for improvement on the site in question:

1. Homepage is quite academic – pulling out some audience questions that would be relevant to be answered on the homepage instead of the content that was there
2. Navigation could be more task-focused – noting that the current navigation was driven by organisational structure rather than tasks users would want to complete
3. News archive – picking out the need for recent articles
4. Calls to action – acknowledging that many of these are worded to provide information not prompt an active response
5. Content consistency – advising a uniform page structure for easier reading
6. Audience segmentation – recommending clearer delineation between groups of site users

The response concluded with an example audit table and some content recommendations, organised under ‘Keep’, ‘Improve’ and ‘Review’.

Reviewing this response as a whole, I liked the up-front suggestion of an auditing approach and the example audit table which was then supplemented with detail about the site itself and recommendations relevant to different aspects of the site in question. This helped me visualise what the audit outputs could look like, and I could see ways to get there.

Likelihood to convince me to audit: 7/10

Claude presented interactives to actively take me through an audit process

Anthropic’s Claude interface took a different approach to the other AI products, immediately asking about the main goal of the audit with options to pick. I picked ‘full inventory’. It then asked the desired breadth of the audit with another set of options, from which I picked ‘full site crawl’. The next screen asked me about the output format, and I picked ‘spreadsheet’.

After approximately 2-3 minutes, with a running commentary of what was being done, Claude presented a list of ‘headline findings’ of bugs, orphaned content, out-of-date content and broken content (dead links, incomplete calls-to-actions etc). It also produced a downloadable Google Sheet with two tabs – the first containing a summary of the issues to be fixed, their locations and a reason why they needed to be fixed, the second with a full inventory, with one row for every URL in the site and columns logging the page title, content type, date, status (with colour-coded options: ‘Keep’ (green), ‘Update’ (amber), ‘Rewrite’ (dark amber), ‘Review’ (blue) and ‘Critical’ (red)) and recommended actions for each page.

 

Reviewing this response, it was by far the most effective at making me feel I was starting the content auditing process and supporting me through it. Being presented with the opportunity to answer questions upfront meant I could control how the audit went, and having a spreadsheet created for me to review saved a lot of effort making this from scratch by scraping the site for a sitemap and page metadata.

Likelihood to convince me to audit: 9/10

Conclusion: If you’re putting off auditing your website, try getting AI to help

If you’re looking to improve your website, doing a content audit is one of the best steps you can take – working out what you have, what’s working and what’s not will put you in a strong place to move forward and make positive changes. Depending on how well you know your site, and how much time and resource you have to spend on it, you may appreciate some help to get started auditing its content.

From my quick experimentation with the different AI tools available I learned that, with the exception of the small non-reasoning model, each AI product offered something to help initiate the content auditing process of a website. At a basic level, asking AI to look at your website will pull out something you may not find yourself such as an out-of-date page, an irrelevant CTA or an accessibility glitch. AI can also take a zoomed-out look at your site that you may be too close to adopt yourself – such as reviewing your navigation pathways, appraising your audience segmentation or analysing the consistency of your content voice and tone.

If you’ve never audited a website before, AI can offer a lot of guidance to get you started, and whether you choose to do it yourself taking recommendations from the AI on tools and/or processes or whether you prefer to hand the heavy-lifting to AI to get your audit spreadsheet started – leaving you to deal with the more nuanced decisions that require specialist knowledge and experience, these tools can save you time and effort and may just be the nudge you need to do this all-important website maintenance task.

 

I previously introduced QuillMark, a tool I built to help web publishers check content against the University’s editorial style guide. In this post, I explain the technical design and methodology behind the prototype.

Introduction

My name is Shlok, and this summer I am working as an AI and UX Innovation Intern in the Information Services Group (ISG). During my internship, I have been developing QuillMark, a prototype tool designed to help web publishers check their content against the style guide. This post outlines the system design, methodology and AI pipeline behind the prototype, including how deterministic checks, language models and a MapReduce approach work together to improve reliability while keeping publishers in control.

 

QuillMark highlights violations in your existing text rather than generating an entirely new version. This:

1. avoids the risk of AI making unnecessary changes to unrelated parts of the page, which would require a manual audit each time
2. allows publishers to clearly see each violation and accept or reject suggested changes
3. reduces output tokens, lowering both hallucination risk and cost

 

Problem: The style guide contains a large number of rules, around 60–70. If all rules are sent to the LLM at once, the model may not check the text against every rule reliably.

 

Research: https://openreview.net/pdf?id=R6q67CDBCH

 

Solution → MapReduce (Divide and Conquer: Map → Collapse → Reduce)

 

System Design and Methodology

 

Pipeline:

1. Deterministic checks to flag blatant violations
2. Fine-tuned small model flags likely obvious violations
3. Large model audits the final page deeply for complex deeper violations

 

The deterministic checks are used to flag “obvious” violations that are directly detectable using regex or code. For example, this could include the use of a forbidden word, or a spelling convention such as using “benefited” instead of “benefitted”. These issues can be detected using a direct search of the page and do not require AI.

 

The second stage uses a smaller fine-tuned model. This model would be fine-tuned on the Style Guide rules and training examples. It would again be used to detect relatively “obvious” violations, but ones that are not simple enough to be detected reliably through deterministic checks. These issues are not highly complex, so they should still be visible to a smaller model.

 

After the publisher fixes the deterministic violations and the violations found by the fine-tuned small model, there are likely to be remaining deeper and more complex issues that both previous approaches did not pick up. The final large model can then focus mainly on these more complex issues. This means it spends less attention on obvious violations and more attention on issues that are easier to miss.

 

This improves the process in two ways.

 

First, it allows the final large model to focus on deeper issues. If the whole page was passed directly to the large model from the beginning, it would likely flag many obvious violations first and might miss some subtler issues. By fixing the most obvious issues earlier, the large model can focus more on smaller, judgement-based, or easily missed problems.

 

Second, it may reduce cost compared with running the large model multiple times. One alternative would be to run the large model once, fix the obvious issues + some deeper ones it finds, and then run the large model again to find the remaining deeper issues. In the first run, the large model may spend much of its output on obvious violations. In the second run, after those obvious issues are fixed, it may be able to look more deeply. The proposed approach tries to achieve a similar effect more cheaply by using deterministic checks and a smaller fine-tuned model before the final large-model review.

 

This pipeline is shown in Figure 1.

 

 

Using MapReduce to reduce instruction-following degradation

Based on personal experience with large language models, as well as existing research such as the Curse of Instructions, we cannot rely on a large language model to follow a very large number of rules at once. In this case, the style guide contains around 60–70 rules. If all of these rules are passed to the model in a single request, the model may not check the page against every rule properly. It may follow some rules, ignore others, or miss violations because there are too many instructions to apply at the same time.

 

To solve this, I propose using a MapReduce approach, which is a divide-and-conquer technique: Map → Collapse → Reduce.

 

The rules would be broken into smaller chunks, with each chunk containing a fixed number of rules. Each chunk would then be sent as a separate request, asking the model to check the page only against that smaller set of rules. This allows the model to focus on fewer rules at a time and check them with more attention.

 

After all the chunks have been processed, the responses would be gathered together and combined into one report. This is the collapse stage. The combined findings would then be passed through another large language model request in the reduce stage. This final request would clean up the output by removing duplicate findings, resolving overlapping issues between chunks, and filtering out possible false positives or hallucinated violations.

 

This approach makes the checking process more reliable because it avoids asking the model to apply all 60–70 rules at once. Instead, each group of rules is checked more carefully, and the final report is produced by combining and cleaning the results. This helps ensure that all rules are checked with more equal rigor, rather than relying on the model to remember and apply every rule in one large prompt.

 

The MapReduce approach is shown in Figure 2.

 

 

Update: Large-Model Cleanup from the Reduce stage was excluded from the prototype as the probability of duplicates occurring is low and the cost of such a cleanup is relatively high with minimal benefit.

 

Read more about the technical system plan: https://blogs.ed.ac.uk/website-communications/building-quillmark-testing-a-drupal-style-guide-assistant-with-web-publishers/

Watch a video demo: https://media.ed.ac.uk/media/t/1_yozad8ie

 

Future enhancements:

• Strengthen Deterministic Regex-matching with more training data (web pages)
• Audit deterministic findings using a cheap, local AI

I introduce QuillMark, a Drupal prototype designed to help web publishers check content against the University’s editorial style guide. I reflect on how I developed and tested the tool with publishers, focusing on what their feedback revealed about usability and how it will shape the next version.

Introduction

My name is Shlok, and this summer I am working as an AI and UX Innovation Intern in the Information Services Group (ISG). During my internship, I have been developing QuillMark, a prototype tool designed to help web publishers check their content against the style guide. This blog post explains the problem I was trying to solve, how I built and tested the prototype, what I learned from publishers and what I plan to do next.

 

The problem

Our style guide contains more than 60 rules covering areas such as spelling, punctuation, tone, formatting and terminology. Previous research showed that web publishers found it difficult to remember and apply every rule while writing and editing content.

 

This is understandable. Publishers are often working under time pressure, and checking a page manually against dozens of rules adds a significant cognitive burden. Even experienced publishers can miss small details, particularly when they are concentrating on the meaning and accuracy of the content.

 

I began exploring whether a tool could make this process easier. The aim was not to replace publishers’ judgement or rewrite their content automatically. Instead, the tool would identify possible style-guide violations, explain the relevant rule and allow the publisher to decide whether to apply or dismiss each suggestion.

 

This became QuillMark.

 

Read more about the Style Guide

 

Why I built the prototype in Drupal

Drupal is where publishers already create and edit web content, so it made sense to build the prototype directly into that environment rather than create a separate tool.

Before starting development, I attended Drupal in a Day to understand the platform, its content-editing interface and how a custom tool could fit into an existing publishing workflow.

 

Building QuillMark within Drupal meant publishers could check their content without copying it into another system. It also allowed me to test the tool in a realistic environment, using the same types of fields, buttons and interactions that publishers encounter in their day-to-day work.

 

Read more about Drupal in a Day

 

Planning the checking process

Before writing the prototype, I planned the system in detail.

 

One of the main design decisions was that QuillMark should highlight individual issues in the existing content instead of generating a completely rewritten version. Rewriting an entire page could introduce unnecessary changes and would require the publisher to audit every sentence. Showing individual findings makes it clearer what the tool has identified and keeps the publisher in control. This also prioritises the HITL (Human-in-the-Loop) framework, which I intend to use in my AI-based projects to ensure that people remain involved in reviewing decisions made by AI.

 

The system design proposed three types of checking:

1. Deterministic checks for clear violations that can be identified using code or regular expressions, such as prohibited words or spelling conventions.
2. A smaller AI model for relatively straightforward issues that require more context than a simple text search.
3. A larger language model for more complex, judgement-based rules involving areas such as tone, structure or plain language.

 

The plan also used a MapReduce-style approach. Rather than asking a language model to apply all 60–70 style-guide rules in one prompt, the rules would be divided into smaller groups. Each prompt could then concentrate on a limited number of rules before the results were combined. This was intended to reduce the risk of the model overlooking instructions because it had been given too many at once.

 

The original design included an additional AI step to remove duplicate findings and resolve overlaps. I left this out of the prototype because duplicates were expected to be relatively uncommon, while the extra model request would increase cost and complexity.

 

Read more about the technical system plan: https://blogs.ed.ac.uk/website-communications/quillmark-the-technical-details-behind-my-drupal-style-guide-assistant/

Coding the prototype

I developed the prototype iteratively, beginning with a small number of style-guide rules and expanding the checks once the basic workflow was working. For development, I used Codex. I gave it my plan and asked it to design the architecture. After reviewing it, I asked it to proceed with the implementation.

 

The first stage used deterministic checks for issues that could be found reliably without AI. These checks searched the content for recognisable patterns and returned the location of the issue, the relevant style-guide rule and, where appropriate, a suggested correction.

 

The second stage used a large language model to identify issues that depended more heavily on context. I divided the rules into smaller prompt groups so that the model could focus on a manageable set of instructions during each request.

 

The prompts asked the model to return structured findings rather than a rewritten page. Each finding needed to include enough information for QuillMark to:

• identify the relevant content;
• explain the problem;
• show the related style-guide rule; and
• suggest a possible correction.

 

Publishers still had to approve or dismiss each finding. This human-in-the-loop approach was intentional. Style rules can depend on context, and an automated suggestion will not always be appropriate. QuillMark was designed as a decision-support tool, not an automatic editor.

 

Watch a video demo: https://media.ed.ac.uk/media/t/1_yozad8ie

 

Designing the usability test

Once the first iteration was complete, I adapted an existing usability-testing template to create a test for QuillMark.

 

I conducted three sessions with web publishers. Participants were asked to work through a series of tasks covering the main parts of the prototype, including running the checks, understanding the two stages, locating an issue in the editor, reviewing a rule and applying or reverting a suggested fix.

 

I observed how participants used the interface, where they hesitated and whether the information on screen matched their expectations. I also asked how they might use the tool as part of their normal publishing process.

 

The purpose was not only to find technical bugs. I wanted to understand whether the concept itself was useful and whether the interface communicated how the tool was intended to work.

 

What publishers told me

The publishers thought the tool would be useful as a final check before publishing. They said they would still use their own judgement and would not automatically accept every suggestion.

 

Most of the feedback was about the usability of the tool. Some parts were unclear, such as the difference between the two stages and some of the button labels. There was also too much information on the screen, and some issues were repeated. The publishers also wanted to see what a suggested fix would change before applying it.

 

What I learned

The testing suggested that the core idea is useful, but the user experience needs to become simpler.

 

Publishers do not necessarily need to understand which checks use regular expressions and which use an AI model. They need to know what issue has been found, why it matters, what the proposed change is and what action they can take.

 

The sessions also reinforced the importance of designing for scanning. More explanation does not always create more clarity. In a publishing workflow, concise labels, clear states and well-grouped findings may be more valuable than displaying every piece of supporting information at once.

 

Next steps

My next step is to refactor and review the prototype code before making changes based on the usability feedback. This will include reviewing the AI-generated code to check whether each section is needed, whether the same result could be achieved more simply, and whether any parts repeat code that already exists. I will compare the generated code with the rest of the codebase so that it follows the same structure and does not add extra functions or files without a clear reason.

 

Where the code is longer than needed, I will simplify it by removing repeated checks, combining similar sections, and reusing existing functions. I will also keep individual files to a manageable size, generally no more than 1,000 to 3,000 lines depending on the file. Larger files will be split where this makes the code easier to follow, and hardcoded rules will be moved into the database where possible.

 

The next iteration will focus on:

• clarifying or simplifying the two-stage workflow;
• reducing repeated and overwhelming findings;
• improving button labels, status messages and colour distinctions;
• previewing changes before they are applied; and
• making the interface more concise and easier to scan.

 

In March, I started my internship as a Digital Developer within the Website and Communications team. My internship focuses on exploring Figma, the industry-leading digital design tool, and the user interface (UI) component library that the University has been developing within it.  

First impressions as an intern 

On my first day – somewhere between onboarding, learning a whole new set of acronyms, and unexpectedly getting free pizza – I began to get a sense of what the next few weeks might look like. I also met with colleagues I’d be working with closely on the project: Sonia Virdi (Human Centred Specialist in Web Strategy and Governance) and Mel Batcharj (Content Designer in UX and Digital Consultancy).

Looking back, that first week was a bit of a whirlwind. From getting to grips with the University’s structure, to navigating room bookings in the Edinburgh Futures Institute, to settling into the Forrest Hill office (and quickly becoming a fan of the huge monitors), it took some time to find my rhythm. 

Attending my first stand-up and adjusting to a new way of working was all part of that process. At the same time, I began exploration and research, which quickly became central to my day-to-day work. What stood out most was how quickly the experience shifted from unfamiliar to genuinely engaging, especially as I explored the problem space in more depth. 

The platforms and tools behind this project 

Several platforms and tools underpin this work, helping us design, document and maintain digital components while ensuring consistency between design and development. 

EdGEL (the University’s coded component pattern library)

EdGEL is the University’s coded component pattern library. For those unfamiliar, a pattern library contains all the digital assets that are required when building and creating digital products and services. EdGEL’s underlying design framework is Bootstrap, which provides the starting point when developing coded components.

Bootstrap

EdGEL is widely used across websites and web applications. It acts as a central source of truth for how interfaces should be branded, structured and implemented at the University. It was developed in 2015 when the first Drupal installation of the University’s central web platform, EdWeb, was launched. 

EdGEL website 

Documenting EdGEL components (Pattern Lab and Storybook)

The assets contained within EdGEL are currently documented within Pattern Lab, which is being replaced by the tool Storybook. Both tools provide a practical way for developers to explore the available EdGEL components and understand how and when they should be used.

Storybook implementation of EdGEL pattern library 

Figma Design 

Figma Design is an industry-standard tool used to create user interface designs (UI), prototypes and support teams through its collaborative features. It’s widely adopted by organisations (from startups to large enterprises ) because it brings design and development workflows closer together, reducing inefficiencies in how digital products are created and iterated on. 

More recently, Figma has introduced AI-powered features for prototyping and layout generation, making it an exciting tool in the current digital software landscape. 

What is Figma? (Figma article)

Building a University UI component library in Figma

In 2021, as part of the University of Edinburgh’s design system project, Figma was adopted to develop a library of digital UI components.  

Blog on the University’s design system project 

The goal was to make the University’s branded EdGEL components more accessible to a wider range of colleagues, align the two libraries and streamline digital development workflows. Additionally, the project’s aim was to simplify the process of making design changes and improvements to the EdGEL components, ensuring they are fit for purpose across a broad range of University environments. The work to create the Figma UI component resource would support a variety of roles involved in designing and developing digital platforms. 

 

 

Why Figma was chosen 

Figma was selected for several reasons: 

• Its functionality outweighed any other digital tool in respect to component development. 
• It helped bring design and development practices closer together, creating a more efficient workflow. 
• It enabled component and resource sharing across the University, reducing duplication of effort. 
• It supported more consistent use of the University’s branded components across digital products and services. 
• The strong user community provided a rich resource for learning best practices and obtaining support. 

My internship has two key aims

The two aims of my internship both focus on Figma, however, they approach the challenge from different angles. The first is about understanding how staff across the University currently design and deliver digital work, and where Figma fits within that landscape. The second is about improving the University’s existing Figma component library to better support its users. 

Understanding how Figma fits in at the University

The primary aim of my internship is to learn more about how Figma supports digital development processes across the University. This involves assessing Figma alongside the other tools teams use every day and understanding where it could add value.

We focused on understanding how people work

As a second-year computer science student, I was keen to move away from a purely engineering perspective and instead immerse myself in the user side – exploring not just Figma as a tool, but the people using it.  

Understanding how staff design, collaborate, and deliver digital work is central to this. Importantly, we’re not just interested in Figma itself, but in the wider workflows and tools that people rely on.

To guide this work, we focused on a few key questions: 

• What tools are people using day-to-day? 
• How do these tools support their work? 
• What works well and what does not? 
• How does or could Figma improve their workflow process? 

A survey helped us reach staff across the University

To help us understand how people work, we created a survey to reach a large proportion of internal staff across a range of roles. These included: 

• marketing and communications staff producing high volumes of digital content 
• visual designers working on platforms and services 
• developers building and maintaining applications and systems 
• user experience (UX) professionals carrying out human-centred research and design 
• academic staff designing and prototyping their own tools 
• others (such as project managers working with product teams) 

A key decision we made from the start of my work was not to focus solely on “designers.” Through discussions, we recognised that design isn’t confined to traditional visual design roles. People across the University make design decisions about layout, content, structure, and user experience, often using whatever tools are easiest to access within the University. 

Working with This Is Milk to enhance and improve the existing Figma UI library

Our work is being supported by the design agency This Is Milk, who are helping us make better use of Figma’s newer features while improving the University’s UI component library.

This is Milk 

This will involve: 

• making the library easier to access and integrates into users’ workflows
• improving synchronisation between the EdGEL codebase and the Figma UI library to reduce maintenance effort 
• developing training resources to help colleagues get started with Figma 

What I’ve learned so far

Developing my own understanding of design systems and Figma

Alongside our research, I’ve been developing my understanding of both design systems and Figma. 

The internship has given me the opportunity to build on concepts I was already familiar with, like components and variant properties, while exploring newer ideas, including design tokens and more scalable system structures.

Adopting a user-first mindset

So far, the experience has shown me that designing digital systems isn’t just about creating components or choosing the “best” tools. It’s about understanding people, workflows, and the small details that make something either frictionless or frustrating to use.

Importantly, the internship has pushed me to think more from a user-first perspective, grounding decisions in usability, accessibility, and real user needs rather than focusing only on the tehcnical or visual aspects of a solution.

Looking ahead

As my internship continues, I’m particularly looking forward to getting more hands-on with improving the design system and component library, as well as continuing to engage with staff to better understand how these tools can support their work. 

Myself, Sonia and Mel will continue to blog about the project as it progresses. Our next post will explore the survey research in more detail, including how we carried it out, who took part and our initial findings.

UX Scotland is an annual two-day conference for people working in user-centred design. This year, I went along to the John McIntyre Centre to hear about the latest developments in UX. In this post, I’ll write about my highlights from the conference.

Object-oriented UX in action: rebuilding a public sector website with structured content

The challenge: dividing up content and naming groupings

Out of all the talks I saw, this was the one that I found the most interesting. Joey Gartin, a content designer at Renfrewshire Council, told a story about a multiyear project to redesign the council’s website. It started with a familiar situation: the council had a large website that people found confusing and hard to use. Users couldn’t find things, and when they could, it wasn’t always easy to understand.

Renfrewshire Council needed to work out a better way to divide up and group the content on their website. Then they needed to name those groups in a way that made sense to people.

I enjoyed hearing about this because this is one of those problems that never seems to go away. When we work with digital content, whether that’s designing a homepage or tidying up a shared set of folders and files, we’re constantly looking for ways to group things. Then we’re also constantly looking for names for those groups that will make sense to people.

OOUX involves favouring nouns over verbs

Renfrewshire Council approached the problem using methods from Object-Oriented User Experience (OOUX). This is an approach to digital product design that focuses on establishing the things (or ‘objects’) that matter to your users. The approach emphasises the importance of working out the nouns involved in your service before you move on to the verbs. I’d read a bit about it a while ago on Duncan Stephen’s blog:

Duncan Stephen writes about how the Scottish Government have used OOUX approaches

The argument in favour of focusing on nouns is that this approach is more closely aligned to how people think. When you walk around a supermarket, you have a shopping list of objects. To reflect this, a supermarket is typically organised into sections that reflect broader groupings of those objects: Fruit, Vegetables, Pasta, Cake, and so on.

In a similar way – the theory goes – people often approach websites with a list of nouns in mind. They are therefore scanning webpages for keywords that match these nouns.

But many council websites use a hybrid of verb-based groupings and noun-based groupings. So you get sections called Pay, Apply, Report and Request forming a core part of their information architecture. Here are two examples.

The City of Edinburgh Council:

West Lothian Council:

 

Renfrewshire have not adopted this model. While their page titles often start with verbs, the groupings for these pages overwhelmingly use nouns:

Renfrewshire Council

Joey cited the City of Sydney as another public body using this approach:

City of Sydney

OOUX helped Renfrewshire Council to create templates

With your objects figured out, the next step of an OOUX approach is to work out:

• the relationships between objects
• the calls to action that objects offer users
• the attributes that make up objects

Joey talked us through this work. He also talked about how Renfrewshire Council used this as a foundation to create templated designs for common page types.

For example, take these two pages, both categorised as ‘service requests’:

• Report a housing repair (Renfrewshire Council)
• Graffiti (Renfrewshire Council)

Because they’re both service requests, you can see common sections on both pages.

For example, on the ‘Report a housing repair’ page, you have sections called:

• Before you report a repair
• How to report a repair

And on the ‘Graffiti’ page, you have:

• Before you report it
• How to report it

Joey showed us how this works behind the scenes. Renfrewshire Council use custom content types in Drupal to help structure the content writing process. So if you’re a content editor writing a service request page, you’ll be prompted to add a section on ‘Before you report it’. This helps content writers know what they need to include in a page. It also brings consistency to the website, which ultimately benefits users.

My reflections on how templates could work at Edinburgh

At Edinburgh, we use content types in our Drupal-based CMS EdWeb to create News and Event pages. So it was interesting to see another public sector website using a more extensive set of content types in Drupal. It made me reflect on how this approach might work for us. The context is so different: at Renfrewshire, it sounded like they had a more centralised approach to content management. The bulk of Edinburgh’s content publishing model is more devolved, which makes templated approaches to content more challenging. But there are clearly benefits available when you can make it work.

So lots to chew on from this talk, and it was a fun presentation to boot.

Other highlights

Sara Wachter-Boettcher on burnout

Sara Wachter-Boettcher’s keynote, “You don’t need more grit: breaking the burnout cycle in UX” was a run through of how and why burnout affects designers working in tech. It was a useful reminder to appreciate the limits of our influence within an organisation, and to be careful not to attach too much of ourselves to our job. It was cool seeing Sara in person. Her book Content Everywhere was one of the first things I read when I started in my role, and I thought it was really good.

Sara Wachter-Boettcher

Craig Abbott on AI and accessibility

Craig Abbott spoke about the risks and opportunities of using large language models for website design and fixing accessibility issues. One point that stood out was that LLMs are trained on some pretty ropey data. WebAIM estimate that 95% of the top million websites have detectable WCAG 2.2 failures, and these are the kinds of site that LLMs are trained on. So if you ask an AI to create a website, it will often produce something that superficially looks ok but is packed with accessibility problems.

The WebAIM Million: The 2026 report on the accessibility of the top 1,000,000 home pages

Craig showed us an example of a website he’d quickly created with AI and talked through the accessibility problems. He also showed us his other experiments. He’d had inconsistent results using AI to detect heading level failures or write alt text that took the context of an image into account. But he’d had some successes with using AI to complete more technical tasks with testable results.

Craig Abbott

James Chudley on digital sustainability

James Chudley presented on bringing sustainability practices into digital design. The highlight for me was seeing his experiments with visualising page weight across a website. Taking inspiration from an infographic using colour bars to illustrate rising global temperatures, James had applied a similar idea to visualising where a website is using more energy-intensive design choices.

James has posted his slides here:

James Chudley: Presenting ‘Beyond human centred design’ at UX Scotland

Another great conference

UX Scotland was a good chance to hear from some leading lights in the field, catch up with people and hear about some case studies that are relevant to us at Edinburgh. The two days were well organised and it gave me a lot to think about as we continue to support UX and content work at the University.

UX Scotland

I was pleased to have a talk accepted at the annual UCISA Women in Tech (WiT) conference. I went to Newcastle for a day of talks, workshops and networking. I left with a better understanding of the WiT community , and a reinforced appreciation of the need for inclusivity in Higher Education.    

UCISA is an industry body supporting digital professionals working in the education sector. I have been actively involved in UCISA since 2022 when I helped set up the UCISA UX Group and became co-chair of this UK-wide community of practice. I’ve organised many UCISA UX events but had not attended one of UCISA’s flagship annual conferences, the Women in Tech event, until this year. My presentation about our project on staff profiles resonated with attendees, I learned a lot about diversity and representation in the sector from attending the other talks and I enjoyed connecting with other Higher Education professionals over shared inclusivity challenges. Here, I reflect on my highlights from an interesting day.

Sharing our staff profiles work piqued interest from other universities

As well as the clear focus on inclusivity, one of the themes of this year’s WiT was real-world applications and problem-solving. I felt our staff profiles project spoke to this theme, so I submitted a talk to share what we learned from research and the steps we have taken towards finding a new profiles solution.

My session was well received – perhaps unsurprisingly, colleagues from other institutions shared the same concerns and challenges in designing a profile solution that showcases staff in the best light, keeps them findable in searches, and yet remains easy for staff to update. I valued the chance to make new contacts, exchange ideas, and learn about other institutions’ diverse approaches to the ‘profiles problem’. I resolved to stay in touch as we take steps towards implementing a profiles solution at the University, recognising just how universal this need is across the Higher Education sector, and seeing an opportunity for meaningful collaboration.

Read more about the staff profiles project in the blog post series:

Collected blog posts about staff profiles project

Results of a 2025 WiT survey revealed risks and opportunities

One of the core activities of the UCISA WiT committee is to regularly collect data to understand diversity within IT departments across the FE/HE sector. At the conference, WiT committee co-chairs Christi Hopkinson and Katie Wilde shared a preview of the results of the 2025 survey, completed by more than 200 respondents from 66 institutions, with 74.5% of the respondents identifying as female. Several findings stood out from the preliminary report in terms of risks and opportunities.

There’s a risk of retention due to barriers to progression

Responses to a question about progression routes revealed significant proportions of respondents had started in IT in the following areas:

• First/Second Line Support
• Application Support
• Business Analysis
• Project Management
• Web Development

Responses to a follow-up question about the roles respondents were currently working in showed continued representation in these areas, which indicated a degree of stasis when it came to progression in the sector. Areas where women were underrepresented included:

• Infrastructure
• Security
• Enterprise Architecture
• Senior Management.

The survey results showed a third of respondents had considered leaving their institutions or the sector, citing pay, workload and progression as popular reasons motivating them to think about moving on. Common blockers to progression included:

• Lack of available higher-grade roles
• Promotion structures tied only to management, not technical excellence
• Career pathways not transparent
• Women having to ‘prove more’ to progress

There’s potential to be gained by investing in non-technical skills

Most respondents cited the following skills as important for the roles they were currently in as well as for progression:

• Problem solving
• Communication
• Analytical skills
• Customer service
• Business analysis
• Strategic thinking

This spread of skills reinforced the value of focusing training and development on non-technical abilities to complement technical skills, and to ensure expertise was appropriately directed to deliver on broader institutional goals.

There’s room to improve on diversity, inclusion and discrimination

Responses to questions about inclusion and belonging revealed positives and negatives about the workplace respondents were part of.

On the positive side:

• 79% felt valued as part of a team
• 67% felt they were treated fairly and equitably

On the less-positive side:

• Only 34% felt the leadership reflected diversity in the workforce
• 41% felt there were opportunities for careers advancement
• 46% felt their organisation supported under-represented groups
• 47% were satisfied with their organisation’s diversity initiatives

The spread of these numbers provided a clear steer of areas to focus on to achieve less exclusive, and better-balanced IT workplace.

Achieving inclusivity starts with individuals and requires thinking beyond statistics

The survey data gave a snapshot of the current state of inclusivity in the sector, however, anecdotes from individual presenters painted a more vivid picture of what inclusivity could look like. The WiT programme included several women sharing stories of their induction into tech and reflecting on their individual progression routes. It was refreshing to see the diversity of pathways they had taken and interesting to hear about what they had learned along the way, and their tips for success.

Monica Jones, Chief Data Officer at the University of Leeds acknowledged that career progression paths rarely run smoothly, and advised setting personal goals and milestones to work towards, to be best-prepared for promotion opportunities when these came up. Julia Lloyd, College Manager – Business and Law, at the University of the West of England, emphasised the benefits of ‘quiet leadership’ and shared tips to create space for different voices – such as thoughtful structuring of meetings, design of communication channels and rewarding contributions over confidence. A joint talk ‘The Not-So IT Crowd’ from Catriona Blair, Joanna Addison and Sasha Titus from the University of Kent, and a session by Amber Mothersille from the University of Northampton emphasised the value of non-technical skills in technical roles – recognising the need for empathy, trust-building and adaptability to strengthen teams and build excellent digital services.

Breaking barriers requires breaking old biases and habits

A final takeaway from attending the WiT event was a call-to-action to question the way we work – specifically to foster inclusivity by making room for new ways of thinking and for fresh perspectives.

In an interactive exercise led by Katie Wilde, we considered five roles necessary for the operation of successful teams (the Navigator, the Connector, the Builder, the Challenger). In a period of honest reflection, we shared the roles we naturally adopted in team settings, and the roles we tended to overlook or disregard. Going through this exercise was a good leveller as well as a reminder to make room for diversity in everyday team settings instead of relying on familiarity.

The day closed with a thought-provoking talk on male fragility, delivered by Jake Dovey, a UCISA mentor. Drawing on personal anecdotes experienced through his involvement with UCISA, Jake’s talk described instances where men had overreacted defensively to being challenged and where women had inadvertently softened situations to avoid potential conflict and ‘keep the peace’. Jake challenged the women in the room to recognise these instances going forward, and prompted a call-to-action for all to recognise these harmful patterns and call them out to collectively help break the disruptive cycle.

Final thoughts – WiT26 was less about women and more about inclusivity

WiT26 delivered a packed programme which encouraged me to think outside my work in UX and more broadly about the joint responsibilities we all have in creating and fostering an inclusive work environment. I came away with recommendations of books to read and concepts to learn more about, and a heightened awareness of embedding inclusive practices in my day-to-day work and activities. I am keen to see the full results of the WiT 2025 survey and to remain part of the WiT community going forward.

 

Drupal In A Day (DIAD) is an in-person training event, designed as a beginner-friendly hands-on introduction to Drupal, the open-source content management system. When the University hosted DIAD, several of the UX team took the chance to attend.

As content management systems go, Drupal is one of the mainstays. It’s been around for 25 years and is particularly famed for its robustness, reliability and flexibility – so much so it’s trusted to power websites of enterprises, governments and higher education institutions around the world. It’s open-source so there’s no proprietary lock-in – the functionality, features and innovation available in Drupal comes from a thriving worldwide community of contributors – as individuals, agencies and organisations.

One of the best places to learn about what Drupal is, what it stands for and what it aims to achieve is the blog site of its founder, Dries Buytaert.

Dries Buytaert blog

In 2025, Hilmar Kári Hallbjörnsson taught the first Drupal In A Day on the final day of DrupalCon Vienna.  Hilmar had been teaching Drupal to students at the universities of Reykavik and Iceland for many years and felt strongly that teaching Drupal to new generations, to inform them of its capabilities, and to inspire them of its potential was something the Drupal community needed, as a way to nurture the longevity and sustain the future of Drupal. Through a tremendous effort, he made the first Drupal In A Day happen in Vienna in October 2025. It was very well-received, successfully establishing the Drupal In A Day format going forward.

Read more about the first Drupal In A Day in Hilmar’s blog post from 2025

Drupal in a Day: Vienna 

Hilmar travelled to Edinburgh to deliver Drupal In A Day with our own Web Development Team Manager, Gareth Alexander at the start of June 2026.  Nick (Senior Content Designer), Shlok (AI and UX Innovation Intern) and Hannah (Digital Content Style Guide Intern) from the UX team signed up for the event along with other LTW interns and staff from the wider University with an interest in learning about Drupal. Here, they reflect on their experiences of the day.

Nick’s reflections 

I attended this training to bring my understanding of Drupal up to date. The last time I set up a Drupal site was in the early 2010s, and a lot has changed since then. With Drupal providing the backbone to EdWeb 2, I wanted to get to grips with some of the terminology and concepts that underpin conversations within our team about what our central CMS can do. 

I was particularly interested to learn more about Drupal CMS, a new service that helps you set up a Drupal site more quickly and with less need for technical understanding. 

After getting the required software set up on my laptop (thanks Kirsten), I clicked along with Hilmar and Gareth as they walked us through the various sections of Drupal CMS. We worked through steps to create a new content type and we created different Views to display the same content. We tweaked image formats. We also learned how to attach tags to a piece of content and how these tags can act as a filter for overview pages. 

By the end of the day, I hadn’t suddenly become a Drupal expert. But I did have a better understanding of how EdWeb 2 works behind the scenes. In particular, I felt like I knew more about how EdWeb 2 takes the content you enter when you create a page and presents this in different ways elsewhere on a site. 

Alongside that, I would now feel more confident in setting up a new Drupal CMS site, and I now have a better understanding of what this system offers in comparison to other ways of operating a website. 

Finally, the day gave me a better sense of how the community aspect of Drupal is central to how this project keeps going. Hilmar and Gareth emphasised that there are various events in the calendar where people working with Drupal can meet up and collaborate. That’s a powerful message: that anyone is invited to learn more, become part of the community and contribute to how this system works. 

Shlok’s reflections

Before starting my internship, Drupal was a completely new concept to me. I did not really know what a content management system was, how Drupal worked, or why it was used by universities and other large organisations. Because of that, Drupal In A Day was a really useful introduction for me. 

I thought the format worked well because going through everything in one day helped keep a flow of information. It was quite intense and sometimes fast-paced, but that also meant we were able to cover a lot of concepts in a short amount of time. I would say I was able to follow around 70% of the session, which felt like a good start considering Drupal was completely new to me. 

One of the parts I found most useful was the introduction to Drupal itself. It helped me understand what Drupal is, what a CMS is, and why the University uses it. I learnt that Drupal is valued because it is stable, secure, flexible and open source, which makes it suitable for large and complex websites like those used by universities. This helped me understand why Drupal is still used by many major institutions. 

I also found it interesting to learn about the Drupal community and the different ways people can work with Drupal. Before the session, I assumed Drupal was mainly for developers. However, I learnt that people can build careers around Drupal in different ways. Some roles involve coding and development, while others focus more on design, content, user experience, training or project work. That helped me see Drupal as more than just a technical platform. 

One point that really stood out to me was when we were told that Drupal has a steep learning curve at the start. This was useful to hear because it made the difficulties I was facing feel expected. Since many people find Drupal challenging in the beginning, it was reassuring to know that not understanding everything straight away was normal. It made me feel more comfortable continuing to learn. 

During the practical parts of the day, I learnt about some of the basic Drupal features, such as creating and managing content pages. At first, this was quite confusing because the concepts were new to me. However, as the day went on, I started to become more comfortable with the terminology and the way Drupal is structured. 

Another part that I found very helpful was the follow-up assignment where we had to build something from scratch. After learning so much in one day, I think it was important to have a task that allowed us to apply what we had learnt. It helped me consolidate my knowledge, see which parts I had not fully understood during the session, and become more comfortable with Drupal before starting work on my internship prototypes. 

One thing I would have liked to learn more about was the command line and the use of terminal commands. We touched on some setup and development processes, but I think spending more time on what the commands do and how they fit into the Drupal workflow would have helped me. I would also have liked to learn more about the coding side of Drupal, especially how to build custom Drupal modules. This is particularly relevant to my internship because my work is focused on AI and innovation within Drupal. 

If I could suggest one change, it would be to make Drupal In A Day into a two-day or three-day format. The first day could stay mostly the same, giving everyone a broad introduction to Drupal. The second day could be a slower guided session where participants build something with support from the instructors, similar to the assignment we were given afterwards. A third optional day could focus more on coding and custom module development for those who want to explore the technical side further. 

Hannah’s reflections 

I signed up for the Drupal in a Day training with very little knowledge of the CMS beyond working with existing EdWeb 2 sites, such as when creating prototypes of Style Guide pages or writing blog posts. So, I was interested in increasing my knowledge of Drupal as well as gaining the ability to build a site. Before the training began, I was unsure how well I would follow the instructions as a beginner, but Hilmar, Gareth, and all of the members of the Drupal community who were present at the training were extremely helpful, informative, and patient when it came to giving assistance at points where I was confused or behind. 

Once I had overcome a technical error with my laptop and decided to use Drupal Forge, I was able to follow along with the instructions being provided and replicate the Artist biography pages that Hilmar and Gareth were demonstrating. The course was fast-paced and detailed, and while maintaining this pace was a challenge, it made the course engaging and the product of the day felt like a genuine accomplishment. 

With the knowledge that I gained throughout the training, I feel that I have a strong foundation that will allow me to continue to develop my skills in building a Drupal site further in the future. In particular, I think the example site that we were creating during the training was incredibly useful in that it allowed us to try out several different aspects of building a site with Drupal, such as different content types, tags, and images, as well as exploring some different design options as well. 

I was impressed to learn about how community centred Drupal is, and that contributions and developments to Drupal are made by users and members of the community. Gareth and Hilmar’s explanations of how this works really helped me to understand why Drupal is as adaptive and intuitive as it is (such as in its security and bug fixes), which is that these developments are a direct result of user experience. Furthermore, Gareth and Hilmar also emphasised the flexibility of Drupal, due to the ability to install ‘recipes’ that customise the functionality of sites that you are building. 

Overall, I think Drupal in a Day was an extremely useful and practical training session that has equipped me with the skills to build a basic site and further develop these skills whenever I have the chance.  

Emma’s reflections

When it comes to Drupal, I am largely self-taught – pretty much everything I know has been gleaned from reading, attending/watching sessions from Drupal events, and (predominately) pestering people in-the-know with my questions. When Drupal In A Day came to the University, I pondered whether I should attend. I’ve contributed to the community since 2022. I’m on the Drupal leadership team. Surely I should know Drupal by now? I took a split second to reflect and realise that you never fully know Drupal. There’s always something new to learn, something to challenge what you thought you understood, or something to clarify an area you weren’t quite sure about. Conscious of not taking a space away from a complete Drupal beginner, I opted to sit in on the event, to follow along, while working on other things.

The day combined practical exercises with general Drupal knowledge. Hilmar and Gareth began with the basics, covering key parts of the process to get started and familiarise with Drupal – like setting up DDEV, using Drupalforge and creating a Drupal.org profile. As the day went on, it was great to see quick progression to experiment with some of Drupal’s flagship modules, especially Drupal Views which holds huge power for presenting and displaying structured content in a range of adaptive ways.

Drupal CMS, Drupal’s low-code product provided the playground for learners. Having worked on Drupal CMS since it began, I found it heartening to see it being used to introduce Drupal to new audiences, to help them learn what Drupal has to offer and to help them start ideating on how they might use it. Adopting a UX perspective, I tuned into comments from fellow attendees about Drupal CMS, noting observations to follow up and logging areas for UX improvement to carry forward in my ongoing Drupal UX contributions.

Reflecting on the day as a whole, it helped attendees achieve what is often the hardest thing about Drupal: Getting started. All too often people new to Drupal can find it overwhelming, and they find when everything is possible it’s hard to choose a direction. Drupal In A Day sets learners on a path to start discovering Drupal and making it their own, in other words, planting a seed from which the community can continue to grow.

In our May Content Improvement Club session, we focused on how to run a usability test. We ran through the basics of putting a script together, watched a clip from a test and had a go at prioritising some issues.

Two problems for people working with content

We started this session by outlining two problems for people working with content.

We don’t see people using our websites

The first problem is that we don’t typically see people using the things we create. We build a website based on our best assumptions of what our users need and how we think they will interact with it. Then at some point in the future, someone uses the site. They might find it easy to use. They might find it difficult. But we don’t know, because we don’t get to see.

It’s hard to self-assess our own website

The second problem is that it’s hard to self-assess a website that we’re already familiar with. When we look at the site, we bring our understanding of the structure and the context it sits in. We know what the acronyms mean. We know where to find the contact form. We know where the links go.

This isn’t necessarily the case for a user coming to the site for the first time.

Here’s Steve Krug, author of Rocket Surgery Made Easy:

“If you’re building something, you’re not going to be able to see where it’s going to confuse people. It’s not going to confuse you. You know too much about it.”​

Steve Krug interviewed on the Brave UX podcast

This is sometimes known as the ‘curse of knowledge’. Erika Hall, author of Just Enough Research, explains:

Whenever we learn things, we forget what it’s like not to know those things. […] The more you know, the more you expect other people to know. And if you become a real expert in a topic, forget it.

Erika Hall writing in the Mule Design Studio newsletter: Don’t chicken out about talking to people

Usability testing is a way of addressing these problems. It gives us a chance to see what it’s like for someone visiting our site for the first time and it counteracts the curse of knowledge. That gives us valuable insight into where a design is working and where it isn’t.

A quick definition of usability

Before we go any further, a quick word about what we mean by ‘usability’.

In short, usability refers to how easy, effective and satisfying something is to use.​

The cover of Don Norman’s book ‘The Design of Everyday Things’ features a coffeepot with a spout and handle on the same side. This would score poorly in a usability test as it wouldn’t be easy, effective or satisfying to use.

ISO definition

There’s also an international standard (ISO 9241-11) definition of usability:​

“The extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use.”​

ISO definition of usability

This is a more technical definition, but it’s worth bearing in mind. It emphasises the fact that we need to think about specified users and their goals. That will come up when we get to writing our script.

A simple usability test

The simplest usability test you can do takes about 5 minutes:

1. Grab a colleague / friend / family member who doesn’t know your site​.
2. Sit them at a computer and open the homepage of your site.
3. Set them a task. For example: “Imagine you’re a student and you want to get a replacement student card”.​
4. Sit quietly and watch as they try to complete the task.

This doesn’t take much effort, and sometimes you uncover a usability issue or two.

In Content Improvement Club, we looked at how to run a more extensive usability test. But we wanted attendees to know from the outset that usability testing doesn’t have to be a big complicated process. It can still be beneficial even if you do it on a small scale.

There are three roles in every test

In a usability test, there are three roles:

• A moderator, who reads the script, sets tasks for the participant and asks questions​.
• A participant​, who completes tasks on a website​, thinking out loud​ as they do so.
• An observer​, who watches and takes notes​.

There can be any number of observers, who watch the test live or on a recording.

We watched an example of a usability test

The best way to understand how a usability test works is to watch a recording of one.

Here’s Steve Krug demonstrating how he runs a test:

Usability Test Demo by Steve Krug (YouTube video, 24 minutes)

In Content Improvement Club, we watched a short clip of someone completing a task on a library website at a UK university.

This was the task:

You’re working on a presentation with four other people from your course. You need to find a study room in the library for the four of you this weekend. Can you find out if a suitable study room is available at the library?

We noted down the issues that we saw, and then we shared them on a Microsoft Whiteboard. Even though it was a single task, there were a lot of issues, which is fairly typical. That’s why it’s often helpful to follow your observations with a prioritisation exercise.

We prioritised the issues

In the session, we demonstrated how you can prioritise issues using a matrix like this:

To place things on the matrix, you make a quick assessment of how significant the issue is. Then you assess how easy it would be to fix. The issues you typically want to focus on first are those in the top right quadrant: major issues that are easy to fix.

It can be helpful to have a set of questions to establish whether an issue is major or minor.

These are adapted from Dave Travis’s work on Red Route usability testing:

• Does the problem occur in a task that is highly important to you or your users?
• Is the problem difficult for users to overcome?​
• Did multiple participants experience the same problem?

Red route usability: The key user journeys with your web site (Dave Travis / archive.org)

Using questions like these give you a shared set of criteria when assessing the significance of usability issues in a group.

But we’re getting ahead of ourselves. How did we get to this point?

How to write a script

Before you can run a series of tests, you need a script.

Use a template

We shared our usability testing script template, which draws on the work of Steve Krug:

Research brief and usability testing script (DOCX, 51KB)

The template includes:

• a brief, where you articulate the goals of the testing
• a lead-in script, which you read to participants before testing begins. This covers what the session will involve and sets expectations. ​
• a set of tasks, presented in a table with two columns. One column contains the task, and another the expected path to complete the task.​

Come up with tasks

We start by identifying tasks and then develop them by adding a scenario.

We practised this in the session. Attendees suggested tasks that someone might need to carry out on a university library website. For example:

• Check opening times
• Find a book on your reading list

Develop tasks by adding scenarios

Next, attendees developed these tasks by adding a scenario.

For example, “Check opening times” becomes:

• You’re a student and you want to check the opening times of the library during the winter break. How would you go about doing this?

“Find a book on your reading list” becomes:

• You’re a new student and you want to find the book “Campbell’s Biology”, which is on the reading list for your course. Can you show me how you would find out if the library has a copy of this book?

Tips for writing good tasks and scenarios

We shared some tips for turning tasks into questions​:

• Aim for 5 to 10 scenarios per session.​
• Keep tasks focused: one goal per task.​
• Frame tasks as realistic scenarios, not instructions.​
• Add light context to make it realistic.​

We recommend running a pilot usability test before going ahead with a round of testing. This helps you see how the full usability test flows from start to finish. ​It also gives you a chance to refine the script and ​ensures the session runs smoothly for participants on the day.​

Logistics

Ahead of the Content Improvement Club session, we asked attendees if they wanted us to cover any topics in particular. Most of these came under the wider topic of logistics.

When should you test in a design process?​

Test earlier rather than later. Testing earlier in a design process is ideal because it’s easier to make changes based on what you learn. If you’re creating something new, this usually involves creating rough drafts or prototypes, and then later, a high-fidelity version.

Changing a prototype is easy because no one is very attached to it. But when a design is a later stage of development, it tends to be more painful to learn that something isn’t working. By this point, you and your colleagues have usually invested a significant amount of time in an idea. If the fix involves changing something fundamental to the product, you might have to undo work that’s already been done.​

How long does a testing session take?

It varies, but we usually run testing sessions of about 30 minutes.

How many tasks do you set?

In 30 minutes, we can usually fit in between 5 and 10 tasks.

How many participants do you need?

Three to five participants is a good target. Jakob Nielsen argued that five participants is enough for one round of tests. This is because when you run the same tests with multiple people, you tend to see the same usability issues reoccurring. It’s a case of diminishing returns: by test number six, you aren’t usually learning anything new.

Why you only need to test with five users (Nielsen Norman Group)​

In Rocket Surgery Made Easy, Steve Krug recommends three participants per round of testing. This way you can run more rounds of tests – for example, by testing an initial idea and then a later iteration of the same design.

How do you recruit participants?

Mailing lists, Teams channels and surveys are great places to put call outs. We advise that you avoid revealing the testing topic or content in advance.​

Aim for participants who reflect real users where possible. This can prove difficult, so don’t let it stop you if you can’t find participants who don’t match the profile of your users.

How do you take notes?

Obviously, you can scribble notes anywhere you like. When we’re running a series of tests, we often take notes in a spreadsheet. This allows us to quickly spot which tasks caused problems for multiple participants.

Usability test results spreadsheet (XSLX, 36KB)

How can we make testing more accessible and inclusive?

Accessibility and inclusivity should be considered from the very start of the testing process. One important step is asking participants early on whether they use any assistive technology, so you can understand their setup and make any necessary arrangements. Our own introduction questions and templates include this for that reason. In the template, this is an introductory question, but if you’re recruiting via a survey, you could mention this there.​

​Having a diverse participant group will usually lead to more useful and representative findings. For example, if you’re testing a student-facing service, including both undergraduate and postgraduate students can help capture a wider range of experiences and needs. Again, this can sometimes be a challenge, but if possible something to aim for.​

Ideally, usability testing should include participants who regularly use assistive technologies. This provides valuable insight into accessibility barriers that might otherwise be missed. However, this can sometimes be challenging due to recruitment costs, specialist panels, or limited budgets.

Some resources that can help:

• Deque: Considering accessibility when designing a usability test ​
• Scottish Government Service Manual: Usability testing with disabled and non-disabled people​

Acting on the findings​

So you’ve run a round of testing. What happens next?

Involve senior colleagues in playbacks

In 2015, Caroline Jarrett wrote about a phenomenon she and Steve Krug had noticed when working on websites. They would run a set of tests on a website and uncover some usability problems. But then six months later, the problems were still there: no one had gone in and fixed them. To investigate why this was happening, they ran a survey of UX professionals.

Caroline Jarrett and Steve Krug’s analysis of why usability problems go unfixed

Out of 131 responses, the most common reason was that findings from usability testing conflicted with a decision maker’s opinion.

One way to address this is to get decision makers in the room (or on the Teams call) when you watch a highlights reel of test recordings. There really is no substitute for seeing user behaviour first hand. Reading about it in a report doesn’t carry the same weight.

Use the momentum created by testing

Testing creates a shared motivation to fix things​. Use this to your advantage. If you can, take action to fix usability problems while people’s memories are fresh​.

Make small changes first

Sometimes usability testing highlights small problems that are easily fixed. A link that doesn’t go where someone expects it to. An item missing from an A-Z.

Sort these things out first. Small wins like this give you the motivation to sort out the knottier problems.

If you want to find out more

This was a quick introduction to running your own tests. If you want to learn more,  Steve Krug’s introductory guide is a great place to start:

Rocket Surgery Made Easy by Steve Krug (listing on DiscoverEd)

We post about case studies of usability testing at the University on this blog:

• How usability testing guided user-centred improvements of the Digital Collections Platform (Emma Horrell)
• How usability testing helped the Library to improve their website (Nick Daniels)

The Prospective Student Web Team do the same:

• Usability testing the display of multiple entry years in the undergraduate degree finder (Pete Watson)
• Usability testing of new programme search and page navigation features (Nicole Tweedie)

Acknowledgements

Various points made in this blog post are taken from this 2015 post by Neil Allison:

Making usability testing agile

How to hear about future sessions

We promote these sessions via our mailing list. If you’re interested, please sign up:

Join the UX and Content Design mailing list (University login required)

Suggest a topic for a future session

We picked usability testing following a suggestion from the community. We’re keen to continue covering topics that colleagues across the University would find useful. It would be really helpful if you could let us know any ideas you have using this form:

Suggest a topic for Content Improvement Club (University login required)

Other training that we offer

More training is listed on the User Experience Service website:

Training | User Experience Service

Our Editorial Style Guide contains many conventions and we know from research that publishers struggle to remember to apply them. Could automation help? We experimented embedding style guide rules into a Drupal module that checked content against the rules in the editorial interface and suggested corrections when the rules weren’t followed.

As part of a concentrated effort to make it easier for publishers to apply our style guide rules, Mostafa Ebid, a student who came to work with us in summer 2025, installed Editoria11y, an open-source Drupal feature and configured it with selected rules from our University style guide. Applying this feature to a site with demo content, he tested it with University staff to see how well it worked, and how useful and usable they found it.

Read more about our ongoing style guide work:

Collection of blog posts about the Editorial Style Guide

Drupal Editoria11y is an open-source accessibility checker

Editorial accessibility ally (shortened to Editoria11y) is a module developed by John Jameson from Princeton University that enables automated content checking directly in the editorial interface. Originally created to help content editors catch accessibility issues, its configurable architecture makes it well suited to embedding other kinds of rules, such as institutional style guide conventions. The checks can run in real time as content authors type, and can also be applied to content in published pages and previews, to flag issues to be corrected.

Editoria11y can include more than 50 built-in content tests covering image alt text, link quality, heading structure, and general content issues. Flagged issues are noted at the page level by an indicator bar, which includes a count and a categorisation of the type of issues (default categories are headings and alt text). Through this bar it is possible to identify the precise inline location of the problematic content with visualisers. Each visualiser is associated with a modal that contains detail about the problem element and plain language tips explaining how to fix it.

In addition to the on-page alerts, it is possible to review issues across a site via a reporting dashboard which can be configured to log recurring issues or most problematic pages, as required.

Read more about Editoria11y on Drupal.org:

Editoria11y Accessibility Checker project page

We designed tests to learn if Editoria11y would be useful and usable to embed style guide rules

Editoria11y presented itself as a mechanism to enable web publishers to check their content against style guide rules, and we wanted to understand the kind of experience this provided in EdWeb2. In particular, we wanted to understand:

• If Editoria11y could effectively check content against our style guide rules
• How Editoria11y presented results of the checks in the editorial interface
• If publishers could use Editoria11y to correct content that misaligned with the style guide

Moreover, we wanted to learn if the addition of Editoria11y to the EdWeb2 editorial interface improved the publisher experience or not.

We picked deterministic rules about dates and numbers for the tests

Like many Drupal features, Editoria11y is very flexible and customisable to fit a range of different use cases. Since it was new to our publishers, we were keen to avoid overengineering it, taking care to configure it to present the minimal information necessary to achieve our testing goals.

The University’s Editorial Style Guide contains between 60 and 70 separate rules, of which fewer than half are deterministic (in other words, can be applied directly without editorial judgement). It made sense to include deterministic rules in the tests, to enable us to accurately assess how effective Editoria11y was at checking them.

Rules in the dates and numbers section of the style guide seemed a good fit for the tests, so these were configured into Editoria11y.

We set up Editoria11y to display an indicator bar, visualisers, modals and a dashboard

The module was set up to display the indicator bar, the inline visualisers and the reporting dashboard. Content that contained sentences with dates and numbers was saved in draft in a demo interface which had Editoria11y applied. In the test scenario, participants were asked to assume they had been asked to review this draft content before it went live and to use a checker tool to help them with this.

 

 

Watching participants use Editoria11y, we learned what worked and what didn’t

Tests were carried out with seven participants. Observing how they made sense of the different parts of Editoria11y and interacted with it, we were able to draw conclusions about how useful and usable it was to support the context of web publishing in EdWeb2.

Participants understood the relationship between the indicator and the visualisers

The first part of the tests involved showing participants the draft content in the editorial interface with Editoria11y applied. All of them were able to understand that the number of style guide errors on the page were tallied in the indicator bar at the bottom right of the page, and that they could use the indicator bar to reveal precise locations of the errors on the page, together with a modal for each error, explaining the error and the suggested fix.

Placement of the visualisers and modals often made it hard to read the draft content

Taking an overview of the visualisers in the draft content, participants commented that they felt a bit overwhelmed to see so many. Some felt the placement of the question mark visualisers made it difficult to ascertain which parts of the content needed to be corrected, which was slightly helped by yellow outlines around the text. Placement of the modals masked the text beneath, however, meaning participants found it difficult to engage with the original context of the errors to be corrected to assess whether to accept the suggestion or not

Descriptions of the errors in the modals were not clear to some participants

Reading the information in the modals, some participants were unclear of the error it was pointing out. Specifically, in a modal describing the rule to write dates without including ‘th’ or ‘rd’ after numbers was written as ‘Write dates without commas or ordinal suffixes’. Some participants were unfamiliar with what ‘ordinal suffixes’ were, but seeing the suggested change presented as a ‘before and after’ tracked change with the error crossed out (in red) and the suggested change presented in green helped participants understand the proposed correction.

Participants were able to use the modals to correct some style guide errors

When they had read and understood the errors being highlighted, most participants were able to assess whether to accept the suggested fixes and apply them based on the information contained in the modal. Several participants entered into a flow of using the arrow keys in the modals to clicking through the different errors and accepting the fixes, especially when the fixes related to a recurrent error – for example, capitalising the first letter when writing days of the week. They appreciated a notification alerting them that the fix had been applied, which temporarily appeared at the top right of the screen, although some said they would have appreciated this notification to remain for longer as it was easy to miss. They were also unclear whether they needed to save after applying each fix, or if this could be done when they had worked through all of the fixes on the page.

Not all errors were fixable with automatic rule application – some required interpretation and judgement

Viewing the suggestions in the modals, several participants noted a problem with the fixes being based on automatic application of the style guide rules. Specifically, relating to the rule about omitting ordinal suffixes for numbers, there were several instances where it didn’t make sense to apply this rule directly. For example, a date written as ‘5th of December’ contained a suggested fix to remove ‘th’ but not to remove ‘of’. Another relating to ‘4^th year’ of studies suggested removing the ‘th’ but not writing ‘fourth’ as a way to make the sentence readable, and similarly, another advised removing ‘st’ from ‘1^st floor’ which was an incomplete fix.

Some participants’ trust in the checker waned when they realised it didn’t catch all the errors

Of those who took part in the tests – some were more familiar with the rules of the style guide than others, and this impacted the trust they placed in Editoria11y. When they reviewed the Editoria11y outputs against the draft content and spotted corrections that hadn’t been picked up or flagged by Editoria11y, they were inclined to disregard the tool and check the content for themselves to ensure the text was completely compliant.

Few participants said they would use the dashboard as they felt that was for a site administrator

Navigating through the different options on the indicator, participants were able to access the dashboard interface, which was titled ‘Content Accessibility Issues’. Reviewing what was there, in sections called ‘Top issues’, ‘Pages with the most issues’ and ‘Recent issues’ most participants said they didn’t feel they would use this feature, and presumed it would be for someone with full responsibility for the whole site.

Editoria11y has potential as a style guide helper tool, but would need contextual refinement

Taking the findings together, Editoria11y showed promise as a way to embed the style guide rules into draft content, to avoid web publishers needing to navigate away from the editorial interface to check the rules in the guide itself to then apply them. Participants understood what the indicator bar was there to achieve, and liked the idea of being able to check off corrections to their content through the modals. Some identified areas for improvement included:

• Refinement of the style guide rules embedded in Editoria11y, to include examples, exceptions and applications based on scope and context
• Embedding a more complete set of style guide rules into Editoria11y to help build trust in the tool
• Adaptation of the modal options to include ‘Accept with edits’ as well as ‘Accept’ and ‘Ignore’ to account for instances where editorial judgement needed to be applied to the suggested correction
• A way to show corrections in categories (for example, all those relating to numbers together, all those relating to punctuation together and son on) which could be addressed by the editor in sequence, to avoid overwhelm in the interface
• Suggestions for correcting content when reworked sentences were required – powered by an LLM such as ELM

As well as Editoria11y surfacing style guide rules, there is potential to use it for its intended purpose, as an accessibility checker, containing checks about the heading hierarchy and alt text on images. If this was to be included as well as style guide rules, however, progressive disclosure of the information should be used to avoid the interface becoming too cluttered.

Many of these areas overlap with work currently progressing in open-source Drupal, firstly develop a Context Control Center – as a way of handling rules to enable AI-assisted content production, and secondly to develop an AI Content Review feature, capable of reviewing content against defined rules and conventions.

Read more about these Drupal developments in my related blog post:

Think like a machine: How building a Drupal context-handling feature is providing a new lens on content design and style rules 

 

AI development keeps evolving as do the ways people seek to use AI. Traditional software development runs the risk of trying to perfect AI features people won’t use. Revisiting our previous AI research helped me tease out new opportunity spaces for AI features to help with content design tasks.

Last summer, Mostafa Ebid joined the UX team for a summer internship and built an AI assistant tool which integrated the University’s main AI provider ELM into EdWeb2, our Drupal content management system. The idea for the tool came from hearing University describe the difficulties they experienced when publishing web content. The tool included the capability to write content, design content and proofread, and was designed to work by typing prompts in a chatbot interface, right-aligned to the main part of the editorial interface. When prompted, an orchestration of AI agents were triggered to read textual content and use ELM to make suggestions for improvement based on what the publisher had asked for. Improved text was displayed in the sidebar chatbot interface for the publisher to review and consider using.

Read Mostafa’s blog post about how he developed the tool:

Integrating ELM with EdWeb – Building an AI tool for publishers

Initial tests of the tool with publishers revealed some potential, but identified the need to do further tests, specifically to understand limitations around the user interface display and to learn if the tool was useful to publishers in the context of content they were familiar with (as opposed to generic stock content that was used in the first round of tests).

Read my blog post about the initial tests of the tool:

Initial insights from UX testing our Drupal AI content assistant tool 

Mel Batcharj accessibility tested the tool, focusing on keyboard navigability, and Nick Daniels ran tests with two publishers in October last year. I recently reviewed the findings of this research to consider advancements in Drupal AI in the past 12 months, to assess whether the original premise for the tool was still valid, and to think about residual content design challenges AI could potentially help with.

Advancements in Drupal AI have resulted in improved AI features

The Drupal AI Initiative began in April 2025 with a group of participating organisations making a commitment to collaborate to build the future of AI in Drupal. As a result of the initiative, various workstreams began to shape Drupal’s infrastructure to support AI, to experiment with new innovations and to improve the UX.

Read more about this workstream:

Drupal AI Initiative project page on Drupal.org

A more accessible AI chatbot is now available as a Drupal recipe

Our original AI content assistant tool was built into a panel of the editorial interface as a custom build, which worked well when using a mouse, but which accessibility testing showed was restrictive when navigating using a keyboard. Since the tool was built, however, accelerated development in the wider Drupal AI community prompted the creation of an open-source AI chatbot freely available to apply. Adopting this chatbot was preferable as it avoided the need to maintain custom code and it was possible to use it with a keyboard only.

There’s an active Drupal issue to address the need for the AI chatbot interface to be expandable

Several of the participants who took part in Mostafa’s tests of the tool last year found it awkward to scroll through the AI chatbot output as it was presented in the narrow left-aligned interface. The same problem had been noted in the wider Drupal community, and therefore I raised an issue to have this rectified, which is being worked on as part of the AI Initiative task backlog.

We did more tests on our AI tool – this time using participants’ own content

In the first round of tests, four participants were all presented with the same piece of content (on the topic of safety procedures) and asked to use the AI tool to improve it in specific ways (such as writing it for user needs, making link text better, and so on). This approach turned out to be limited, as since participants were unfamiliar with the content, they were unable to assess whether the outputs of the AI tool were an improvement on the original content or not.

In a subsequent round of tests we therefore adopted a looser approach – asking participants to supply a piece of content they were already working on, and then asking them to use the AI tool to improve it to suit their needs. The results from these tests were more indicative of how useful publishers found the tool to make content improvements.

Results of these tests highlighted how the AI tool needed to change

Since we placed participants in a situation where they were using AI on content they knew well and could critique and appraise authentically, the results of these second-round tests gave clearer indications of what worked with the existing tool, what didn’t and where AI could be best applied to help with content design tasks.

Participants didn’t notice the content options in the AI tool, or the help text

The tool contained three different content options: Design Content, Write Content and Proofread, presented in a dropdown menu in its interface.

Initially, participants didn’t notice these options and used the default Write Content option.  When they later experimented with the Proofread option they found no discernible difference between these options in terms of outputs, which led them to believe that a simpler version with a single conversational interaction option would be preferable.

The tool defaulted to reading the content on the page, and working on this when prompted. Participants were initially unclear that this is how it worked, and they didn’t notice the help text to enable or disable this mechanism presented in the tool interface. Taken together, this feedback suggested that a simpler version of the AI chatbot, such as the one from the Drupal recipe, would be easier for publishers to use.

The AI tool had some value as a writing partner to suggest restructures to textual content

Responding to the task to experiment with the AI tool to improve their content, the participants quickly got used to how the tool worked, and recognised its use as a writing partner to prompt about their content and receive suggestions for improvement in a conversational way.

Prompts they used to improve a page of content in the body text field included:

• ‘Rephrase copy to condense, highlight key messages and make it accessible to pet owners looking to join practice’
• ‘Proofread copy so that it appeals to pet owners non clinicians’
• ‘Turn this page into web ready content. It needs to be concise, easy to scan, readable to a wide range of audiences’

These prompts resulted in edited versions of the page content, typically including structural elements like headings, bullet points and calls to action, delivered in the chatbot interface.

The tool lacked capacity to tweak or iterate on previous versions of content – which participants wanted

Once they had reviewed the tool’s initial outputs, both participants entered conversational turns with the tool, asking it to perform successive tasks on the content it had previously produced, to edit it further, in line with their specific requirements and rules.

Prompts they used to tweak initial AI outputs included:

• ‘Remove adjectives and exclamation marks’
• ‘Remove brackets’
• ‘Remove any unnecessary words, fix typing errors, suggest improvements for SEO’

With every new prompt in the conversation, the tool produced a fresh output, meaning the publisher was left to review a succession of different versions of the edited content, presented in the chatbot interface, without any indication of what had been changed. Participants found it difficult to review edits, as they would usually do when working on a piece of content, in order to compare the ‘before’ with the ‘after’ – ultimately to assess whether the AI tool outputs were to their satisfaction.

They said they would have liked the tool to have presented the edits in a ‘tracked changes’ format that they were familiar with from word processing programmes.

Participants didn’t really need the tool in the interface as they tended to edit text content elsewhere

When describing their usual content process, participants said they would usually prepare their content in a word processing programme like Microsoft Word rather than edit directly in the Drupal editorial interface. There were several reasons they chose this method – a key ­­reason being the need to involve others to check (and in some cases, sign off) their content in preparation for the website. They were more familiar with referring others to check their content or proofread it when it was in the Microsoft suite, than when it was in the Drupal editorial interface.

Furthermore, Microsoft Word accommodated the addition of comments and tracked iterative changes to pieces of content which was not possible within the Drupal editorial interface. This content preparation habit suggested that while the AI tool was useful to suggest content edits, this would have been more useful before the content was in the interface, and therefore could be achieved by pasting content to be edited into a browser-based AI tool or app (such as ELM).

Within the interface, the tool only had use as a ‘final check’ mechanism, to catch any typos, errors or style misalignments before the content was ultimately published.

The tool needed to be able handle more than text as pages were typically made of multiple elements

Reviewing the test set-up and comparing it to their usual ways of working with EdWeb2, the participants said the pages they worked on would usually be made up of more than just textual content in the body text field. They would typically work on pages with multi-column layouts, making more extensive use of Drupal paragraphs or including structural elements like accordions, feature boxes and cards. They were interested to know how the tool may make appraisals or suggest improvements for those sorts of pages to help them arrange their content in appropriate ways.

We identified new opportunities for AI content publishing features

Extrapolating on the feedback from the tests, several use cases and scenarios for applying AI to content design tasks emerged, which will help inform our ongoing work to apply AI to make content design tasks easier for publishers.

AI-assisted content structuring

Describing their typical content writing workflow, participants said they found it difficult to move from a text-based editor like Microsoft Word into the Drupal editorial interface where they needed to make use of Drupal Paragraphs as well as page elements like accordions to structure the content. Potential areas for AI development could therefore include:

• A mechanism to convert textual content into appropriate structural elements
• A way to make suggestions for accordion labels or structure
• A feature to ensure uniform creation of cards or feature boxes
• A means of cross-checking style consistency of pages made of multiple elements or with a specific layout

AI- assisted content design for SEO/GEO/AEO

Having their content picked up by search engines or being machine read was something participants wanted, but they were unsure how to write, tag and structure their content to make this happen effectively. Potential areas for AI development could therefore include:

• A way to have their content analysed for SEO effectiveness, based on signals like content quality, scannability and key word alignment
• A mechanism to suggest content changes aligned to specifically defined SEO goals and target user engagement measures

AI assisted content reviews – against specific style conventions and contexts

As well as ensuring their content followed the rules of the University’s Editorial Style Guide, both participants mentioned other conventions that they needed to apply to their content, that existed at a more local website level. For example, one participant’s site had a rule not to use brackets or exclamation marks, or to overuse adjectives, so they would have found it helpful to have a way to cross check content against these rules before publishing.

The Drupal Context Control Center is an emerging feature designed to handle the application of context rules within a site across various scopes and use cases, and Drupal AI Content Review is a related feature, designed to appraise content against given context rules and conventions. Together, these Drupal features may be a good fit to help University web publishers make use of AI to shape their content with the uniformity they require.

Read more about the Drupal Context Control Center and its development in my recent blog post:

Think like a machine: How building a Drupal context-handling feature is providing a new lens of content design and style rules

Read about AI Content Review on Drupal.org

We’re changing the direction of the AI tool based on what we’ve learned

Last summer it seemed certain that an in-interface AI chatbot content assistant helper was what we needed to build. A few improvements to the UI to make it expandable and navigable with a keyboard and it would be ready to go. As it turned out, things had moved on, and these problems were addressed by the wider Drupal community. This meant we could go back to our research findings to re-examine how AI could be best applied to aid content design tasks, and to consider how it could best fit into existing workflows of our publishers to assist them with difficulties they faced. As we continue with internships this summer, we’re excited to re-focus and plan more research to explore some of these emergent opportunity areas. Aligning with in-progress Drupal AI developments, we’re open to learning how we can apply and adopt the work of the Drupal community to our University digital publishing context.

Riding on the success of our internal Effective Digital Content course, we set out to expand by building an external version for the short courses platform, taking a product thinking approach. Three months on, experimenting with a proof-of-concept course has convinced to pause this work – to avoid falling into a build trap.

The success of our relaunched Effective Digital Content (EDC) course, to date completed by more than 400 University staff, prompted ambitions to reposition the course for external audiences by including it in the University’s Short Online Courses platform. With the help of colleagues from the Short Online Courses team and the Learning Technology team, we did some market research, identified target audiences, defined a product vision and goals and began using the Canvas platform to develop a proof-of-concept.

Read more about our plans to reposition EDC as an external course in my blog post from March 2026:

Repositioning Effective Digital Content as a short online course: A product approach

At the end of sprint three (of a total of seven planned sprints) we faced unknowns and unanswered questions preventing us from achieving some of the fundamentals we’d defined in the product vision and goals. Resisting a sunk-cost fallacy-motivated urge to continue building, we made a sensible decision to stop and to pause until we’re in a better-informed place to have the confidence to continue.

In this post, I reflect on the benefits of adopting product thinking, the discomfort with facing difficult questions upfront and the practicalities of learning about audiences for expansion opportunities.

Revisiting the Product Kata helped clarify feelings of uncertainty

Melissa Perri’s book ‘The Build Trap’ contains a helpful framework to guide product development which I have become familiar with through my involvement with Drupal product teams. On the strategic level, this framework helped me set out a vision for an external version of the Effective Digital Content course, establish the problem the course was aiming to solve and its audiences, and work out the learning outcomes associated with each course module.

Entering the execution phase, however, taking each course module at a time and building each one out to meet the needs of the established audiences, things took longer than planned, and felt more difficult than we’d anticipated.

Pausing for reflection, we unpicked where things were going wrong. Going back to our vision, we had wanted to repeat the success of the internal EDC course, giving learners practical experience of writing digital content for their audiences. We had been able to instill this experience in the internal course because we had been able to regularly engage with University web publishers, to fully understand their content design challenges and design practical experiences for them that specifically addressed their friction points. Without such detailed insight into external audiences, we were effectively basing our course design decisions on guesswork and assumptions, which explained why our initial efforts seemed to be missing the mark. The strategic vision was sound, but our capacity to fully explore the problems and optimise associated solutions was limited, therefore execution was flawed.

The practical workbook is EDC’s best asset – but is hard to replicate for broader audiences

A lightbulb moment in our reflections came when considering what worked well about our internal EDC course. When staff complete this course, they are required to complete exercises in a workbook which they submit to the UX team for feedback. This tests learners’ ability to do fundamental content design tasks like structure headings, write hyperlinks and turn lengthy text into chunks to be scan-read. Reviewing more than 400 submitted workbooks, we affirmed the importance of this element of the course both for learners and for our team as owners of the course as a product. Through these workbooks,  learners are able to practice content design in the specific context of the University sites they are responsible for, and as the product team, we are able to see impact the course has had in improving content design knowledge.

Replicating the workbook concept for external audiences would require knowing about the contexts and content they were working with, in order to design exercises that required them to test those skills. Without knowledge of our audiences’ circumstances, the best we could do would be to design a generic set of exercises, devoid of the nuances needed to really engage learners in practising content design techniques.

Testing what we had built so far taught us what we didn’t know

In UX and product design there’s a saying: the best time to test is yesterday, failing that, test now. Resisting the urge to keep building EDC modules, we made the decision to take the three modules that had been built and to test them with University publishers. Feedback from the tests was positive, which was nice to hear, but didn’t really help us assess if the modules we had made were a good fit for external audiences. University staff wouldn’t take an external Effective Digital Content course as they had already taken the internal version. To assess if we’d made a good product we needed to know answers to questions such as:

• What needs should we prioritise for our target audiences?
• How do our audiences currently meet these needs?
• Would univerisities with a distributed content model find an external course useful?
• What would motivate our target audiences to take this type of course?
• How well do existing courses meet user needs and expectations?
• Will our proposed course be valuable to target audiences?

In another of Melissa Perri’s books ‘Product Operations: How successful companies build better products at scale’ by Melissa Perri and Denise Tilles, the authors use a case study at a financial services organisation, Fidelity, to show the relationship between user research and the product design lifecycle. Applying this relationship by positioning EDC external as the product, it was clear that our outstanding questions fell into the ‘Core UXR question’ category at the ‘Discover’ ‘Define’ and ‘Design’ stages and therefore needed to be addressed before proceeding to the ‘Develop’ and ‘Deploy’ stages.

Having worked with other teams both within and outside the University, we knew all too well the potential risks and consequences of building without adequate research – and resolved that it was better to stop building to avoid wasting effort.

Successful expansion will rely on targeting audiences and researching their needs more fully

Taking a pause in the build will allow us to take time to assess the gaps in our knowledge of our target audiences, and work out ways of learning what we don’t know. Understanding the nuanced needs of our target audiences will help us familiarise with the market for content design training in the public sector to assess the value of the expansion opportunity. Referring again to ‘Product Operations’, learning the differences between the markets associated with our expansion opportunity (the total addressable market, the serviceable addressable market and the serviceable obtainable market) will help us decide whether the proposition is viable, deliverable and desirable or not.

In the meantime, we’re using what we’ve learned to improve internal EDC and training

As a team, we really value what we learn from time spent in research, and we always endeavour to act on what we have learned and make sure research does not go to waste. In this case, going through the process of reworking three course modules to aim them at external audiences has pinpointed ways to improve the internal version of our course – in particular to make the introductory module clearer and more impactful and to be clearer on some accessibility concepts. Submitted workbooks and feedback from our regular Content Improvement Clubs also provide a constant source of learning, to identify areas publishers still struggle with that we can continue to address with subsequent tweaked versions of EDC and topics for additional publisher training sessions.

In January 2026, Alex Burford, a learning technologist from the School of Informatics contacted the UX Service following completion of the new Effective Digital Content online course. Alex had really enjoyed the course and was keen to explore ways to implement the content design best practice principles it teaches within Open Course – the platform that the School of Informatics use to share their course materials.

Informatics Open Course Materials

Identifying key areas for improvement

Alex had received positive feedback from students who were able to successfully locate their course materials. However, from our discussions and a review of the content on the Open Course platform, (which relies heavily on tables), we both felt there was scope to review the current layout and explore potential alternatives.

Within the tables, we also identified that a more consistent and informative approach to formatting links as well as creating more effective headings could improve the user experience.

In addition, Alex was keen to look at the best ways to support Open Course publishers with content design and embed ways to make it easier for them to prepare effective content. To this end, we talked through potential guidance and training that could be developed with a specific focus around Open Course content.

We spoke with Open Course publishers to gain their perspectives

Before progressing any further with our ideas, we were keen to establish a baseline understanding of how Open Course is currently used by those who publish content on it. This would help us to learn about their processes and approaches, identify pain points as well as areas that were working well.

Alex reached out to Informatics colleagues to see if they would be happy to speak to us about their experiences of using Open Course. In late January / early February 2026 we carried out a series of semi-structured interviews.

Format and aims of the interviews

The UX Service facilitated the interviews via Teams, observed by Alex, to learn about the experiences of Informatics colleagues who published content on Open Course. Informatics colleagues shared their screen and talked through how they achieve content publishing and formatting tasks in Open Course. This enabled us to see first-hand how they went about these tasks and hear their perspectives and feedback.

The aim was to confirm areas that were working well and also the main pain points. We were keen to learn the reasons why tables were used, what the experience was like for those editing content within them and understand more about their approach to writing headings and links.

We gained a lot of useful information from the interviews both from a content perspective, but also in relation to more technical aspects relating to the platform itself which Alex could feedback to Graham Dutton, the developer working on Open Course.

This information then provided the basis on which to form an action plan around how to make improvements and decide on the best way to provide support and/or guidance.

Tables: were they necessary, if so could they be improved?

Following our discussions with Alex and the semi-structured interview insights, we felt we had a good understanding of how tables were used and the pain points relating to editing content within them.

We first wanted to explore whether tables were needed, or if there were alternative layout options to avoid the need for tables completely. If they were needed, we could then look at how they could be improved?

Prototypes were created by the UX Service

The UX Service mocked up some prototype tables, initially in Word before also creating them in an Open Course playground site we had access to. We used the following ‘Schedule and materials’ page, where almost all information was displayed in a table.

INF2-SEPP: Schedule and Materials | Open Course Materials

Prototype one

Our first prototype involved removing tables completely and using headings and paragraph text to display the same information. What we soon realised was that this made the information hard to scan and increased the length of the page. We felt from trying this approach, there were definite benefits to having a structured column layout to help communicate key dates and materials for course participants efficiently.

Prototype two

Our second prototype then explored how we could improve the current table layout and make it as accessible as possible. The existing page had a continuous table with six columns. All headings and links were included in the table itself which made it quite hard to navigate. Also, the number of columns meant that text was wrapping onto two lines quite often, which again impacted on readability.

We explored presenting the same information in a two-column layout, using rows with clear left-hand headings to signpost key information. We also split the continuous table into multiple tables, one for each week of the term. Column and row headers were also applied within the table editor tools. These changes we felt improved the overall look and feel of the page but also made it easier to navigate to the information you needed.

Stress-testing prototype two

After sharing both options with Alex, we decided to proceed with prototype two and carried out some further testing to make sure it was fit for purpose.

JAWS – Screen reader testing

Using the prototype in the our Open Course playground site, we used JAWS to test out how the content on the page would be navigated and interpreted by a screen reader.  Whilst this approach may not fully capture how all users of assistive technology navigate, interact and experience the page, it did provide useful insights around how tables in Open Course interact with screen readers. Overall, the new table layout responded well.

We found that JAWS:

• read out the number of columns and rows in a table as expected.
• navigated the table content in a logical order. It interpreted tabled cells moving from left to right, first reading the column headers (‘dates’, ‘themes’) before information and links within the cells.

JAWS didn’t pick up on blank cells within the table. Instead, it moved to the next table cell which had content within it. Therefore, we recommended that if this table layout was adopted that there should be no blank cells to aid clarity. Where there was no information to put within certain cells, text such as ‘no milestone’ or ‘no tutorial’ should be used.

We are also aware that screen reader users can and may wish to jump from table to table on a page. When navigating in this way, JAWS moved table content to table content without reading out the assigned ‘Week X’ heading we had inserted. Therefore, when you have multiple tables on a page, it’s hard for the user to tell which table they are on. A potential solution to aid understanding could be to incorporate the week number within the table itself, rather than using headings.

Magnification and responsiveness on mobile

When tested, the page (including tables) responded well to 400% magnification, with no loss of information. This check was carried out to ensure that the content reflows correctly for users who routinely view web pages using zoom functionality. We wanted to ensure that information did not move off screen or overlap when zoom was used.

The tables also responded well in mobile view, including when the screen was rotated. This is important to test given the increased usage of mobile devices to access digital content, to ensure all users were able to easily read / navigate the information on the page.

UX Service recommendations

Recommendation to use prototype two table layout going forward

Following our prototyping and stress-testing, we reported back to Alex that prototype two was the option we would recommend.

Recommendation to consider best formats for guidance on headings and links

From reviewing the pages and considering the insights gained from the interviews, we also felt that it would be beneficial to create guidance around:

• writing effective headings and using correct heading levels in tables
• how to use links and write effective link text.

Having this guidance close to hand, ideally accessible directly from the edit screen of the Open Course platform would be preferable to enable ease of access for users.

Reflections and next steps

As a team, we really enjoyed working with Alex on the project. It provided us with the opportunity to apply the content design best practice principles that we teach specifically to learning and teaching content. It was interesting to explore and test out options for using tables in a different context, outwith the central EdWeb 2 Content Management System, for student-facing content.

We are glad that the Effective Digital Content online course provided the impetus for this work. We hope that the course continues to provide the starting point for interesting conversations about how digital content can be improved as it reaches wider audiences around the University.

We look forward to continuing to work with Alex in relation to the guidance and potential implementation of the new table layout before the next semester or at an appropriate time in the future.

As we approach the first anniversary of the launch of the new Effective Digital Content course it was timely to review our approach to marking the content design exercises completed by learners to look for ways to simplify and potentially automate aspects of the process.

In May 2025 the UX Service launched a new version of the Effective Digital Content (EDC) course. The course covers content design fundamentals relevant to digital publishing at the University. To ensure we continue to improve the quality content across our digital estate, all those who publish content for our institution are required to complete the course.

Read more about the Effective Digital Content course, its contents and its development in the blog post from the UX team:

The new Effective Digital Content course is now live

Completing exercises within a workbook is a key part of the EDC learning experience

Content design is a practical discipline that is best learned by doing, therefore, when we redesigned the EDC course, it was important to include an interactive element that ensured learners gained practice trying out key techniques as part of the learning experience.

This practical element manifests as a workbook – as learners work though the different modules of the EDC course, they complete related exercises in a workbook. When they have finished all six EDC modules, they submit their workbook with their completed exercises to the UX team. We mark their exercises, return feedback on their work in the form of comments within the submitted workbook and then issue them with accreditation in the form of a digital badge.

The UX team devised a workflow to manage marking workbooks and providing feedback

The workbook submission, assessment and feedback process represented a new way of training publishers in content design, and back in May 2025, Nick Daniels, Katie Spearman and Mel Batcharj from the UX team came up with a series of steps to ensure they could access the submitted workbooks and mark them, to provider the learners with feedback on their work:

• Receive the submitted workbooks from the EDC course to a Microsoft OneDrive via a Microsoft Form
• Allocate them to be marked by members of the UX team using a Microsoft Excel spreadsheet
• Once marking is complete, the marker returns the workbook with feedback to the learner via email

A year after launch, we observed some kinks in the process

With a steady influx of workbooks from learners, the process worked well, with Nick, Katie and Mel splitting the marking and feedback provision between them. That said, when there were spikes of increased numbers of learners completing the course, prompted for example by reminders to complete it to gain web editing access, the process revealed itself to have some areas of inefficiency. Working as a team, we took some time to map out the existing process in granular detail to pinpoint some areas for improvement, detailed below.

Keeping track of submissions involved manual additions to a spreadsheet

An Excel spreadsheet was set up to keep a record of all the workbook submissions along with their marking history. This spreadsheet was in a different location to the OneDrive where the workbooks were received, however, therefore it was necessary to copy and paste the names and details of learners into the spreadsheet each time a submission was received, to keep it up-to-date. On occasion, this had meant that the Excel spreadsheet and the OneDrive were out of synch – with workbooks to be marked in the OneDrive that hadn’t yet been logged on the spreadsheet.

Notification of submissions came via individual emails and were easy to miss

When a learner submitted a workbook having completed the EDC course, a notification was sent to Nick, Katie and Mel’s email accounts from a Microsoft Forms account email address. These emails could sometimes be overlooked as they existed alongside other emails in individual inboxes, meaning the step to log the submissions in the Excel spreadsheet was delayed.

Returning marked workbooks from individual email accounts made it tricky to keep track

When marking of a workbook was complete, the marker (either Nick, Katie or Mel) composed an email to the learner with the marked workbook as an attachment. In some cases, learners replied directly to Nick, Katie or Mel either with comments in response to their workbook or with feedback on the EDC course or process. As a team, it was helpful to keep track of these interactions with learners as they were a valuable source of feedback, but this was difficult to achieve in a streamlined way since the responses were held in individual email accounts.

There wasn’t an easy way for the team to share marking and feedback approaches

As they marked more and more workbooks, Nick, Katie and Mel developed more and more efficient ways of handling workbook marking and feedback issuing. They shared best practices through meetings and calls but it was clunky to keep track of the tips and techniques they had found since the marked workbooks were passing through individual email accounts.

Issuing digital badges required learners’ UUNs which needed to be manually extracted

Once the marking was complete and the feedback issued, the final step was to issue an EDC digital badge to the learner. This process was managed by the UX team using the learner’s email address to assign the badge. If the learner had submitted the workbook using their alias email address, there was an additional step for the marker to find their UUN email address in order to award them their badge.

We identified ways we wanted to automate and streamline the process

Since the end-to-end process was handled entirely by Microsoft products, using learner data available in the same system, we felt that it should be possible to streamline and automate certain aspects of it. We mapped out a wish-list of areas to improve, largely focused on alleviating active effort required from the team, and on automating aspects of the process that were prone to human error associated with the manual data handling. These were as follows:

• Workbook submissions automatically dropping into a central location to be marked without the need to log them in a separate spreadsheet
• Correspondence regarding workbook submissions (including notifications, sending out marked workbooks and emailed feedback responses) centrally handled through a single, universally accessed account
• Learner data associated with workbook submissions automatically formatted to facilitate returning marks and feedback and issuing digital badges

With help from the SharePoint Solutions team, we were able to make improvements

Several of the UX team had researched the potential of Microsoft’s Power Automate to achieve the identified changes but we had little experience of using this service. We reached out to the SharePoint Solutions team and Richard Sharp, SharePoint Solutions Specialist helped us build a Power Automate flow handling data through a SharePoint site and a central EDC Online Course email account to achieve the automations we had requested, helping us optimise the process.

AI culture prompts us to embrace automation but it starts with reviewing processes

In an age where every day brings a new AI-powered innovation, there’s an inherent urge and temptation to seize opportunities to apply AI to our processes and procedures to free up human time and avoid human error. Identifying such opportunities must start with looking at the processes and procedures in granular detail, however.

In this case, AI intervention wasn’t an appropriate solution to the inefficiency problem, but as it turned out, going through the groundwork mapping out the process was still helpful to spark thinking about another automation mechanism to save our team time and effort.

Going through the EDC marking and feedback process diagnosis made me reflect on the broader value of applying AI thinking as a mindset shift to bring in new ways of thinking to old problems, to effect change making best use of the tools available to us.

AI tools to support content tasks are becoming more and more widespread. As part of my contributions to open-source Drupal I’ve been researching how to prepare and package content design and style rules that these tools can use effectively.

Using AI to help with content design tasks (and indeed, any other type of tasks) is now standard practice for many. As anyone who has experimented with it knows, the value the AI can provide is largely dependent on the prompt it receives, and the contextual data and information it is given to work with.

The way you provide your chosen AI with context affects the results you get

I have encountered two main methods for providing AI with context data. The first is the ‘prompt and pray’ approach, where you start with vague instructions and then engage in back-and-forth to drip-feed the AI the necessary information in the conversational turns. The second is the ‘context engineering’ approach, where you front-load information to proactive guide the AI, for example, defining its role, setting explicit goals, providing background and adding contextual constraints and rules.

Context engineering is more proactive than prompt and pray

Prompt and pray is better suited to a standard LLM interface or chat assistant (like ELM or Claude), and is the preferred choice when you’re looking to brainstorm and experiment, and are happy to take variable outputs. Context engineering relies on a more structured interface, designed to handle workflows (like Claude Skills) and is therefore the best choice if you’re looking for consistent, predictable outputs within set parameters, but it requires you to prepare your context data upfront, and for your chosen AI mechanism to have a way of handling it.

The Drupal Context Control Center is a management system for context data

Context data is a term given to the structured, site-specific knowledge – such as brand guidelines, regulatory requirements, editorial rules, and terminology – that tells an AI how you want it to work, to ensure its outputs reflect your actual preferences and standards rather than generic defaults.

In January 2025, our team was fortunate to spend a day of Drupal AI experimentation with specialist company Freely Give, in which we applied AI solutions to some of the web content management and design problems faced by the University. One of our experiments involved prototyping a basic style-guide checker into EdWeb2.

Read about this early experiment in my blog post:

An automated Editorial Style Guide? Experimenting with Drupal AI Automators

Looking back, this experiment was a very basic form of context handling, and since then, following the launch of the Drupal AI Initiative in June 2025, there have been rapid developments in thinking about AI and context. The Drupal Context Control Center (CCC) was initiated specifically for the purpose of enabling AI to handle context data and I am proud to have worked on its design, collaborating with Kristen Pol, Aidan Foster and others from the AI Initiative.

Read about the Context Control Centre on its project page on Drupal.org

To design the CCC architecture we brainstormed context application scenarios

Starting with a blank page, we started trying to come up with the main building blocks that the CC needed to have, but we found it very difficult. Drupal is phenomenally flexible which meant every function we wanted could be achieved in various ways. We quickly moved from thinking in the abstract to considering real-life scenarios when people would want to use AI to make use of context data to control what happened with their content which helped us tease out the tasks we wanted the CCC to support.

An example scenario was as follows:

Content creator wants to refresh a prospective student recruitment campaign. They ask the AI, integrated in the content management system, to help. Acting on the instructions, the AI prepares a sample campaign page which includes a slogan that doesn’t match the voice and tone and contains images from winter when the campaign is for summer. The content creator rectifies this by providing the voice and tone guidelines and updated imagery. The AI tries again, it’s better but this time there’s a word spelled differently from the style guide. The creator uploads the style guide rules. The page looks good, but the content creator wants another version to compare. The AI comes up with a version 2. The content creator wants to track interactions with the first version and swap to the second based on performance. To do this they connect their Google Analytics Acquisition reports and give the AI instructions of the thresholds to look for, to initiate the change.

We defined building blocks of the CCC and the relationships between them

Considering what would be needed to support the scenarios we came up with, we identified that the CCC needed to house several types of data and we thought about the dependencies and interactions the CCC would need to support.

1.     Data items that set out ‘the What’

First and foremost, the CCC needed to include the context items (rules and guidelines) such as:

• Brand guidelines
• Voice and tone rules
• SEO keywords
• Campaign or project briefs
• Style guide
• Image bank
• Pattern library

In addition, it needed to contain subcontext items (more refined versions or child/subsets of the context items) such as:

• New product brand guidelines
• Seasonal campaign copy
• Seasonal image set
• Landing page pattern set

2.     Data that set out ‘the When and How’

As well as the various sorts of rules and guidelines, the CCC also needed to include details of the circumstances and situations in which to apply them. Collectively, these were called the context scopes and they mapped out boundaries and constraints such as:

• Global (site-wide)
• Site sections
• Content types
• Use cases (for example – writing teaser copy, working with images)

3.     Mechanisms that control how agents select context

Connecting the ‘what’ with the ‘when and how’ relied on AI agents identifying context scopes and selecting appropriate context items to apply. For this to occur successfully, the CCC needed to include some means of facilitating as well as guardrails to steer suitable choices. These included:

• Context limits (limiters on numbers of context items and tokens)
• Scope subscriptions (agent configurations to define opt-in or opt-out)
• Target entities (specified content entities with context built-in)

We tried out terms in practice before deciding on labels

Labelling parts of interfaces is always tricky, particularly within Drupal where certain words have legacy meanings and, owing to its flexibility the functionality associated with terms may change over time. It was especially important to make careful label choices for the different parts of the CCC architecture, given its anticipated universality.

To inform my work on the CCC I decided to re-read ‘Design by Definition’ by Elizabeth McGuane, which book sets out and appraises a range of approaches for deciding upon terminology, labels and names for objects.

When choosing a name to fit a system, the author recommends appraising the options against three criteria:

• Novelty – how standard or unique the name needs to be
• Flexibility/mutability – how well the name works in different contexts
• Memorability – how easily the name is recalled

Applying this idiom helped us decide upon several key CCC terms, including:

Context item: any piece of information fed into an AI powered mechanism (typically an agent) to help the AI’s working memory to produce responses and outputs that are accurate and tailored to the users’ requests.

Context source: origin of any piece of information fed into an AI powered mechanism (typically an agent) to help the AI’s working memory to produce responses and outputs that are accurate and tailored to the users’ requests.

For the CCC to work as intended, inputs need to be machine-readable

With key parts of the CCC defined and the architecture mapped out, we started to consider how well the CCC could handle variations of context data loaded into it. This required us to think more broadly about how AI makes sense of information.

In the excellent book ‘Machine customers: The evolution has begun’ by Katya Forbes, the author outlines many use cases where people enlist AI agents to take care of tasks for them and diagnoses the underlying factors necessary for the AI to satisfactorily work on behalf of the people concerned.

In one such use case she breaks down the stages required for a human to take a purchasing decision compared to a machine.

For a human to decide on a purchase, they go through the following stages:

1. Awareness – ‘I have a problem’
2. Interest – ‘This might solve it’
3. Consideration – ‘Let me evaluate my options cognitively and emotionally’
4. Intent – ‘I’m leaning toward this choice’
5. Purchase – ‘This feels right’

For a machine to decide, the stages are different:

1. Query initialisation – parameters received ready to begin search
2. Discovery – options identified that meet basic criteria
3. Evaluation – comparing options against weighted parameters
4. Verification – validating performance claims and reliability
5. Selection – optimal choice identified based on data

From this comparison use case, it is clear that designing contextual data such as guidance documents to be used by AI requires a different approach to designing content to be read by humans. In particular, writing guidance information that relies on sentiment and/or subjective interpretation will be lost on AI and therefore not applied in the ways intended.

In its current form, our Editorial Style Guide is only partially AI-ready

Last year, in the early days with ELM, members of the UX Service tested ELM’s ability to apply the rules of the style guide to check content. They found it had limited success and found that in some cases, ELM applied its own rules to checking the content which couldn’t be traced to the style guide. Reviewing this experiment in light of the machine customer journey, it becomes clear that for an AI like ELM to faithfully and consistently apply style guide rules, the rules need to be written in a way that demands minimal interpretation, in other words, a deterministic way.

Read John Wilson’s blog about experimenting with ELM and the style guide:

Testing ELM’s ability to return useful results with prompts about the Editorial Style Guide

Adopting a ‘machine-first’ lens to the Editorial Style Guide to analyse content in selected sections, it is possible to pull out some of the deterministic (automatable) rules to compare with non-deterministic (requiring judgement) ones.

Analysis revealed deterministic and non-deterministic rules in the Editorial Style Guide

I completed a quick review of three sections of the style guide to assess how AI-ready they were.

1. In the Headings and page titles section

Deterministic rules:

• Do not skip heading levels
• Do not use H5 or H6 headings (maximum four levels)

Non-deterministic rules:

• Use word and language your users will be looking for and familiar with
• Write descriptive headings – avoid vague words

2. In the Acronyms and abbreviations section

Deterministic rules:

• Do not put full stops or spaces between letters of an acronym
• Do not abbreviate Professor to Prof
• Do not use eg, ie or etc

Non-deterministic rules:

• Spell out acronyms multiple times on long pages or pages with accordions
• Well-known acronyms don’t need spelling out
• Use abbreviations only when better known than the full version, or when space is limited

3. In the Links section

Deterministic rules:

• Do not use a URL as link text
• Do not use ‘click here’, ‘more information’, ‘learn more’ as link text
• Put links on a new line, not inline in a sentence

Non-deterministic rules:

• Add details about what a link will do (open in a new tab, require a University login) where relevant
• Avoid duplicating links where you can
• Reserve button styling for the most important links

Going through this short exercise with our style guide established that before we can make valid use of AI-powered features like the CCC, there is preparatory groundwork required to some of our content design guidance and rules more AI-readable, and potentially to create a new machine-readable version of the Editorial Style Guide.

I’m looking forward to investigating the potential for the CCC at the University

My work with others in the Drupal community on the CCC has led to a successful release of a beta version, with a release candidate coming soon, planned alongside the regular Drupal AI module release cycle.

Building on our early AI experiments with the style guide, I am keen to lead the UX team to explore the CCC’s potential to handle and apply editorial rules within a Drupal-powered CMS like EdWeb2. Our previous research with web publishers tells us that applying the style guide consistently can be genuinely difficult, and as AI-powered content design tools like the CCC continue to emerge, there is real opportunity to put this to work on the challenges our publishers face.

Read about our previous research with publishers about using the Editorial Style Guide

An analysis of responses to our Editorial Style Guide survey by Hannah Watson

Usability testing the Editorial Style Guide site by me in my previous Content Designer role

Full automation of good content design is unlikely and undesirable but it may be possible to offload some of the more straightforward rules to an AI mechanism like the CCC, freeing publishers to focus their attention on the trickier aspects of content preparation. Taking a machine-assisted view will also provide the chance to revisit existing non-deterministic rules to assess whether some could be made more granular, broken into sub-contexts or context scopes for more precise application.

In line with our broader aim of improving the tools available to content publishers, I am excited to see what the CCC can offer for real-world content design challenges at the University.

 

Progressive thinking about inclusive content combined with a review of our content design tools prompted us to look at the effectiveness of our Inclusive Language Guide. Before we could think about improving the guide, however, we needed to ensure staff knew it existed.

Since the relaunch of our Effective Digital Content course last year, and following a succession of content improvement club sessions regularly attended by web publishers and content creators, it’s been gratifying to see more and more University staff learning about content design and putting theory into practice. The raised awareness and interest gave the UX team cause to review the guidance we direct staff to follow, to check that it’s as clear and easy to apply as possible.

The Inclusive Language Guide (ILG) was published In June 2022, following months of careful research, investigation and analysis by Ari Cass-Maran, former Senior Content Designer of the UX Service. Nearly 4 years on, amidst increasing UX and content design maturity, it was timely to revisit the guide with a view to improve it. Before we could think about improvements, however, we firstly needed to appraise how well the guide was known and being used by University staff.

Read more about the origins and publication history of the Inclusive Language Guide in Ari’s blog posts:

Inclusive Language Guide

Inclusive Language Guide: how we co-designed with our community as part of a human-centred Design System

Inclusive language awareness and practices have advanced since we published our guide

Since 2022, there have been many positive developments in inclusive language practices and approaches in the public sector and beyond. In November 2023, members of the Home Office Digital team created inclusive language guidance as part of their goal to embed diversity and inclusion into their research and design practices. In September 2024, the Department for Education published the first release of an Accessibility and inclusive design manual and initiated research to learn how easily users could find information within it, with a view to making improvements for a second release. Further afield, in 2024, the Council of Europe published inclusive language guidelines and the European Institute for Gender Equality published ‘Words Matter’ a guide and associated toolkit designed to encourage more gender inclusive language practices. Content-design publications like ‘Considerate Content’ by Rebekah Barry and ‘Designed with Care: Creating Trauma-informed content’ by Rachel Evans and contributors helped shed light on practical ways to prepare content in more sensitive ways, for example, taking into account needs associated with neurodivergent conditions and needs triggered by previous difficult experiences. On a more conceptual level, Karen Yin’s book ‘The Conscious Style Guide: A Flexible Approach to Language that includes, respects and empowers’ prompted a philosophical approach to inclusive language use, going beyond lists of ‘do’s and don’ts’, instead, calling for engagement with changing cultural norms to make decisions around language use within a framework guided by content, context, consequence, complexity and compassion.

Read about some of the developments in inclusive language practices:

Conscious Style Guide website

Inclusive language by design, published 22 November 2023 on the Home Office Digital blog

Accessibility and inclusive design manual blog, published 29 October 2024 on the GOV.UK website

Guidelines for the use of language as a driver of inclusivity by the Council of Europe

Words Matter: Supporting Gender Equality Through Language and Communication, published by the European Institute for Gender Equality

Before we could make improvements, we needed to understand the guide’s current use

Working in UX, with its overarching aim of making things better for people, it can be tempting to track developments in user-centred design and apply emerging thinking directly to the work in front of you – in this case, the iteration of our Inclusive Language Guide. But to make changes that are meaningfully impactful, it is often better to pause first and understand the current state in order to plan accordingly. In this instance, that meant taking time to assess how our existing guide was performing – to find out whether it was known, adopted, and being applied by  University staff with content publishing responsibilities.

Appraising the guide’s effectiveness involved assessing receptiveness, discoverability and findability

We weren’t certain whether staff knew about the Inclusive Language Guide, let alone whether they were using it. In order to find this out, we identified two broad categories of behaviour that would indicate successful engagement with the guide.

The first related to receptiveness – did staff realise the need to write inclusively, or recognise that inclusive language was something they needed to think about at all? , in other words,  that inclusive language was’a thing’?

The second related to information-seeking- once staff had identified the need, how did they go about addressing it? The answer depended on whether the guide was findable (for those who knew it existed) or discoverable (for those who didn’t).

These behaviours mapped neatly onto a framework from an old but still salient blog post from 2006, in which Donna Spencer wrote about four modes of information-seeking:

• Known item seeking – looking for something specific you know exists
• Exploratory seeking – browsing when you have a general sense of what you need
• Don’t know what they need to know seeking – no clear awareness of the gap or what would fill it
• Re-finding seeking – locating something you’ve accessed before

Four modes of Seeking Information and How to Design for Them (Boxes and Arrows blog)

When it came to the Inclusive Language Guide, the staff who already knew it existed would draw on known item seeking or re-finding behaviours, and if those succeeded, the guide could be considered findable. Those who had identified a need but didn’t know the guide existed would rely on more exploratory methods, and if those succeeded, it could be considered discoverable. Our research needed to probe for all of these.

We developed a scenario-based test to understand perceptions and expectations around inclusive language

Before beginning any piece of research, the UX team thinks carefully about what we want to learn, framing our intentions as explicit research questions. Working together, Mel Bacharj, Content Design Assistant, and I identified three core questions to guide our research:

• Do staff know the guide exists and the type of guidance it includes?
• Do they know what it can help them with?
• Can staff recognise situations where it would be useful to them?

Drawing on UX and product literature, we identified a scenario-based approach as the right method for testing whether staff felt a genuine need for the guide. Rather than asking directly about the guide itself, we would present participants with a content preparation scenario where the guide’s rules could apply, and observe whether they recognised the need unprompted. This approach is grounded in the principle of ‘talk about their world instead of your idea – described by Rob Fitzpatrick in the book ‘The Mom Test: How to talk to customers and learn if your business is a good idea when everyone is lying to you’ – which cautions against asking people about a product or tool directly, since doing so tends to invite polite, unhelpful answers.

From there, the research followed a logical sequence. If participants identified a need for guidance on writing inclusively, the next stage would ask how they would naturally go about finding that information – testing whether the guide was discoverable. If they located it, the final stage would assess whether they could find relevant guidance within it to help them write inclusively in the content preparation scenario we had tasked them with.

Based on initial findings, we’ve improved the guide’s visibility by adding it to key websites

Following our research plan we conducted testing with staff volunteers and the results were insightful – revealing staffs’ current perceptions, expectations and understanding of inclusive writing practices. Further, more detailed analysis will follow, however, having watched participants search for inclusive language guidance, an primary research finding was that the guide was not that easy to find.

We wanted to act upon this finding as soon as possible, so we reached out to relevant site owners and have now successfully added a link to the guide from the following webpages:

Disability Information | Help | Information Services – Linked from the question: ‘How do I make sure I use the right words to write about disabilities and disabled people?’

Helpful links | Help | Information Services – Added to the list of resources

The Social Model of Disability | Health & Safety | Health and Safety Department – Linked from a paragraph ‘The University’s Inclusive Language Guide contains advice about how to write about disabilities and disabled people’.

Staff EDI Learning | Equality, Diversity & Inclusion | Equality, Diversity and Inclusion – Linked from the Available Learning section

Further Reading and Learning | Equality, Diversity & Inclusion | Equality, Diversity and Inclusion – Added to the list of resources

Resources – by topic | Institute for Academic Development | Institute for Academic Development– Added to the list of resources

With the guide linked from more places, we are in a better place to work on improving its content, to ensure it can be found and used more effectively by those preparing content for the University.

A question has been doing the rounds recently: do we actually need site search?

It’s a fair question and one I have been giving a lot of thought to. With AI-powered summaries increasingly answering queries before users even reach a website, and with navigation that, when it works, can get people where they need to go, it’s reasonable to ask whether a search box is still earning its place.

I want to make the case that not only do we still need it, but that there is more we could be doing to hear what it is telling us, and that in a changing web landscape, the stakes of getting this right are higher than they might appear. This is my take on that question.

The visitors who remain are asking harder questions

AI-powered search, Google’s AI Overviews, Bing’s Copilot, and a growing ecosystem of assistants are increasingly handling the easy, surface-level queries. What are the entry requirements? Where is the main library? When does the term start? Users may well be getting their answers without ever clicking through to a website.

The people who do arrive are doing something more complex. They’re navigating nuance, completing a task, or looking for something specific that a summary couldn’t resolve. They are, almost by definition, higher-intent users, and they are maybe more likely to reach for the site search to find what they need.

This is where the opportunity lives. But there’s a subtler risk worth naming first.

We don’t control what AI says about us, but we control what happens when someone arrives

When a user asks “how much does it cost to study at the University of Edinburgh?” in Google or Bing, the AI summary doesn’t necessarily draw from our content. It synthesises from whatever it finds, and that might include a comparison page from a competitor institution that frames us as the expensive option, or an aggregator working from outdated figures. The user absorbs that framing before they’ve visited us at all.

This matters because users arrive with expectations already shaped. If our site then delivers a confusing, hard-to-navigate experience that doesn’t quickly surface authoritative answers to the questions they came with, we’ve failed twice: once in the AI layer we don’t control, and once on our own platform where we do control the content.

A strong on-site search experience is part of the answer. When users can quickly find accurate, up-to-date information on our platform, in our voice, with our context, we’re not just serving them better. We’re giving them a reason to trust our content over whatever summary brought them here. That matters especially for high-stakes queries around fees, entry requirements, and outcomes, where a third-party framing in an AI summary could genuinely influence a decision.

But here’s the question I keep coming back to: how would we know whether we’re actually delivering that? How confident are we that when someone arrives and searches for fee information, or scholarship options, or how to apply, they’re getting a result that reflects our best, most accurate content and not something buried, outdated, or missing entirely?

That’s where site search starts to feel like something more than a navigation tool.

Site search as a content performance monitor

A search tool you control gives you a feedback loop that no external analytics can replicate. Search logs tell you what people came looking for and couldn’t find through your navigation or from an AI summary. That’s not just useful data, it’s a content audit running continuously, written by your users.

Queries with no good results point towards content gaps. Repeated searches for the same thing might signal a labelling or findability problem. High search volume on a topic you thought was well-covered could mean the content exists but isn’t structured in a way that surfaces it.

Unlike external analytics, which tells you what happened, site search logs can tell you why: what someone was trying to do when they gave up, clicked away, or drilled deeper.

Interrogating both sides of the search

The real power, as I see it, comes from owning the full picture: what goes in, and what comes out.

On the input side, you have user queries, unfiltered, unsanitised, and often surprisingly candid about what your content is missing or getting wrong.

On the output side, you have the results your search returns: which content is being surfaced, how confidently, and whether it’s actually relevant. A query returning weak or irrelevant results is a signal. A query returning nothing is a louder one.

When you can interrogate both ends of that pipeline, you can start to close the loop. You can identify underperforming content before a user gives up on it. You can spot where your taxonomy doesn’t match how people actually talk about things. You can track whether content improvements change what gets returned for a given query. And critically, you can start to test whether your most important content is performing as you’d hope, including the answers to the questions AI is already being asked about you.

This feels like a feedback mechanism that no external tool can give you, grounded in what your users searched for, on your platform, against your content.

There is an argument that site search itself could go further, using ELM to surface AI-generated summaries grounded in our own content, rather than leaving that layer entirely to Google and Bing. But that is a conversation for another post.

Content quality sits at the foundation

Site search can surface problems, but fixing them is a separate conversation, one about content ownership, editorial process, and where responsibility sits. What site search data can change is the evidence base for that conversation. Instead of relying on assumptions about what content is needed, or waiting for user feedback to trickle in, there’s a continuous signal available.

Good titles, clear headings, accurate metadata, and well-structured content aren’t just best practices. They’re what make that signal readable. The better the content is structured, the more faithfully a search tool can reflect what’s actually there, and the more useful its logs become as a diagnostic. It also stands to reason that when AI systems draw on that content, they’re drawing on something accurate and well-framed, rather than leaving the field open to whoever has structured their content better.

Rethinking what success looks like

If AI is handling the top of the funnel, raw session volumes seem to me to be an increasingly unreliable measure of whether a web presence is doing its job. Site search offers a different kind of evidence: did people find what they were looking for? What were they looking for that wasn’t there? Where did the content let them down?

These feel closer to the questions that actually matter, and a well-instrumented site search can start to surface them.

Back to the question

So, do we need site search?

My view is yes, but perhaps not only for the reason you might expect. It’s one of the few tools we control that can tell us, in our users’ own words, what our content is and isn’t doing. When users arrive, already primed by AI summaries we had no hand in, it’s often the fastest route to the authoritative answer we’d want them to find. And without it, we’re largely guessing whether our most important content is performing as we’d intend.

In a web landscape where external signals are becoming less reliable, that feedback loop seems more valuable, not less.

The question isn’t whether we need it. It’s whether we’re actually listening to what it’s telling us.

Last month I was honoured to receive a national award for Outstanding Leadership from industry body UCISA, recognising my work driving positive change through UX. This achievement prompted me to reflect on my experiences leading UX in different realms over the past few years, and to think about what UX leadership means to me.

At the start of 2025 I was asked to step up to a senior leadership position within the global open-source Drupal community. Drupal, the primary content management system used by the University, has a long-standing reputation for being developer-centric and, coinciding with the launch of a new low-code site-building product, they needed help to steer it towards non-technical audiences.

Without thinking too hard, I jumped at the opportunity. I knew it wouldn’t be easy but I was motivated by the chance to use UX as a force for positive change. I didn’t really have much of a plan, but I was confident I could draw on the UX knowledge and experience I had and figure out ways to make things better. Looking back, this represented a milestone in my UX leadership journey and my broader approach to leadership.

I took over running the University UX Service in October 2022. At the start of 2023, I was a UX team of one and I needed to recruit some team members to rebuild and re-establish the service. I was fortunate to be able to bring in some amazingly talented people to work in my team and with their help, and with some successes, failures and near-misses along the way, was able to transform the UX Service into what it is now – a thriving unit delivering sustained value for the University, one improved digital experience at a time. With that behind me, at the start of 2025, I was open to new opportunities to stretch my leadership capabilities. I had accepted that I would get things wrong before I got them right, but I was drawn for the associated learning, which I recognised would help make me become a better all-round leader.

As it turns out, 2025 became quite the year. I won the Women in Drupal Define Award (in October 2025), and the inaugural UCISA Outstanding Leadership award (in March 2026). Taking on the challenge of leading UX in Drupal paid off in more ways than I could have anticipated. Seeing my work bring about positive UX change in Drupal provided me with a renewed sense of purpose in my job leading the University UX Service, as well as in my roles running the UCISA UX Group and contributing to W3C. I’ve boiled down my thoughts on what successful UX leadership looks like for me into three reflections which I’ve turned into action points to take forward as I progress in UX leadership.

UX needs an adaptive leadership approach, and you always need to promote UX value

When I run brainstorms for UX events with the UCISA UX Group committee, ‘Getting buy-in for UX’ is a topic that regularly comes up. How do we get senior decision-makers to invest in UX? Surely making digital services, products and systems more user-centred is something everyone wants?

Well, yes, but it’s complicated. UX can be disruptive. UX research reveals problems, and once problems are unearthed, there’s an obligation to address them, and that requires time, resource and effort which may not be readily available.

Keeping this in mind helps me shape my UX leadership approach. When I’m contributing to Drupal, I take into account that Drupal is a volatile, fast-moving, open-source community, where new ways of thinking and novel ideas are the norm. There is room for UX amongst the many other forces driving change. The Drupal community has autonomy to make changes and is a solutions-powerhouse, ready to react and respond to the needs and demands arising from UX research. Taken together, this mean I can effectively lead UX in Drupal by presenting and talking about UX on a conceptual level, conducting UX research and openly sharing the findings to prompt and rally the community around making user-centred changes.

Leading UX in the public-sector context of the University context needs to be handled differently. Before initiating any UX research activity, I take time to understand the nuances of a situation, to anticipate the context-specific value UX may bring, and honestly assess the resource and commitment required to achieve that value.

The UX Service receives many requests for UX help, but a fraction of the requests we receive are not progressed, meaning digital experiences that could be improved are left unchanged. I recently led a retrospective with my UX team to understand reasons why, and to look for patterns. We concluded that in a number of cases, there is a gulf between people’s expectations of what UX improvement involves and the reality of making improvements happen. This gap is often what causes teams to abandon their UX plans.

This insight shapes how I lead UX at the University. Rather than describing it in purely conceptual terms, I make a point of bringing teams into the practical reality – the sometimes messy, non-linear steps required to achieve improved UX. Leaning on  the persuasive power of word-of-mouth, myself and my UX team take every opportunity to cite and promote successful case studies and testimonials from teams we’ve worked with, demonstrating the real-world value UX brings, and praising teams that choose to apply agency and sustained will to make things better, acting on what they’ve learned from user research.

If you’re going to be a good UX leader, you need to be worth following

As part of her talk at the UCISA Leadership Conference in March 2024, the inspirational rugby player Maggie Alphonsi shared a short video of a person dancing alone at an outdoor event to make a point about leaders and followers. As the seconds ticked by, another person joined the first dancer, and then another and another until a crowd formed. I reflected that being a leader (akin to being the first dancer) is a lonely existence unless people follow you, and that doesn’t come as a given, it all depends on your actions and the impression people have of you, and whether they can meaningfully relate to you.

Since UX is universally applicable, leading it effectively requires more than UX expertise alone. In the service dominant logic model, value only emerges when operant skills (in this case, UX skills and techniques) are applied to operand resources (digital experiences to be improved) through genuine partnerships. Successful partnerships depend on trust and shared understanding, which in turn rest on contextual knowledge and empathy. To lead meaningful UX improvements, I need to retain a constant understanding of what good digital experiences look like, and that means stepping out of the UX bubble to engage with emergent technologies, developing standards, market forces and digital trends.

Distilling down the focus of UX down to ‘making digital experiences better’ leading UX carries a perpetual invitation to innovate, and to seek creative ways of addressing problems. I approach this with a magpie’s mentality – actively scanning to learn about anything that might help me do my job better, whether that’s a change framework, a data modelling tool, an assessment approach. My radar is deliberately wide and I opt not to stay in my lane, always aspiring to grow my sphere of knowledge, influence and connections, motivated by the learning rewards and staying true to the mantra: ‘to get different results, do things differently’.

The breadth of that knowledge pays dividends. As a UX leader, stepping outside familiar territory and taking a genuine interest in different fields puts me in a stronger position to build partnerships across a wide range of spheres — and to respond intelligently to the UX challenges that arise within them. Technology is constantly evolving, as are human needs from it. So I never turn down an opportunity to learn. Furthermore, adopting a humble position of learner means I can absorb knowledge freely, without the constraints of defending expertise I’ve already declared.

Managing and growing partnerships, is of course, just one dimension of UX leadership. In his Driesnote recorded at DrupalCon Chicago 2026, highly-respected Drupal leader (and all-round duderocker and legend) Dries Buytaert reflected on his own leadership journey, describing how his drive and ambition for building and growing open-source Drupal led to him becoming an ‘accidental leader’.  I reasoned that, being an effective leader needs an inherent passion, goal and inner sense of worth, which emulates to others like the energy from the lone dancer, drawing them to follow.

As a UX leader, you’re responsible for shaping future UX leaders

I take my leadership roles very seriously, and I am committed to using my position as a platform to support and encourage widespread adoption of UX approaches and practices. My goal as a UX leader is not just to convince people of UX’s value, but to inspire them to practise it and learn for themselves.

When I was invited as a guest lecturer to speak about running the University’s UX Service to students from the University of Edinburgh Business School, I emphasised the time and effort I devote to building operational capability – in other words, thinking bout the future, and pre-empting needs and demand for improved digital services and developing a strategy to address these in a sustainable way.

A core part of my UX Service strategy is a UX coaching model, built in response to a clear reality – there will always be more user experiences to improve than there are UX professionals to improve them. Coaching others in UX techniques and approaches is an effective way to address the imbalance. When colleagues have the ability to carry out UX research to diagnose UX problems themselves and then apply UX techniques to address them, it’s a win-win. Not only are more  digital experiences improved, problems are caught earlier and UX becomes firmly on the radar.  Democratising UX skills through coaching embeds UX capability, driving a future-state mindset where UX is part of normal software and digital management processes and procedures across the institution.

As well as converting colleagues to the UX cause, there’s the next generations of UX leaders to consider. In summer 2026, the UX Service will continue working with interns and we will grow the number of student workers in our team from three to four. As in previous years, I am excited for the opportunity to support and learn from these emerging UX leaders, to encourage them to bring  their new ideas, provocations and thoughts on how we can do better. Because when it comes to UX leadership, there are always ways to do things better and therefore continual opportunity for motivation and drive.

Here are some photos of my UCISA award and me

I was unable to attend the UCISA Leadership Summit when my award was presented, but it was transported back to me to enjoy and celebrate after the event.

 

After Stratos’s blog post last week, I revisited Joshua Mitchell’s experiment asking an AI to simulate what using a screen reader actually feels like, not to replace proper testing, but to generate a transcript of the experience that could be shared with stakeholders who had never encountered one. The results were striking: skip links that went nowhere, navigation menus entirely unreachable by keyboard, and link text repeated identically ten times with no distinguishing context.

Stratos’s post asked whether AI is improving or impeding web accessibility, and ended with an open question to the community: are you already using AI in your accessibility workflows?

I’d just come back from DrupalCamp England, where I’d presented a talk called “Same Image, Different Story”, one I first gave at DrupalCamp Scotland, and will be taking to Drupal Dev Days later this year. It’s a talk I’m deliberately treating as a work in progress, updating it with new thinking and developments each time rather than delivering the same version twice.

And I think I might have part of an answer to Stratos’s question, though it’s more of a diagnosis than a solution, at least for now.

The problem hiding in plain sight

Harvard’s Digital Accessibility Services has a useful guide on writing alt text that includes a section called “Consider the Context.” It shows the same photograph of Hollis Hall used in two different articles. One is about students enjoying the spring weather, and the other is about the building’s famous residents, and it demonstrates that each use case demands entirely different alt text. Same image, different story.

It’s a compelling illustration of best practice and is the cornerstone of my talk. That single example, two articles, one image, two completely different appropriate descriptions, captures the problem more precisely than any technical explanation I could give. But it also quietly exposes an architectural gap: most content management systems, including Drupal, the platform that powers the University of Edinburgh’s EdWeb 2, don’t give editors anywhere to act on that guidance. The image gets a single alt text field. One description, stored once, is applied everywhere the image is used. An article about student life. A seasonal blog post. A facilities page. The same text, regardless of which detail is editorially significant in each context.

This isn’t a quirk of how one editor set things up. It’s a fundamental constraint of how Drupal’s media architecture works. And the consequences reach further than you might expect.

The anti-pattern that reveals the problem

When Drupal introduced the Media Library, it was framed as a shared asset pool designed to encourage image reuse and reduce duplication, and the intent was good. Upload once, use everywhere. But what we’ve observed in practice is editors quietly working around it: uploading the same image multiple times under different filenames, just so they can have different alt text, or set a different focal point, for different editorial contexts.

The platform designed to reduce duplication is inadvertently encouraging it. That’s a significant signal. When users consistently work around a feature, it usually means the feature doesn’t match how they actually need to work.

Where does AI fit into this?

Stratos’s post noted that AI-generated alt text is improving, but inconsistent, and the W3C’s own work on machine learning accessibility is honest about the gap. A bar chart described as simply “a graph with coloured bars” versus one that explains the data in full is the difference between access and exclusion.

There are already Drupal community contributions that tackle this, and they’re genuinely promising. AI may be able to offer a better first draft of alt text to editors to update manually, especially under time pressure.

But here’s the thing that my talk kept circling back to: even if AI could generate better alt text, Drupal has nowhere contextual to put it.

If the media architecture only supports one alt text value per image, then it doesn’t matter how good the AI generation is. The result still gets flattened to a single description, applied in every context, whether it’s appropriate or not. You haven’t solved the accessibility problem; you’ve just automated the production of the wrong answer, faster.

There’s a further constraint worth naming, too: current AI alt text tools work from the image alone. They don’t read the surrounding page content, the article headline, the body copy, or the editorial context, so they have no way of knowing whether the focus should be the students, the architecture, or the changing seasons. The next step in making this genuinely useful is finding ways to pass that subject matter to the AI, so it can generate alt text that’s not just accurate, but relevant to the specific editorial context it’s being placed in.

A practical step we could take today

There’s something worth drawing from Joshua Mitchell’s experiment here. He didn’t ask AI to fix the screen reader experience; he asked it to describe it, making an abstract problem visible and actionable.

We could apply the same thinking to alt text validation. Rather than waiting for the architecture to catch up, AI could be pointed at an existing page and asked to interrogate it: does this alt text accurately describe the image? Does it make sense in the context of this article? Is it serving the reader, or just technically present?

That’s a use case that’s achievable right now, without any changes to how Drupal stores media. And given that EdWeb serves over 600 subsites with around 1,500 editors, the ability to audit contextual appropriateness at scale, rather than relying on individual editors to self-assess, could make a meaningful difference.

The question I’m sitting with

The more interesting challenge, and the one I’m actively exploring at the University, isn’t “can AI write good alt text?” It’s “can we build an architecture where AI can write the right alt text for a specific editorial context, and can the CMS preserve and serve that appropriately?”

That feels like a genuinely solvable problem. The Drupal community is already moving in the right direction with contributions that allow editors to override media properties per-use rather than per-asset. Pair that with AI-assisted generation, editorial context passed as subject matter, and human review, and you start to have something that could meaningfully improve accessibility at scale, across a platform serving over 160 environments and more than 600 subsites, as EdWeb does.

This is part of a broader piece of work I’m developing at the University around AI editorial assistance, focused not on generating content, but on helping editors make better decisions: style guide compliance, accessibility checking, and contextual awareness. It’s early days, but the alt text problem feels like a good place to start.

To borrow Stratos’s framing: keeping the human in the middle means making sure the human has the right tools to make contextually appropriate decisions, not just faster ones. I’ll be updating this thinking as the talk evolves, and as the work here at Edinburgh develops. If anyone else in the Higher Education community is thinking about this, I’d love to compare notes.