Our previous research found that students often skipped videos on University webpages. This follow-up explores whether audio, timestamped videos or structured transcripts better support students in finding the information they need. 

In June, I wrote a blog post about whether students watched videos on University webpages, describing my research as part of the UX Service. 

Do students actually watch videos on websites? 

As part of that research, we ran a small qualitative study with three student participants, observing their natural behaviour as they encountered video content embedded in a student-facing newsletter from the University. We asked a simple question: when a University-wide communication points students to video content, do they actually watch it? 

Our first case study suggested the answer was often no. Two of the three participants were not engaged by video content, with one actively avoiding it and another being highly selective. In contrast, one participant was receptive to video, likely due to its relevance to his current situation, highlighting the importance of timing and context in determining video engagement. 

This blog post continues where that left off, testing why and what students would rather have instead. 

We investigated student reactions to alternative forms of video content

The obvious next question was what students would choose instead of a video if given the option. We decided to run A/B tests, which are controlled experiments that typically compare multiple versions of a digital interface, for example, a webpage to determine which performs better based on user behaviour. We recruited six participants and showed them the same newsletter in three different formats. 

• Two participants were shown an audio version. 

• Two were shown a video with timestamps. 

• Two were shown a transcript, restructured into digestible text rather than auto-generated wall of text. 

The findings from this short experiment were interesting. We learnt that, of the three alternatives, the audio format was the least well perceived by the participants. The main reason was because an audio version offered no visual elements, no way to scan ahead, no quick way to extract the one piece of information someone actually needed. Even when the participants were explicitly told that audio was lighter in weight and reduced the carbon footprint of the web estate, it was still the least preferred option. 

The video with timestamps on the other hand, was much more positively received. Timestamps allow people jump straight to the relevant section rather than watching linearly, therefore allowing a more efficient way to consume information. 

Of all three alternative formats, the transcript was the clear preference of the participants, since this permitted them to quickly skim through the content and find the key information they were looking for. The transcript participants favoured wasn’t just “text instead of video”,which can often appear as a ‘wall of text’ it was text that had been deliberately restructured to pull out the key takeaway points. Taking these findings together, we learned that, for video transcripts to be easily read, and to ensure people can extract most value from them, it is worth putting effort into structuring and presenting text from transcripts, and not simply relying on the auto-generated ones.  

Our research revealed ways to make video content more impactful and effective

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.

 

Earlier this year, the User Experience (UX) Service researched how colleagues find and use the University’s Inclusive Language Guide. This post explores why we used scenario-based research, what it revealed about colleagues’ behaviour and how the findings are improving the guide. 

The Inclusive Language Guide is only useful if people can find it 

The Inclusive Language Guide sits within the University’s Editorial Style Guide and provides practical guidance when writing about disability, race and ethnicity, and sex, sexuality and gender. The guide was developed in 2022 through a co-design process involving colleagues from across the University.

Inclusive Language Guide 

Having useful guidance is only part of the challenge. Colleagues also need to know it exists and be able to find it when they need it. 

More recently, we reviewed the Inclusive Language Guide to understand how discoverable it was for colleagues creating digital content. Emma Horrell has written about how this work prompted improvements to the guide’s visibility and signposting across the University. 

You don’t know what you don’t know: Improving the way we position inclusive language at the University

Why we used scenario-based research 

We wanted to understand more than whether colleagues were aware of the Inclusive Language Guide. Awareness alone does not tell us what people do when they need support, so we used realistic scenarios to observe how colleagues responded to situations where they might need guidance. 

We focused our research questions on four areas: 

• How colleagues approached decisions about inclusive language. 
• Where colleagues looked for support and whether they could find relevant guidance. 
• Colleagues’ awareness of the Inclusive Language Guide and what they expected it to help with. 
• Any gaps, ambiguities or areas of confusion. 

These questions shaped the structure of the interview and activities we designed. 

Researching colleagues approach to inclusive design decisions 

We combined semi-structured interviews with scenario-based activities to understand both colleagues’ existing experiences and how they approached real content decisions. 

We spoke to nine colleagues who create digital content 

We carried out nine interviews with colleagues in different roles across the University. Participants had a range of experiences creating digital content, including two student interns who brought perspectives of those newer to the University’s digital landscape. 

We began by exploring participants’ existing experiences 

Before looking at the scenarios or the guide itself, we asked participants about the types of content they create and whether they had ever needed to check terminology or wording before publishing. For example, we asked whether they had been in a position where they needed to consider how to word something to make sure the language was not excluding anyone. 

This helped us understand the approaches colleagues already used and the sources they relied on when dealing with these topics. 

Realistic scenarios showed how colleagues look for guidance 

We introduced a series of realistic scenarios based on everyday content tasks. 

Rather than asking whether participants knew about the guide, the scenarios allowed us to observe how they approached these situations in practice.

Example scenarios included: 

• writing promotional content about a Pride initiative and wanting to ensure the language used was fair and respectful 
• writing about someone who had experienced trauma and wanting to avoid misrepresenting them 
• checking the preferred terminology for the chair of a company board before publishing a news article 

In posing the scenarios, we gained insight into where participants would usually look for this type of guidance, how confident they felt making language decisions and what they understood by the term ‘inclusive language’.

We adapted our scenarios as we learned more 

One of the original scenarios asked participants how they would write about someone elected to lead a company board. Rather than focusing on terminology, several participants described how they would research the individual or organisation before publishing. 

While this reflected realistic editorial practice, it did not help us answer the research question we were exploring. 

For later sessions, we refined the scenario so that it focused specifically on checking the preferred terminology for the role before publication. This encouraged participants to think about language choices and produced richer insights into where they expected to find guidance. 

What we learned: Four themes around finding and using inclusive language guidance

Across interviews, there were four themes that emerged consistently. 

Colleagues valued inclusive language even when they had not heard of the guide 

Across the interviews, participants consistently demonstrated that they valued using respectful and inclusive language. They were already thinking carefully about inclusive language, but they did not always know where to access University specific guidance.  

Many described that they would check terminology with colleagues or seek advice from specialist teams when writing about unfamiliar topics. They wanted confidence that they were using appropriate language. 

Awareness of the importance of inclusive language was much higher than awareness of the Inclusive Language Guide itself. 

Colleagues expected to find guidance through different routes 

The scenario-based activities helped us understand where colleagues naturally looked for support. 

Participants frequently described turning to colleagues, managers, Equality, Diversity and Inclusion (EDI) resources, staff networks and external websites. A few participants did eventually land on the guide, but they had not recalled it or looked there first. 

Several participants also relied on previous examples when writing content. Others spoke about balancing official guidance with lived experience and perspectives from the communities being represented. 

This highlighted an important consideration. Colleagues expected inclusive language guidance to be connected to the places they already look for support, linked from elsewhere rather than existing solely within the Editorial Style Guide.  

Improving signposting therefore remains just as important as the guide itself. 

Colleagues understood the phrase ‘inclusive language’ in different ways 

Participants interpreted the phrase ‘inclusive language’ in different ways. 

Some described it as “using the correct terminology”, while others framed it more broadly as “not leaving anyone out”. Those with accessibility backgrounds often drew direct connections between inclusive language and accessibility. 

Despite these differences, the term ‘inclusive’ was recognisable, with several participants using it unprompted when working through the scenarios.

Colleagues looked for practical guidance first 

After exploring how participants would approach different situations, we asked them to navigate the Inclusive Language Guide. This helped us understand whether the structure matched their expectations when looking for support. 

Several participants were unsure where the practical guidance began. Some mistook the introductory blog post for the guide itself, while others expected the examples and practical advice to appear much earlier on the page. 

Although participants valued the context and principles behind the guidance, they prioritised practical information they could apply immediately. The examples within the guide were consistently highlighted as the most useful content. 

The research continues to inform improvements to the Inclusive Language Guide 

Our findings have allowed us to identify some key areas for improvements to the Inclusive Language Guide.

Making practical guidance easier to find 

One area of focus is the guide’s landing page. We are reviewing its structure so that practical guidance is easier to find while retaining the contextual information that participants found valuable. 

The findings highlighted that the current structure does not always support the way colleagues use the guide. Participants wanted to move quickly to practical guidance while still being able to access the wider context when needed.

We are also reviewing page titles, navigation labels and the ordering of content to make it clearer where the guide begins and how it relates to the wider Editorial Style Guide. We’re considering how to better support both first-time and repeat use, making it easier for people to quickly find practical guidance while still providing the context and principles that many participants found valuable.

We’ll also consider how best to surface practical examples and support the different ways colleagues use the guide, whether they are reading it for the first time or returning to quickly check terminology and writing conventions.

Improving connections with related guidance 

The research also prompted us to consider how the guide connects with related guidance across the University’s web estate. 

Participants frequently expected to find inclusive language guidance within EDI resources or other specialist guidance. Improving discoverability means not only improving the guide itself, but also making sure it is connected to the places colleagues naturally go when seeking support.

As outlined in Emma’s blog, since completing the research, we have worked with colleagues responsible for complementary guidance across the University’s web estate to improve signposting between services.

This work will help ensure colleagues can find appropriate support regardless of where they begin their journey. 

Scenario-based research helped us understand behaviours 

The research reinforced an important principle in content design: guidance needs to be easy to find as well as useful.  

By combining colleagues’ existing experiences with scenario-based activities, we gained a better understanding of how people approach inclusive language decisions. These insights have already informed improvements to the Inclusive Language Guide and will continue to shape how we approach future content research.

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.

 

Our June Content Improvement Club session focused on AI. In particular we discussed the changes in search behaviour, how that’s impacting the way people consume content and some of the things you can do to make your content ready for this new search landscape. Attendees also got the chance to work in groups to put some of our top tips into practice.

This topic clearly resonated with with our community, as it was fully booked shortly after being advertised, so we ran a second session to enable more people to attend.

Changes in search behaviour in the wake of AI overviews

We started off the session by providing a bit of context around what has led to the change in the way people now search for information.

AI overviews help answer your question on the search results page itself

In 2024 AI overviews were launched by Google to answer search queries directly on the search results page. AI overviews take snippets of content from relevant webpages (often combining multiple sources) using Google’s existing search index and the same ranking systems as regular search.

Since their introduction, AI overviews have rapidly increased and are now a prominent feature on the majority of search results pages. U.S data from the end of 2025 shows that overviews appear in over 60% of all search queries, doubling from 2024.

New Data: Google AI Overviews Now Appear in 60% of Searches

The information source is attributed using tags, which contain a link to the website itself. However, we are seeing a trend of people finding the information they need and relying on the overview without visiting the website where the information originated. This is known as ‘zero click behaviour’.

 

 

 

 

 

 

 

 

 

The rise in ‘zero click behaviour’

The introduction of AI overviews has meant that people have started to become accustomed to getting an instant answer on the search results page. People are more often relying on overviews, rather than clicking a link to a relevant webpage to locate the information themselves. This means fewer people are making it to your webpage and if they do it may well be later on in their search journey.

In Q1 2026 studies report that 65% of searches on Google did not result in a click to anywhere.

Zero-Click Crisis Worsens – SuperPrompt

This figure is predicted to be significantly higher for those using mobile when searching.

Also statistics from 2025 show that 80% of people used answers directly on search pages and 50% relied on AI summaries for answers.

Zero-click searches reshape marketing strategies in 2025

These changes in search behaviour do require you to think a bit differently about the way your content is being used. This is summed up well by Guus Goorts, an education marketing coach, who says:

LLMs [Large Language Models] increasingly visit  your website on your audiences’ behalf.

Ways to make your content ready for AI

In the session we focused on the things that you can do to optimise your content for AI. Hopefully you will be glad to know that much of the evergreen content design advice we provide still stands strong in the face of the new AI search landscape.

Clear, self-contained content gives AI better material to work with

As AI can sometimes take just snippets of your page or grab sentences out of the wider context, one step you can take is to try to make sure that content makes sense even outwith its surrounding context.

The more self-contained and specific your content is, the more likely it is that AI systems will interpret it correctly and present accurate information to users. You can achieve this by avoiding:

• non-specific or generic headings that rely on page context, but do not make sense when lifted off the page – for example ‘Funding’ rather than ‘Postgraduate funding options for international students’
• references to content elsewhere on a page such as ‘see below’ or  ‘as mentioned above’ as AI may retrieve the sentence without the content it refers to
• vague descriptors in the form of unclear or missing subjects such as ‘This must be submitted with your application’ as if the surrounding text is missing, neither the reader nor AI knows what ‘this’ refers to
• videos without transcripts, captions or summaries as AI tools cannot watch video content, they process text far more reliably than video

Answer questions directly and avoid unnecessary preamble or woolly language

AI predicts likely answers from the content it finds. It can potentially miss the answer if it’s buried in long winded sentences, or not clearly explained.

In the session we gave the example of a prospective student asking: “What accommodation does the University offer?”

We provided two example bits of content that the AI could draw from.

Example 1 : ‘Our accommodation includes single rooms, shared flats and accessible housing.’ This contains the answer directly, it names the accommodation types and it can be lifted into an overview without losing meaning.

Example 2: ‘There are lots of options of different accommodation with various numbers of rooms depending on your needs.’ Whilst this sounds friendly it doesn’t provide much useful information. Phases such as ‘lots of options’ and ‘to suit your needs’ take up space without answering the question.

Content that is well structured and accessible is still key

Making sure your content is both well-structured and accessible is still key when we are looking at how AI will use and interpret it.

Frequent, meaningful headings add structure

Headings are one of the main ways people navigate content, they act as signposts, helping users quickly identify relevant sections and understand what information sits underneath them at a glance.

Frequent, meaningful headings also play an extra role in supporting the AI-generated summaries we know are becoming increasingly common. This is because headings help AI understand the structure of a page by breaking content into logical, retrievable chunks.

Headings also create a hierarchy that shows the relationship between different pieces of information on a page. Therefore, you should always mark up headings with the correct heading level, rather than using bold, underline or larger font sizes. AI also uses the heading hierarchy to:

• understand, summarise and cite your information
• work out which topics are primary, which are subordinate, and how information is grouped together

You can read more about using headings effectively in the style guide, our previous blog post and also on Caroline Jarrett’s ‘Editing that works’ website.

• Headings and page titles (Editorial style guide)​​
• ​Blog post on ‘Editing that works’ (which covers headings)
• ​Editing that works: Headings guidance (Caroline Jarrett)

Accessible content and AI overlap

Content that has been made accessible is a big step towards being AI-ready as screen readers and AI systems rely on many of the same signals, although the outputs are different. Therefore many of the things you hopefully already do to make content accessible will also help AI understand it. We can demonstrate the overlap by looking at headings, link text and alternative text.

Headings: screen reader users use headings to navigate the page structure, whilst AI tools will use them to understand and prioritise your content.

Link text: descriptive link text provides context and helps screen reader users decide where to go next, whilst AI tools will use this link text to help interpret page meaning and relationships between content.

Alternative text: well written alternative text helps users who cannot see images extract meaning from them, whilst it also provides text that AI can use to understand image content.

You can read more about how to make your content accessible in Mel’s blog posts from previous Content Improvement Club sessions:

• Using links and headings to make your content more accessible​
• How to write good alt text​
• ​Revisiting alt text for January’s Content Improvement Club 

Descriptive link text supports users and AI

Descriptive link text is important for users as it helps them understand where the link will take them at a key decision point. This is increasingly important for AI too as AI search and retrieval tools use links and their surrounding context as signals to understand the relationship between different pieces of content.

Therefore, a link labelled ‘Accommodation options for postgraduate students’ provides a much stronger signal than ‘Learn more’ as it helps AI understand both the destination content and how it relates to the current page.

Descriptive link text also helps AI-generated summaries and citations. When AI retrieves content to answer a question, it attributes the source(s). Clear link text can reinforce what a linked page is about and strengthen the connections between related topics.

You can read more about writing effective link text in the style guide and Caroline Jarrett’s ‘Editing that works’ website:

• Links (Editorial style guide)​
• ​Editing that works: Links guidance (Caroline Jarrett)

Write for humans, using plain language

In the age of AI, it’s still important that we write for humans first and foremost. This means using language that your users understand within your content and explaining terms they might not know. Short paragraphs and sentences will also help.

Google’s advice for content to be successful with Google Search is to focus on creating ‘people-first content’, rather than content made ‘primarily to gain search engine rankings’.

Creating Helpful, Reliable, People-First Content | Google Search Central 

You may find Nick’s blog post from a previous Content Improvement Club useful on this topic:

Now you’re speaking my language – what we covered in the February Content Improvement Club session

We worked in small groups to put the theory into practice

After our whistlestop tour of advice to help make content AI-ready, we then worked in small groups, using pages that attendees had brought along to the session. We each picked one or two areas to focus on and shared suggestions to improve the content and talked about what was perhaps already working well. This created some interesting discussion and allowed attendees to gain feedback and new perspectives on their content from colleagues they may not usually work alongside.

Frequently Asked Questions – the debate continues

The debate around the effectiveness of Frequently Asked Questions (FAQs) is one which has been circulating for some time within the content design world. It has reared it’s head again in relation to AI, given the rise of the ‘answer economy’ where people are typically asking more focused questions and looking for a quick answer.

The main point of contention is whether or not we should be writing more content in a question and answer format. There are different views on this topic, we’ve seen advice to use questions in your content that aim to match the questions that users are asking AI tools.

On some interpretations of this advice, you could read this as a call for more FAQs on websites. We’d like to urge caution before you take this approach though as FAQs aren’t without their problems.

FAQs can often:

• duplicate content that you have elsewhere on the site – this causes problems for site maintenance and search engines don’t tend to like it
• answer questions that aren’t really frequently asked
• make content hard to scan because they push keywords away from the left of the screen as they often start with ‘Can, What, How, Where’

So in summary, don’t rush to change all your headings to questions and create more FAQ sections. If you do use questions, answer them without waffle.

The University of Bath have written a helpful article about this topic.

Why AI doesn’t need Frequently Asked Questions (FAQs) | Digital Content and Development

Content maintenance is more important than ever

The final point we covered was the fact that content maintenance is more important than ever. This is because all published content can be cited by AI to generate an answer regardless of when it was last reviewed or updated. Therefore ensuring that you know what live content you have and whether it’s up to date is key if you want AI to bring back accurate information in response to search queries.

Duplicate content can also confuse AI, so regularly auditing your content to make sure there is effective cross referencing between pages and that the same information doesn’t appear on multiple pages, will again make it easier for AI to interpret and find what it needs.

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’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

A year on from the relaunch of our Effective Digital Content online course, we’ve recently made some improvements, particularly in relation to the ‘Understand what your content is for’ module. We’ve rewritten the module, moving from a video to a text-based format.

In this blog I’ll share a bit about the updates we’ve made and the thinking behind them.

Over the last year we identified ways to improve the module

It was always our intention that the course would follow a cycle of continuous improvement, where it would evolve when we identified ways to make it better for learners.

Over the past year, we’ve learnt quite a lot about what makes a module effective through our work to adapt the course for external audiences. Although this project has paused for now, the work has allowed us to improve the course for University colleagues.

We’ve also continued to learn about people’s preferences in how they like to consume information, as well as the environmental impact of the content we create and maintain.

Developing a proof-of-concept course for external audiences gave us a fresh perspective

When exploring how we could evolve the course for external audiences earlier this year, we had to think about new ways to teach certain concepts. The video in the first module explaining the importance of understanding the audience and purpose of your content was one of the areas we knew would need most work. The video had been produced many years ago, specifically for University staff and therefore contained various terms and references that wouldn’t be appropriate for an external course.

We knew how important it was to get this module right as the principles it teaches underpin the rest of the course topics. It involved going back to the drawing board to consider the key learning outcomes of the module and the practical skills we wanted to teach. This process provided the opportunity to re-imagine what the module could become and importantly whether we needed video to convey key messages.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Ongoing user research showed video was not always the preferred format

We were also aware from speaking to colleagues who had taken the course and in usability testing of the new module, that video was not always their preferred method of obtaining information. Some colleagues mentioned that their preference would be to view or download the transcript instead of watching the video in full, even if the video was relatively short. The benefits of this approach were that they could digest the information at their own pace and be able to refer back to relevant points more efficiently, rather than having to locate the correct place in the video.

These insights, alongside the time intensive process involved in producing and maintaining a new video influenced our decision to reconsider whether a text-based format would work better.

The environmental cost of media use cannot be underestimated

We’ve also been learning through ongoing research into digital sustainability about the environmental cost of media use, particularly images and videos. We’ve learnt the importance of considering the value that the media brings before choosing it as a medium to communicate your message. You can read more about this work in my previous blog.

Videos and images – when do they add value and not just page weight?

As a result, we’ve been questioning whether the video we previously used in the course enhances clarity, context or understanding, or whether we could convey the same information without it.

The module has evolved but the underlying principles remain

We came to the conclusion, based on user feedback and our own inclinations about how to improve the module, that we would remove the current video in the internal course and replace it with a freshly written text format module.

The underlying principles of the module stand true – that the starting point is always to think about the audience and purpose of your content. As a result, the accompanying workbook activities for the module remain unchanged.

There are a few new additions to the module

We’ve added in a new section to explain the key concepts of user needs and business needs, which should help to provide a good foundation for later modules. There are also some more practical University related examples of key concepts.

The previous iteration of the module didn’t have any interactive elements. We’ve received positive feedback on the quiz questions we’ve included in other modules of the course so we decided to include two multiple choice quiz questions so you can check your understanding as you go.

How to take the course

If you’d like to take the Effective Digital Content course, you can find more information on our course page, including a link to enroll through People and Money.

Effective Digital Content

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

The carbon cost of using images and videos on a webpage often goes unnoticed but cumulatively can add up. Dono Abdurahmanova and I have presented at both the UCISA digital sustainability conference and the Green Software Foundation Scotland meet up in the last week or so on the topic of sustainable media use.

The key theme of the talk was around intentional use of media and our efforts to shift the narrative around the perceived necessity for videos and images to create effective content. We also shared how we’ve been working with web publishers at the University to quantify the impact of their media content. You can read more about this aspect of the work in Dono’s blog.

Do students actually watch videos on websites? 

We finished by highlighting some steps you can take to make your images and videos more sustainable, if you decide they are the right medium to communicate your message.

The environmental cost of media use

Media is and will continue to be a key part of digital content strategies, that is a given and it undoubtedly does have its place as an effective communication tool – although the environmental impact cannot be underestimated.

Every page view, video stream and file download consumes energy and generates emissions. This is a fact which often goes unnoticed, as it’s not visible or as well known as other types of day-to-day activities which everyone knows create emissions.

When talking about the ‘weight’ of a web page (as I do in the title of this blog) this refers to the cumulative size of the files that are needed to load a webpage, such as images, videos, text, scripts, etc. These elements add weight to the page and the heavier the page, the greater the estimated CO2 emissions generated.

Here are a few statistics around the environmental impact of media use which provide a bit of context around why we felt it was important to think of ways to raise awareness about digital sustainability.

Digital content generally

When thinking about digital content generally, on average digital content consumption emits around 229kg of CO2 per person per year, which is up to 4% of our individual carbon footprint.

​Transition Templates AI & Digital: Pathways to Net Zero+  Dr Joanna Boehnert

Webpages

When it comes to webpages – globally the average web page produces approximately 0.36g of CO2 equivalent per page view. For a site with 10,000 monthly views, that is 43kg of CO2 equivalent per year.

Website Carbon Calculator v4 | What’s your site’s carbon footprint?

Images and videos

Then drilling down further into images and videos – streaming one hour of video content generates approximately 55g of CO2 equivalent.

Carbon impact of video streaming | The Carbon Trust

Despite their environmental impact, images make up between 49-58% of the total size of an average web page.

HotCarbon

​The starting point is to consider the value of the image or video

Often the messaging around digital sustainability can be dominated by talk of optimisation, compression and technical aspects like images formats and file sizes. However, this is actually a step ahead of where the starting point should be, which is whether the image or video adds value to the user in the first place. This is a principle which is also included in the Institute for Sustainable IT – Handbook of Sustainable Design for Digital Services.

When considering the value of media, it can be helpful to ask the following questions:

• Does it enhance clarity, context or understanding?
• Could you convey the same information without it?

While the answers will be context specific, often you can still provide the user with effective, useful information without it.

Intentional rather than automatic use of media is often not only better for the environment but also your engagement too, as it can reduce the cognitive load for the user and will likely resonate more with your audiences when used in targeted ways rather than in abundance.

“The process of behaviour change starts with awareness”

This is a quote taken from James Clear, author of Atomic Habits, which refers to the fact that people can’t change their habits if they aren’t aware of them in the first place. To this end we’ve been trying to spread the word about digital sustainability and in particular the impact of using images and videos. In doing so encouraging people to rethink their habits in relation to media use.

We’ve done this through reshaping our image guidance for the central content management system and also including the topic in our Content Improvement Clubs.

Reshaping image guidance

Working with the EdWeb service team, we reshaped the ‘Sourcing the right image’ guidance page for EdWeb 2, our central content management system. The page was heavily focused on where to find images and which were the best images to use, as these are often the first questions asked, based on an assumption that images are a necessary part of creating content.

We shifted the emphasis and tone of the page, so that it focused first on considering whether you need to use an image, highlighting the importance of both digital sustainability and accessibility when making this decision. After this we included the guidance to follow if an image is required and how to source one.

We hope that this change in approach helps to reframe the messaging and thinking around image use and encourages more intentional use of images.

You can find the new guidance on the EdWeb 2 hub.

Best practice for image use (University login required)

Including digital sustainability in our Content Improvement Clubs

Each month the UX Service runs Content Improvement Club sessions for anybody within the University who works with content. It’s a chance for them to meet up with other publishers and learn more about content design. At the beginning of the year, we ran a session on ‘5 top tips for improving your content in 2026’ and we made one of the top tips effective image use.

As part of the session, we invited attendees to look at the various different images from across the University web estate and think about which of them added value and what that value was. It was an interesting exercise and once people started to apply their mind to the question, they soon realised that some images added more value than others.

For example, having a stock image of a beach for an internal staff pensions guidance page was deemed to be more decorative, rather than aiding understanding, or helping users complete their task. Whereas the images of students on campus tours, or using University facilities, had more value in attracting prospective students by giving a human feel to events and give an idea of life at the University.

Tips for sustainable image use

If you decide images are the right medium to communicate your message – here are some tips on how you can make them more sustainable.

• Avoid generic stock images – eye tracking studies show that people tend to ignore large or generic images. Using real-life images of people interacting with services or buildings, which are relevant to the objectives of your content is likely to increase their value / impact and make them a more worthwhile addition to the page.

 

• Consider the format of your image – there are various optimisation and compression tools that can help you to reduce the file size of images, without impacting on quality. At the University, we use the WebP format for images in our central content management system which results in files being 25% to 34% smaller than JPEGs.

 

• Consider implementing an image upload size – this can help to reduce the usage of energy-intensive large files. At the University, we’ve implemented an image upload size of 1MB for the central content management system.

Make sure that images are accessible

Whilst the focus of this blog is on sustainability, as online content grows, accessibility becomes increasingly important.

When adding an image into your content, including alternative text (‘alt text’) ensures that more users can access the information it provides. Assistive technologies such as screen readers rely on alt text to convert content (such as text, buttons, images and other screen elements) into speech or braille. This allows blind or partially sighted users to access the same information as sighted users.

You can read more about how to write good alt text in my colleague Mel Batcharj’s blog.

How to write good alt text – what we covered in our March Content Improvement Club session

Tips for sustainable video use

• ​Disabling autoplay – this is a simple yet impactful action that can reduce energy consumption and lower the data demand. Autoplay adds unnecessary weight to the page, causing videos to load and stream regardless of whether the user has chosen to engage with it.

​​

• Including a written summary and transcript – this can support users who prefer reading as well as making your content more accessible. Both summaries and transcripts can help users get the key information they need and reduce the likelihood of them loading a video only to abandon it partway through as it didn’t meet their needs.

​

• Keep videos short and well signposted – we’ve seen from our research that user attention spans are shrinking, particularly when they are task-focused, looking to find the information they need. Therefore limiting the length of videos could help with engagement as well as sustainability. In addition using timestamps or chapter markers means that people don’t necessarily need to watch the whole video, they can skip to the relevant sections, reducing the amount of time the video is playing for, therefore reducing the energy usage.

 

• ​Avoid repetition of information – try not to repeat the same information in the video as you already cover on the webpage.  If you notice that think about whether you need the video – what extra value is it adding, that the text on the page doesn’t already offer.

​

• Think about alternative formats – rather than video could audio files, or podcasts work? Can you communicate your message without the video? Where audio adds value but visuals are not needed, consider using the MP4 audio format instead. Audio files are significantly smaller, require less energy to stream, and can carry the same content at a fraction of the environmental cost.

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.

 

It’s been a whole year since we launched the new and refreshed Effective Digital Content online course. To celebrate the one-year anniversary I thought I would reflect on how the year has gone, share a bit more about what we’ve learnt along the way, and what is coming next.

If you are keen to read more about the development of the course up until the launch date, you can take a look at our previous series of blogs.

Effective Digital Content blog series

Over 350 people have completed the course in the last year

The aim of the Effective Digital Content course has always been to share content design best practice across the vast University web publishing community. We are really happy that so many people have successfully completed the new version of the course, particularly given the new workbook element which we appreciate comes with an additional time investment. It shows a real willingness from the web publishing community to learn more about how to create effective web content.

As a result, over 350 Digital Badges have been issued under the University’s Digital Badge scheme ‘BadgEd’. Again, this is a real positive in terms of showcasing continuing professional development within our publishing community and we’ve seen people sharing their badge with their networks on LinkedIn.

Effective Digital Content badge details

The course workbook – reaping the rewards of trying something new

We’ve written in previous blogs about the decision to add the new workbook element to the course. For us this was very much a case of trying out something new, as it wasn’t something that had been explored in previous versions of the course.

The workbook idea was born out of a wider project to rethink our content design training approach. The key premise of the workbook is to provide a chance for people to apply the course principles to their own context and receive feedback from someone in the UX Service.

Practical application of the course principles with the opportunity to gain valuable insights from the UX Service was something we had seen work really successfully in our Content Improvement Clubs (our regular meet ups for anyone who publishes web content at the University). The task of delivering this at scale given the ratio of UX Service team members to web publishers was quite a daunting task, but one which I’m pleased to say has been a great success.

Over the last year 350 people have completed the workbook and received personalised feedback from the UX Service on their submission – no mean feat from either side!

We’ve had positive feedback about the personalised workbook feedback

We are so pleased with how the workbook has been received and the effort people have taken in completing it. We’ve seen people updating and improving their web pages as they complete the workbook exercises and in response to the tailored feedback we provide. It has been really rewarding to help facilitate and see tangible positive change in this way.

Here’s what a couple of people who have taken the course have said about their experience of completing the workbook.

I enjoyed completing the workbook as it allowed me to reflect on the various aspects of the course. Receiving feedback on the workbook was a great addition and not something I’ve had in other training courses I’ve completed.

Whilst time-consuming, this was very valuable as it really enabled engagement with the material, enhanced my learning and I feel confident in updating the web page I am responsible for now. The feedback was helpful too, thank you.

We’ve gained valuable insights from reviewing the workbooks

The UX Service have also found it incredibly useful to hear directly from publishers through their workbook answers. The process of reviewing and responding to each submission has provided invaluable insights which can help us to enhance the training we provide by responding to topics and areas which we know are most relevant.

Since the launch, we’ve also made further improvements to the workbook activities as well as the course modules in response to feedback we’ve received.

Taking on the task of reviewing each workbook has been a significant new workload for the team and we’ve been on quite the learning curve. However, we are pleased with how collectively we’ve taken on the challenge and the feedback turnaround times we’ve managed to sustain.

We are evolving and refining our processes to keep up with increasing demand

As demand for the course continues to grow from colleagues across the University, we are looking at ways to refine and automate some of the administrative processes involved in receiving and logging workbook submissions. This is to ensure we can continue to provide tailored feedback to an even larger number of people across the University.

We are developing the course for the Short Courses platform

Following a showcase of the course at the UCISA UX Community Day in September 2025, we received interest in the course from the wider Higher Education sector, with colleagues from other UK universities requesting access to the course content, so that they could apply the concepts to content publishing in their respective institutions.

In response, we are currently working with colleagues to deliver a proof-of-concept course for the Short Courses platform by summer 2026. You can read more about how we are repositioning the course for an external audience in Emma Horrell’s blog.

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

How to take the course 

If you’d like to take the Effective Digital Content course, you can find more information on our course page, including a link to enroll through People and Money.

Effective Digital Content