SRHE Blog

The Society for Research into Higher Education


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The ‘unpoliceability’ of AI journal policies

by Dirk Lindebaum

Core to the mission of SRHE is the commitment to high-quality research and scholarship to advance understanding of higher education. This focus makes the conditions under which research is produced, including editorial and journal policies concerning the publication process, a topic of natural interest to readers of the SRHE blog.

The publication process, and how stakeholders interact in that process, however, is profoundly changing due to the widespread use of generative AI (hereafter AI). Consider the irony that authors can now offload their writing to AI; that reviewers can use AI to perform reviews on AI-produced text; and that editors can use AI to make decisions on papers written by AI, and grounding decisions in AI-produced reviews. It is machines chatting with machines, and machines reacting to machines, just without the informed doubt, the willingness of daring to know, the realization that we as social scientists always condition and are conditioned through our work and social practices that follow from our work.

Notwithstanding this development, the use of AI in the research process is routinely and boldly argued to be ‘inevitable’. What is less often understood is that this narrative likely accelerates an already existing crisis of credibility in some fields (eg in management research). A sobering look at the available empirical evidence (summarized here) underlines that the crisis is real.

First, empirical studies suggest that, in 2025, more than 140,000 non-existing references have infiltrated scientific records. Second, while the quality of scholarship is decreasing (as indicated through a measure of language complexity), the use of AI helps researchers to produce more papers and faster (with direct effects on individual careers). All this unfolds against an already disadvantageous baseline, where disruptive and innovate research occurs less often. Third, while many AI journal polices exhort scholars to be the ‘human-in-the-loop’ to check the accuracy of AI outputs, empirical studies suggest that AI biases (caused by human biases in the training data) transfer and magnify in humans through epistemic feedback loops in Human-AI interactions. Fourth, many authors neglect to disclose AI-use, the consequence being that many AI journal policies fail to ensure transparency or constrain AI adoption. Hence, many AI journal policies concerning the use of AI may attempt to ‘police the unpoliceable’, despite the well-intending motives behind such policies.

After years of editorial frontline experience, I believe two policy moves are necessary to counter these developments which, if unaddressed, likely compromise the integrity of science. On the one hand, qualitatively speaking, I propose a set of evaluative criteria concerning how to evaluate (as editors and reviewers), and prepare submissions (as authors), in ways that mobilise judgment to strengthen the voice of authors in terms of mastery of the literature, method, analysis, contribution and recommendations for practice. Developing editor and reviewer judgment to strengthen author voices, and for authors to force themselves to nurture said judgment to inform their own voices distinctly and uniquely, is a matter of paramount importance when the defining scarcity of our time is not access to information or even knowledge, but rather the capacity to develop and apply sound judgment in the presence of seemingly boundless AI slop. I treat judgment here as epistemic judgment concerning reflexive justifications for truth claims – to oneself and others under conditions of uncertainty – and as expressed through the authors’ voice.

This effort has to be earned. This means persisting with developing judgment over time, not despite the struggle involved, but because of it. This is exactly why this struggle matters for AI advocates. While some argue that one needs to be an expert to use the AI in research, this point is often based on decades of building expertise before the arrival of AI. Yet, current socialization practices are such that junior colleagues and PhD students may not find the conditions in which to develop expertise. This does and will negatively affect expertise levels now and in future. The result is that the conditions of re-building expertise shrink, and that means the possibility to recover lost expertise decreases. It seems to me a caution that needs to be put plainly – this should concern usall. However, these ‘qualitative’ criteria are unworkable without also creating the conditions in which editors and reviewers can manage the current deluge of journal submissions.

Therefore, the second, quantitative, proposal is to introduce a submission cap per authors per annum (as assessed by ORCID IDs), given record numbers of submissions to many journals across science subjects. This would provide necessary ‘breathing space’ for often overstretched (volunteering) editors and reviewers, while promoting equitable access to editorial resources, reduce congestion in the peer review pipeline, and discourage the use of the journal as a preliminary check on manuscript submission readiness. Lastly, a submission cap can also counter the creeping ‘junkification’ of science – the mounting volume of low-quality content that permeate academic publishing, which will only intensify in the age of Gen-AI.

Subject to amendments to suit the needs of a particular journal, the proposal would work like this: each author can submit three new submissions per year to a journal, but with a twist to encourage authors to improve the quality of first submission. For each invitation to revise and resubmit a paper, authors could submit one additional paper to a journal (3 + 1). And if they are lucky to get a second (or third) invitation to revise and resubmit a paper, then that number would be 3 + 2 etc. The expansion is author-specific; if submission A gets a revise and resubmit, every author listed on submission A receives +1 to their personal limit at this journal. This expansion would apply to new submissions, and not to invited revisions.

Some colleagues may have reservations about the proposal due to a perceived limit to their productivity. However, let’s appreciate that even if a prolific scholar manages to publish three articles in each of the best five journals in their fields per year, these 15 articles can hardly be said to be detrimental to the scholar’s career prospects.

A submission cap is consistent with scientific progress because it helps editors and reviewers regain vital space to write quality letters and reviews based on the proposed new set of evaluative criteria. Hence, it is key to consider the complementary nature of both proposals, with the benefit of enabling the conditions under which published works can be trusted (more) in the age of Gen-AI. Epistemic agency would flow back to journals and their editors, for academics would set the agenda (rather than big tech).

Rather than advocating contra AI in the research process, my stance here is pro developing human judgment. If developed and assessed well in the research process, the focus on judgment will render the debate around AI journal policies of secondary relevance. Why try to police the unpoliceable if investing in human judgment is one of the best ways to safeguard the integrity of science?

Dirk Lindebaum is Professor of Management & Organisation at the University of Bath, and advocate and owner of “AI-Free Scholarship” which is a registered (noncommercial) trademark that applies to his writing and teaching.


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When will the underrepresentation of Black professors end?

by Zarus Cenac

On the SRHE Blog, in May and July, I wrote about the underrepresentation of Black people amongst professors in the UK higher education sector. In response to my May blog post, a commentator brought up the idea of a time delay between “the demographics of academia” and “changes in the wider population”, and that “estimating what is a reasonable lag is challenging and probably highly contestable” (Golding, 2026, para. 1). The present blog post takes a step along that path. There is an underrepresentation of Black female professors and Black male professors (Paper Whispers, 2025; Figure 1) – for females and males separately, I estimate how many years it may take for the underrepresentation of Black professors to end (Figure 2). This is done by estimating when the percentage of professors who are Black will eventually match Black representation in part of the UK population who are around a certain age (Figure 2). I also look for an indication of whether underrepresentation has been tangibly improving for Black female professors and Black male professors (Figure 2). Additionally, I consider which is the better of two similar methods for calculating the percentage of professors who are Black (eg, Figure 4a and 4b).

Figure 1

The Underrepresentation of Black Female Professors and Black Male Professors in UK Higher Education Institutions

A bar chart is shown – for females, .95% of professors are Black and 3.75% of 45-to-64-year-olds are Black. As for males, Black people are 1.19% of professors and 3.44% of 45-to-64-year-olds.

Note. Data (2024–25 academic year) from Higher Education Statistics Agency (HESA, nd-a) and 2021/2022 UK Census data (Office for National Statistics, 2025) were used regarding Figure 1 and in analysis. As before, statistical analysis was used to explore the representation of Black professors – see Box 1 for a description of this analysis.

Black representation over time: Professors

There have been observations on what the representation of Black professors looks like at different points in time (eg, Gibney, 2024; Rollock, 2025). For instance, between the 2021–22 and 2022–23 academic years, there was a 0.2% increase in the percentage of professors who are Black, with the percentage being 0.9% in 2022–23 (Gibney, 2024), and the percentage of female professors who are Black was 0.6% in 2019–20 and 0.8% in 2022–23 (Rollock, 2025).

I used HESA data for the academic years 2014–15 to 2024–25 (HESA, nd-a) to calculate the percentage of professors who are Black amongst females and amongst males (the green circles in Figure 2). Based on representation across those academic years, predictions were made for Black representation in academic years after 2024–25 by fitting a (quadratic) trendline to the data for females and for males – the trendline is the green, dotted line in Figure 2. Despite trendlines predicting a continued increase in Black representation in the professoriate, it should be expected that the percentage will plateau at some point (see the plateauing indicated in Figure 3 regarding non-professors).

Figure 2

Predicting When Black Professors are no Longer Underrepresented

Scatter graphs showing the academic year when the representation of Black people amongst professors is predicted to match Black representation in 54.5-year-olds for females and males. That academic year is about 2043–44 and 2037–38 respectively for females and males.

Note. Underrepresentation is predicted to end in the academic year when dotted lines cross. HESA (nd-a) data and 2021/2022 UK Census data (Office for National Statistics, 2025) were used for Figures 2 and 4. HESA (nd-a) state the number of professors by ethnicity categories: Asian, Black, Mixed, Other, White, and unknown ethnicity. In Figure 2 (and Figure 1), the representation of Black professors was calculated only using persons of known ethnicity. Additionally, using Census data (Nomis, 2013; Office for National Statistics, 2025), for females and males, Black representation amongst 45-to-64-year-olds in the UK was i) estimated for 2011 and ii) calculated for 2021/2022 (Box 2). For simplicity, census year (Office for National Statistics, 2025) was treated as the year when the academic year starts, eg, with the 2021/2022 Census data treated as the 2021–22 academic year.

Black representation over time: 54.5-year-olds in the UK population

The representation of Black people is lower in the professoriate than amongst people in the UK population who are between 45 and 64 years old – an age range which should reflect the ethnic composition of the professoriate well enough (Cenac, 2026). The midpoint of this age range is 54.5 years of age. For females and males, UK Census data are available for various ages and age ranges, for example, 15 years old, 50-to-54 years old, and 65-to-69 years old (Office for National Statistics, 2025). I estimated the year when ages and age ranges are 54.5 years old. To estimate when an age range is 54.5 years old, I calculated the midpoint of the age range, for example, the midpoint for the 50-to-54 age range is 52 years – the midpoint was used to represent the entire age range.

For ages and age ranges, UK Census data (Office for National Statistics, 2025) were used to calculate the percentage of females and males in the UK population who are Black. Figure 2 shows those percentages and the year in which the age/midpoint is 54.5 years old (orange circles). To estimate/predict what Black representation is like amongst 54.5-year-olds in the UK population for females and males at various times, a moving average line was used – the line can be seen in Figure 2 as a line which is orange and dotted.

The estimated representation of Black people amongst 54.5-year-olds seems to be acceptable as a stand-in for Black representation in 45-to-64-year-olds in the UK in the recent past if we consider representation in 45-to-64-year-olds in 2021/2022 and estimated representation in 45-to-64-year-olds in 2011 (see Box 2).

Figure 3

Level of Black Representation in Non-Professors

Scatter graphs present the percentage representation of Black people amongst non-professors for females and males for the academic years 2014–15 to 2024–25. For females, percentage representation goes from 1.51% in 2014–15 to 3.61 in 2024–25. For males, Black people were 2.10% of non-professors in 2014–15, rising to 4.88% in 2023–24, and at 4.86% in 2024–25.

Note. Data (which are of academics who are not professors) are from HESA (nd-a), and percentage representation was calculated from non-professors whose ethnicity is known.

Predicting an end to underrepresentation

The ethnic diversity of the UK has been predicted to be higher in 2051 than it was in 2001 (Rees et al, 2011). So, it should not be surprising that Black representation amongst 45-to-64-year-olds seems to have been increasing (see the purple circles in Figure 2). Therefore, when examining whether the underrepresentation of Black professors is improving, it is important to take into consideration changes in representation within the UK population (Figure 2). For females and males, between the academic years 2014–15 and 2024–25, the increase in the representation of Black professors does not seem to outpace the estimated increase in the representation of Black people who are around 54.5-years-old (Figure 2). Therefore, between those academic years, there appears to have been no tangible improvement in the underrepresentation of Black female professors and Black male professors.

It is possible to estimate the academic year when there will no longer be an underrepresentation. This can be done by seeing the academic year when dotted lines for professors and 54.5-year-olds cross over each other. That academic year is approximately 2043–44 for Black female professors and approximately 2037–38 for Black male professors. So, in the 2026–27 academic year, we are an estimated 17 years away from underrepresentation disappearing for Black female professors, and an estimated 11 years away for Black male professors.

Gains in Black representation amongst female and male academics who are not professors (Figure 3) suggest that there is reason to be optimistic that Black representation in the professoriate will increase for females and males. However, if Black representation amongst female non-professors is plateauing (at around 3.6%), then the trend amongst female non-professors (Figure 3a) does not bode well for an end to the underrepresentation of Black female professors by the early-to-mid 2040s (Figure 2a).

The percentage of professors who are Black, and the prediction of when underrepresentation disappears, depends on which of two (slightly different) methods is used to calculate the percentage (Figures 2 and 4). Using HESA data, the percentage of professors who are Black is calculated by dividing the number of Black professors by some sort of total number (eg, Gibney, 2024), and the two methods differ in how this total is defined (Figures 2 and 4).

Method 1: Known ethnicity only. When calculating the percentage representation of an ethnic group, or ethnic groups, the percentage can be calculated just from individuals for whom ethnicity is known (eg, HESA, 2026a). So, in this method, people of unknown ethnicity are not included in the total when calculating the percentage of professors who are Black (eg, Gibney, 2022; Figure 2).

Method 2: Overall. From looking at HESA (nd-a, 2026a) data, there seem to have been times when the percentage of professors who are Black (eg, Essilfie-Quaye et al, 2025; Gibney, 2024), or the percentage of female professors who are Black (Rollock, 2025), was calculated from a total number which includes people whose ethnicity is unknown. Under that method, when a percentage is calculated, a Black person could be part of the Black category or part of the unknown ethnicity category.

Compared to Method 1, Method 2 leads to a slightly lower percentage of professors who are Black (Figure 4a and 4b; also see Le Roux, 2022), and predicts that underrepresentation disappears a little further into the future (Figures 2 and 4). As briefly covered in my second blog post, the percentage of professors who are Black may seem smaller (but not very much smaller) than it really is. Method 2 would lead to a slightly further underestimation than Method 1 (Figure 4a and 4b). So, Method 1 may be a somewhat better method than Method 2 for considering the percentage representation of Black professors (eg, see Le Roux, 2022) and for estimating when underrepresentation will end.

Figure 4

Different Methods of Calculating the Percentage of professors who are Black

It is demonstrated, in scatter graphs, that Method 2 calculates a lower level of Black representation in the professoriate than Method 1 does for females and males. Other scatter graphs represent the prediction of the academic year when the underrepresentation of Black professors ceases, for females and males, under Method 2.

Note. For Figure 4c and 4d, analysis regarding Figure 2 was undertaken again, but without limiting analysis to professors of known ethnicity – it is predicted that Black professors will no longer be underrepresented by 2045–46 for females and 2038–39 for males. Considering data and literature (HESA, nd-a; Rollock, 2025), some of the percentages for Black female professors (overall) in Figure 4a are in Rollock (2025) to one decimal place.

Context and future research

There seems to be a dearth of literature which examines how percentage representation changes with time in academia and in the national population to see whether the extent of ethnic/racial underrepresentation in academia changes. So, it is hoped that the approach used in the present blog (Figure 2) is useful for exploring the underrepresentation of ethnic/racial groups in academia outside of Black professors. In previous research, a graph has shown 1) the number of geoscience PhDs gained by people in specific (underrepresented) ethnic/racial groups for various years (amongst the USA’s citizens or people with permanent residency in the country) and 2) the number of people in ethnic/racial groups in the USA for various years (Bernard & Cooperdock, 2018). The graph has been referred to when stating that, when considering four decades, “the representation of students from underrepresented minorities … has essentially been stagnant when compared with the proportion of the relevant groups in the US population” (Bernard & Cooperdock, 2018, pp. 292–293); the present blog post examines the percentage representation of Black people in the professoriate and in the UK population (Figure 2) – if not done so already, it may be useful to examine (across the years) the percentage representation of ethnic/racial groups in academia beyond the Black professoriate (eg, in achieving PhDs) alongside percentage representation at the national population level for an indication of whether ethnic/racial underrepresentation is changing and when the underrepresentation may stop.

In my first blog post, I described how a statistic known as Cohen’s h can quantify the extent that Black professors are underrepresented, and I considered how that statistic can be used to define when underrepresentation is no longer large enough to be noteworthy. Therefore, Cohen’s h could be used to estimate the academic year when the difference between Figure 2’s orange and green lines is no longer noteworthy – that year would likely be sooner than the year when underrepresentation is forecasted to stop.

Whilst this blog post predicts when underrepresentation will end for Black female professors and Black male professors (Figure 2), these predictions are not intended to be conclusive. Ideally, future census data (Northern Ireland Statistics and Research Agency, 2026; Office for National Statistics, 2026; Scotland’s Census, 2025) and HESA data (HESA, 2026b) will be used to update these predictions in the approaching years. Moreover, previous research on predicting the future representation of ethnic groups in the UK (Rees et al, 2011) suggests that the method for predicting an end to underrepresentation (Figure 2) can be refined.

Conclusion

There has been an increase in the percentage of the professoriate who are Black (Gibney, 2024). Nevertheless, there does not appear to have been a reduction in the underrepresentation of Black female professors or Black male professors between 2014–15 and 2024–25 (Figure 2). Despite this lack of reduction in underrepresentation, it is provisionally predicted that the underrepresentation of Black female professors and Black male professors will end in 2043–44 and 2037–38 respectively. However, the prediction for Black female professors seems unlikely given the plateauing indicated in the representation of Black female non-professors (Figure 3a). Hopefully, predictions will be strengthened through subsequent data and improved methodology; it is intended that this blog post encourages efforts to monitor and predict the underrepresentation of Black professors over time so that there can be a clear idea of where the situation is heading.

Box 1

Statistical Analysis: Underrepresentation

Data and analysis. Data of professors in UK higher education institutions for the 2024–25 academic year (HESA, nd-a), and UK Census data from 2021/2022 censuses (Office for National Statistics, 2025), were analysed. R Version 4.5.2 (R Core Team, 2025) was used to run z-tests – tests for seeing if there is a statistically significant underrepresentation – and to calculate Cohen’s h effect sizes (Cohen, 1988; Zieffler, 2025) – to signify the magnitude of the underrepresentation. A z-test found that, statistically, the representation of Black females is significantly lower in female professors than amongst females who are between 45 and 64 years of age (2(1) = 171.08, p < .001, Cohen’s h = .19). Likewise, a z-test demonstrated that Black males are significantly less represented amongst the male professoriate than they are amongst 45-to-64-year-old males (2(1) = 241.78, p < .001, Cohen’s h = .15). Cohen’s hs indicated that these underrepresentations are not negligible (see my first blog post). Caveats. Between 2021/2022 and 2024, there probably was a slight increase in the representation of Black females and males amongst 45-to-64-year-olds (Figure 2). So, comparing 2024–25 data of professors to 2021/2022 census data (as was done in the present blog) is likely to somewhat underestimate the underrepresentation. Probably, the number of individuals in HESA’s professor category does not reach up to the actual total of professors (HESA, nd-b). However, as discussed in my second blog post, the percentage of Black people in that category likely does well enough as a stand-in for the actual percentage of professors who are Black. It should be mentioned that whilst the proportion of professors who are female has risen, females nevertheless account for a minority of professors (Advance HE, 2024), with 32% being female in the 2024–25 academic year and the majority being male (HESA, 2026a) – in the current analysis, percentages were calculated separately for females and males.

Note. References: Advance HE (2024), Cohen (1988), HESA (nd-a, nd-b, 2026a), Office for National Statistics (2025), R Core Team (2025), Zieffler (2025), my first and second blog posts.

Box 2

Black Representation in 45-to-64-year-olds in the UK: 2011 and 2021/2022

Going by Figure 2, in the UK, Black representation in 54.5-year-olds seems to be in the ballpark of i) the estimated Black representation amongst 45-to-64-year-olds in 2011 and, ii) the percentage of 45-to-64-year-olds who are Black in 2021/2022. Percentages for 2021/2022 were calculated directly from census data. I estimated the percentages for 2011, and this estimation involved a few steps which are described next. 2011: England and Wales. Data are available for the 2011 Census for 45-to-64-year-olds in the population of England and Wales for females and males by ethnic background (Nomis, 2013). I used that data to calculate the percentage of 45-to-64-year-olds who are Black in 2011 for females (2.63%) and males (2.37%) in England and Wales. 2021/2022: UK, England and Wales. Using 2021/2022 Census data (Office for National Statistics, 2025), I divided the percentage of the UK 45-to-64-year-old population who are Black (3.75% for females, 3.44% for males) by the percentage of the England and Wales 45-to-64-year-old population who are Black (4.17% for females, 3.79% for males) for females and males separately. 2011: UK. Dividing the 2021/2022 UK percentage by the 2021/2022 England and Wales percentage gave a number (.90 for females and .91 for males) which I multiplied the 2011 England and Wales percentages by for females and males. Multiplying gave estimates of the percentage of 45-to-64-year-olds in the 2011 UK population who are Black – 2.36% for females and 2.15% for males.

Note. References: Nomis (2013), Office for National Statistics (2025).

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

Zarus Cenac has worked at UK universities, for example, he was a visiting lecturer at City, University of London. He is currently an administrator at UCL. His interests include race and ethnicity from an interdisciplinary perspective.


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Do university careers services pay? What fifteen years of Catalan graduate data tell us

by Rosario Scandurra

In May, I presented at the SRHE webinar, Using administrative data for employability/employment research in higher education: insights on findings, methodology and impact, the results of a paper that were still under review. The article, co-authored with Kristinn Hermannsson (University of Glasgow) and Stefano Fusaro (European Commission, Joint Research Centre), is now open access in the International Journal of Manpower, and this post picks up where that discussion left off.

Careers services occupy an interesting intermediatory position in higher education. Everyone agrees they matter, yet robust evidence that they affect graduates’ earnings is surprisingly thin. Research on placements and internships is stronger. Delis and Jones (2023) estimate a 4% salary premium for UK sandwich placements, and Margaryan and colleagues (2022) estimate about 6% for mandatory internships in Germany. Placements embed students in workplaces and may offer a distinct comparative advantage to students who apply for them. The core business of a careers service (eg guidance, counselling, and vacancy matching) is to act as an intermediary between HE and the labour market. Whether intermediation on its own paid off was largely a matter of correlational evidence, generally limited to a small number of institutions, or even a single institution. That was the main conclusion of the systematic review of employability programmes that colleagues and I published in Studies in Higher Education in 2024 (Scandurra et al, 2024). This review reports many positive associations, but few designs could separate career service selection from its effect.

Catalonia is the right place to ask such a question. The graduate survey run by AQU Catalunya, the regional quality assurance agency, asks graduates how they found their first job, in a labour market where search frictions are unusually costly. In fact, temporary contracts are common (at least until the last labour market reform), and more than 35% of recent Spanish graduates work below their qualification level, compared with 21.5% in the EU. We use data from six waves (2008–2023) and a sample of 5,919 graduates under 30 who moved directly from a first degree into work. Around 7% found their first job through the university careers service. In raw terms, they earned about 20% more than everyone else, but they were also more often male, from engineering and from graduate families, so the aggregate earnings premium tells us little.

To obtain a causal estimate, we instrument for service uptake using peer exposure, defined as the share of graduates from the same university, field, and cohort who found their first job through career services. Graduates learn from each other what works, and social learning shifts uptake strongly while showing no relationship with ability, family background, or work experience.

The headline result is an earnings premium of around 8.5%, measured within four years after graduation. The heterogeneity is more substantial. The premium is close to 16% for women and statistically indistinguishable from zero for men. It is around 17% for graduates entering the market with no experience, fading after a year or two of employment for men while women retain about 14%.

Our reading is that careers services work as matching intermediaries rather than as sites of skill formation. They reduce search costs and information asymmetries at the moment of labour-market entry, and they matter most to graduates who lack networks, sector knowledge, and employer contacts. That is why the effect exceeds placement estimates and why it concentrates among women, the inexperienced, and fields with loosely institutionalised recruitment.

Three implications seem appropriate for an interested reader. First, the value of careers services is concentrated in the first transition, which argues for front-loading support around graduation. Second, the graduates with the highest returns are not those who use the service most. Left to self-selection, an effective service can reinforce existing advantage. Targeted, it becomes one of the few institutional levers with evidence of narrowing the early-career gender gap, a point that resonates with UK work on unequal returns to work experience (Luchinskaya and Tzanakou, 2025). Third, measurement: this study was made possible by a single survey question, asked consistently for 15 years, about how graduates found their first job. Graduate surveys that record the entry route would allow large-scale evaluation of career services.

However, several limits of the study should be taken into account. We used repeated cross-sections, so we cannot follow the same graduates over time. Estimates are confined to Catalonia, which has a tight graduate labour market. We identify the effect for graduates whose choices respond to their peers, arguably the very group outreach is meant to reach. Linked employer–graduate records would allow a direct test of the matching mechanism, and that is where we hope to go next.

Rosario Scandurra is Ramón y Cajal Senior Researcher at UPF Barcelona School of Management, Universitat Pompeu Fabra, working on school-to-work transitions, graduate employability, and the territorial divide. The article is open access at https://doi.org/10.1108/IJM-10-2025-0875.


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Students are using AI for mental health: what should universities do?

by Eleni Meletiadou

This blog draws on cross-national research conducted within the Digital Mental Health for Young People (YouthDMH) COST Action CA23153, an EU-funded European research network.

Introduction

Students experiencing anxiety do not always approach university counselling services first. Some turn to digital self-assessment, mental-health apps or conversational artificial intelligence (AI). These tools may feel private and accessible, but students may struggle to judge their reliability or recognise when professional help is needed. Research on young people’s online mental-health help-seeking highlights both the opportunities and limitations of digital support.

This blog presents findings from a cross-national study of 220 students in the UK, Slovakia and Slovenia. Conducted within the YouthDMH COST Action. The study examined reported anxiety, the use of AI and other digital mental-health technologies, perceived usefulness, intentions to reuse these tools and concerns about potential harm. As a Working Group member representing the UK, I am particularly interested in how digital technologies can support young people’s mental health and help-seeking while addressing safety, privacy and unequal access.

The findings raise an important question for higher education: how can universities respond responsibly when students are already using digital tools to manage mental-health difficulties? Rather than simply encouraging or discouraging their use, institutions need to understand students’ experiences and ensure that digital resources complement, rather than replace, appropriate human support.

What my research reveals

Reported anxiety was highest among students in the UK, lower among those in Slovakia and lowest among those in Slovenia. Around half of participants reported using AI or digital mental-health technologies. General-purpose conversational AI, particularly ChatGPT, was used more frequently than dedicated mental-health tools. Perceived usefulness was the clearest factor associated with intentions to use AI again to manage mental-health difficulties. Participants also raised concerns about data protection, dependence, and potential negative effects.

These findings suggest that university policies should consider the tools students actually use, not only those institutions formally recommend. However, the survey does not establish why students choose particular tools. Perceived usefulness and intentions to reuse do not demonstrate that anxiety has improved, that advice is accurate, or that appropriate help has been received. Similarly, differences in reported anxiety across countries do not, by themselves, explain the reasons for those differences or establish their statistical significance.

What previous research adds

Previous research has documented cross-national differences in university students’ mental health, including a study of students in nine countries by Ochnik et al (2021). More recently, a rapid systematic review by Nyakhar and Wang (2025) examined the effectiveness of AI chatbots for mental health and wellbeing among college students. Their review identified promising findings but also limitations in the evidence, highlighting the need for more rigorous research.

My study brings reported anxiety, digital-tool use, perceived usefulness, intentions to reuse, and concerns about harm into one analysis across three national contexts. This offers a more specific basis for institutional discussion than treating digital support as a single category. Evidence about structured mental-health interventions cannot automatically be taken as evidence that general-purpose chatbots reduce anxiety, nor can findings about AI alone explain how students use the wider range of digital resources.

What should universities do?

1. Build digital mental-health literacy into student support. Students need practical guidance on evaluating information, recognising technological limitations, protecting sensitive data and understanding that digital tools cannot reliably diagnose anxiety. This guidance could be integrated into induction, personal tutoring, digital-skills provision and wellbeing support. The aim should be informed use, not simply telling students to avoid technology.

2. Connect digital self-help to human support. Counselling, wellbeing services, primary care and urgent support should be easy to find. Apps, online questionnaires and chatbot interfaces should communicate their limitations clearly and provide understandable next steps. Digital resources should complement, not replace, accessible and adequately resourced professional services.

3. Assess tools before recommending them. Universities should review privacy, reliability, accessibility and evidence of effectiveness before endorsing digital mental-health resources. They should also explain why a tool is recommended and how student information may be processed. The World Health Organization’s guidance on AI ethics and governance for health offers relevant principles for safety, transparency and accountability.

4. Involve students in service design. Universities should ask students which tools they use, what they find useful and what prevents them from seeking help. Consultation should include students with different backgrounds, language needs, disabilities and levels of digital confidence. A single app or support pathway will not meet everyone’s needs.

5. Measure outcomes, not just uptake. Downloads, satisfaction and intentions to reuse are insufficient measures of success. Institutions should evaluate whether students can identify reliable information, access appropriate help and navigate services safely. Where anxiety reduction is the intended outcome, suitable measures and research designs are needed. Universities should also examine whether referral pathways work in practice and whether particular groups encounter additional barriers.

From findings to institutional responsibility

The value of this study lies in connecting reported anxiety with digital mental-health tool use, the perceived usefulness of AI and concerns about risk across three national contexts. It moves the discussion beyond a simple choice between embracing and rejecting AI. Universities need to understand the digital landscape students are already navigating and respond with clear guidance, appropriate safeguards, accessible human support and meaningful evaluation. This requires collaboration between academic staff, student services, digital-learning teams and institutional leaders.

Institutions should neither dismiss students’ use of digital tools nor endorse them uncritically. The priority is to help students make informed decisions, recognise the limitations of digital self-help and reach appropriate support when they need it. Responsible digital inclusion means ensuring that technological accessibility is accompanied by reliable information, human care and institutional accountability.

Dr Eleni Meletiadou is Strategic Lead – Academic Development, Inclusion, Research and Innovation at the Centre for Excellence in Learning and Teaching (CELT), University of East London. She is a Principal Fellow of Advance HE (PFHEA), a National Teaching Fellow (NTF) and a UEL EDI Honorary Fellow. Her research focuses on inclusive and intercultural education, digital and AI-enhanced learning and assessment, equity and social justice in higher education, and student wellbeing. Her work explores how educational innovation can advance inclusion, digital literacy and sustainable learning across diverse educational contexts. She is a Working Group Member in the Digital Mental Health for Young People (YouthDMH) COST Action CA23153, contributing to European research and collaboration on young people’s digital mental health.


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Community engagement in higher education: is its moment finally arriving?

by Thomas Farnell

Community engagement has long occupied a curious position in higher education. Universities have been expected to contribute to society beyond teaching and research, and community engagement has existed in practice for decades. Yet it has often remained fragmented, unevenly recognised and peripheral to the policies and structures that shape what universities do.

Is this now changing? There are signs that it may be. In 2026, UNESCO became the first intergovernmental organisation to specifically name “community engagement” as one of the three missions of higher education, alongside research and teaching. At the same time, universities, European University alliances, networks and national initiatives are increasingly treating engagement not simply as a collection of activities, but as part of the core identity and mission of higher education.

An old idea, narrowed over time

Community engagement in higher education can be understood as how universities address societal needs in partnership with external communities, with “communities” encompassing public authorities, businesses, schools, civil society and citizens. The underlying idea is far from new. From their origins, universities have been fundamentally societal institutions, shaped by and responsive to external needs.

The emergence of the “third mission” in the late twentieth century reinforced the idea that universities contribute to society beyond teaching and research. However, over time, this broader societal role was increasingly subsumed under an economic narrative. The third mission came to be interpreted largely in terms of knowledge transfer, commercialisation and contribution to the knowledge economy. Economic and technological contributions took precedence over broader social and civic dimensions.

The issue, therefore, has never been the absence of engagement. Rather, community engagement has often existed without being clearly named, connected or supported as a coherent mission.

Europe’s “fourth mission”: promising, but weakly operationalised

For a period, European policy appeared to offer a possible way forward. In 2017, the European Commission referred to “civic universities” in its Renewed Agenda for Higher Education. In 2020, its vision for the European Education Area went further by introducing “service to society” as a fourth mission of higher education, complementing teaching, research and innovation and together forming a “knowledge square”.

In theory, this was promising. Distinguishing a mission focused on service to society from innovation could have created clearer space for universities’ broader social and civic role. The European Universities Initiative also seemed well placed to operationalise this ambition. Yet the results have been ambiguous. While reports on the activities on European university alliances show that community engagement initiatives are widespread across European University alliances, “service to society” has not acquired the weight one might expect from a genuine fourth mission. It is remains nested within broader agendas of innovation, society and economy, regional development or the green and digital transitions.

The problem, therefore, is not the absence of recognition of universities’ societal role, but the gap between broad mission-level ambition and narrower operationalisation.

Practice is beginning to move ahead of policy

On the ground, however, something more interesting is happening. Community engagement is not only expanding as an activity; in some cases it is being treated as a defining feature of university identity and as a strategic orientation that is acquiring institutional form.

Three European University alliances illustrate this broader move:

  • The EMERGE alliance offers perhaps the most explicit conceptual repositioning: rather than treating societal engagement as a consequence of teaching and research, it describes it as its “first mission”.
  • The EUNICoast alliance is embedding community engagement as an area of institutional development requiring evidence, reflection, planning and follow-up, including institutional reporting and action plans for all member institutions.
  • The YUFE alliance, meanwhile, positions community engagement as a transversal principle integrated across the alliance’s activities through its Community Engagement Declaration 2025–2030.

In these cases, community engagement is moving from something universities do towards something that helps define what universities are.

A recognisable field is emerging

These developments are supported by a wider transnational movement. Community engagement is increasingly acquiring the features of a recognisable field: shared concepts, methods, tools, platforms, networks, institutional examples and policy advocates. The TEFCE Toolbox provided one of the first structured European frameworks for institutional self-reflection on community engagement. Since then, further resources for action planning, university-community partnerships and community-engaged curricula have been developed and gathered through the platform www.community-engagement.eu. European networks support service-learning and community-based research, while bodies such as the Talloires Network of Engaged Universities and the UNESCO Chair in Community-Based Research and Social Responsibility in Higher Education connect this agenda globally.

National and regional developments point in a similar direction, although unevenly. Flanders and Catalonia have substantial engagement activity but still fragmented policy frameworks. Ireland has gone further in recognising engagement alongside teaching and research and developing national support infrastructure for engagement. In the United Kingdom, the recent civic university agenda has provided new impetus for universities and communities to define shared place-based commitments.

So “practice ahead of policy” is important, but incomplete. In some systems, policy is beginning to catch up.

The time is now

If ever there was a time for universities to demonstrate their capacity to work proactively with communities in addressing complex societal challenges, it is now. And many universities are already acting. The challenge for policy is no longer simply to encourage isolated examples of good practice. It is to recognise and support the emerging infrastructure through which community engagement can become a coherent and sustainable mission of higher education.

Ultimately, the challenge is not to introduce something entirely new, but to name, define, connect and strengthen what already exists. Whether community engagement is called a third, fourth or even first mission matters less than whether it is recognised as a core mission and supported accordingly. Universities are moving. Policy now needs to catch up.

This blog is adapted from the author’s article in Policy Reviews in Higher Education: Farnell, T (2026) Community engagement in higher education: from fragmented practice to core mission?

Thomas Farnell is a Senior Education Policy Expert at the Institute for the Development of Education, an NGO based in Zagreb, Croatia. He has led a series of transnational European projects involving universities, local authorities and NGOs to enhance community engagement in higher education. Email: tfarnell@iro.hr


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Academic professional development as a space for inquiry

by Yaz Iyabo Osho

Academic professional development (APD) has a central role in supporting teaching and learning in higher education. Through development programmes, workshops, resources, professional networks, and opportunities to share practice, colleagues are supported in developing their knowledge, expertise, and ideas.

I have long been interested in another aspect of academic professional development: what it can help us understand about educational practice, student experience, and the particularities of higher education institutions. When universities encounter challenges relating to inclusion, belonging, student engagement, or educational outcomes, how do we determine what kind of development or support is needed? What critically informs those decisions? Whose knowledge and experiences are considered? And what can we learn from what happens afterwards?

These questions underpin an approach I am developing as ‘Research-Informed Academic Professional Development’ (Research-Informed APD). My interest is in how academic professional development can contribute not only to supporting practice, but also to generating and connecting knowledge that informs educational decision-making and institutional learning.

The foundations of my thinking

Although I am now giving this approach a name, its foundations have developed over several years of my research and professional practice. Earlier in my career, working in widening-participation higher education contexts, I became increasingly interested in how students’ experiences could inform our understanding of engagement, progression and success. In my earlier writing on widening participation and enterprise education (Osho, nd), I argued for greater attention to institutional practices and approaches to teaching and learning, rather than focusing on perceived deficiencies among students.

This thinking continued through collaborative research exploring the experiences behind ethnic minority students’ attainment (Seuwou et al, 2023), alongside my own work on racialised awarding gaps and inclusive curriculum practices. Several of my studies were subsequently referenced in the University of Westminster’s Access and Participation Plan (2025–2029), contributing to the institutional evidence base for understanding educational inequalities and informing approaches to curriculum development and inclusion. Later in my career, my responsibilities as an EDI Lead brought together research, colleague development, inclusive and anti-racist pedagogy, curriculum enhancement, and evaluation.

More recently, moving into an institution-wide academic professional development role allowed me to consider these questions across different disciplines, professional roles and institutional contexts at scale. It also made me think more about how knowledge can be generated through educational practice. Looking across my work, I can see a consistent interest in understanding educational questions before deciding how best to respond. Research-Informed APD is the name I am now giving to an approach that has developed through this research and practice over several years.

Building on established scholarship

The idea that academic professional development should extend beyond individual activities is not new. Educational developers and researchers have long explored professional learning, reflective practice, inquiry, and the relationship between development and educational change. For example, Amundsen and Wilson (2012) conducted a conceptual review of the literature on educational and academic development and identified different orientations to practice, including institutional development, skill development, action research and inquiry. Their work invites us to consider not only whether development is effective but also the assumptions that inform its design.

Similarly, King (2022) considers how academics develop their teaching expertise over time, drawing on evidence, reflection and their experiences of practice. She highlights the importance of curiosity, community, and care, as well as the role of academic development and institutional cultures in supporting continued professional learning.

This scholarship provides a useful foundation for my thinking. My particular interest, however, is in the academic professional development function itself, and how we understand its contribution to educational practice and wider institutional learning. I am interested in what academic professional development can help institutions understand about their educational practices, beyond the programmes, resources and support it provides.

Towards Research-Informed Academic Professional Development

Research-Informed APD is the name I am giving to an approach that has emerged through my research, professional practice and reflections on the purposes of academic professional development. I am developing this line of thinking further in a scholarly paper that will examine its conceptual foundations and implications for academic professional development. For now, my proposition is that academic professional development should be recognised not only for the activities it delivers or the practices it supports, but also for the knowledge it can generate and the institutional learning it can enable. This requires us to look beyond responding to immediate educational challenges and consider what we might learn through the process.

Dr Yaz Iyabo Osho is a Reader and Director of Academic Professional Development at the Centre for Education and Teaching Innovation, University of Westminster. Her research interests include inclusive and anti-racist pedagogy, academic professional development, CPD governance, belonging, and the experiences of Black students and academics in higher education. She is currently developing a scholarly framework for Research-Informed Academic Professional Development.


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Five things we learned from universities’ pensions accounting disclosures in 2025

by John Ingoe

It’s been a period of good news for university pensions: Teachers’ Pension Scheme (TPS) costs are set to fall from next year, the USS preliminary valuation results show a sizeable surplus and the aggregate funding position of self-administered trust (SAT) schemes sponsored by universities improved materially over the year to 31 July 2025. But beneath the headline improvement there are stark gaps between  institutions with a SAT defined benefit (DB) scheme. Some universities may soon be considering how best to use a pension surplus, while others could face higher contribution demands under the Pensions Regulator’s new funding regime.  I should note that for trust-based schemes, it is the trustees who are the legal owners of the pension scheme assets. They must act “in line with the pension scheme trust deed and rules, in the best interests of the pension scheme members [and] impartially, prudently, responsibly and honestly.”  As such, decisions on both use of surplus and making up shortfalls do not rest solely in the hands of the universities involved.

We reviewed the 2024/25 pension disclosures of 29 universities that sponsor their own SAT DB scheme for some groups of staff. We looked at how their funding positions have changed, how much universities are contributing, what their schemes cost to run and how these arrangements compare with the wider DB universe and USS. Five findings stood out.

Finding 1: Things are looking up

Balance sheet positions improved and contributions paid by universities fell over the year. Driven by a combination of higher bond yields, strong returns from growth assets, and employer contributions, all but three universities saw an improvement in their balance sheet position over 2024/25.  All else being equal, those same factors should also mean that funding positions, which drive contribution requirements, have improved in recent years. We saw some evidence of this over 2024/25, as aggregate contributions fell to around £190m from £220m in 2023/24.

However, there’s a big caveat – the Pensions Regulator’s new funding code is now in place, and so all else is not equal.  The new funding code may lead to the trustees of SATs pushing for stronger funding targets and less time being given to making good any shortfall, both of which could lead to an increase in contribution requirements. Exactly how contributions change will, as always, depend on each institution’s particular set of circumstances.

Finding 2: The aggregate position hides material differences between institutions

Pension schemes have legal obligations to balance assets and liabilities in the long term. Schemes are frequently reviewed to check the  – “the extent to which the employer and any suitable contingent assets can support the scheme, now and in the future, and the risks to this support being available when needed.” If normal employer contributions are not currently sufficient they may be obliged to pay extra deficit contributions to restore the balance.

Universities and trustees carry out valuations of their pension schemes for a variety of reasons. Every year, universities carry out a valuation to determine the pension scheme asset or liability to recognise on their balance sheet, and the cost of the scheme to show in their accounts for the year.  For universities reporting under the UK accounting standard, the requirements for this valuation are set out in Section 28 of FRS102.

Separately, every three years (or more frequently if needed), trustees will assess whether they have sufficient assets to meet their liabilities (ie the pensions that are promised to members).  In carrying out this valuation, and placing a present value on future pension payments, assumptions are made around future investment returns, inflation and mortality (among other things).  If the valuation shows a shortfall (ie if the assets are not sufficient to cover the liabilities), the sponsor may be obliged to pay deficit contributions to ‘balance the books’.  The requirements for funding valuations are governed  by the Pensions Regulator.

The aggregate position across the 29 universities in our survey is healthy, with an FRS102 surplus of £620m at 31 July 2025.  But funding levels vary significantly and different universities are having to consider very different questions. For a handful of better funded schemes there is a surplus and attention will turn to how the surplus could be used. Could it subsidise the cost of new benefit build-up or improve member benefits, could it improve defined-contribution (DC ) benefits, or could it be returned to the university? For the five best funded arrangements the total surplus on a Low Dependency basis could be in excess of £600m. (Low Dependency is the minimum measure that could be used to determine whether a surplus could be released to a sponsor, ie the university).  Potentially, that £600m could all be used for one or more of the aims set out above.

Others are still facing a shortfall and are continuing to pay extra deficit contributions. The new funding code could mean that scheme trustees look to strengthen funding targets and remove any shortfall more quickly. For the five least well-funded schemes, annual contributions by universities might increase by 50% to meet the new DB funding code’s “Fast Track” parameters. 

Finding 3: HE funding positions lag behind the wider UK DB universe

The Pensions Regulator (TPR) estimates that, for all the DB schemes it regulates, around 80% of schemes are fully funded on a Low Dependency basis and 60% on a buy-out basis. However at 31 July 2025 we estimate that the equivalent figures are 25% and 15% in higher education. On average, DB schemes in the HE sector are not as well funded as their counterparts elsewhere. 

University accounts do not give definitive information, but an educated guess is that:

  • As part of a funding valuation, trustees consider the ‘covenant’ – “the extent to which the employer and any suitable contingent assets can support the scheme, now and in the future, and the risks to this support being available when needed.” – provided by the sponsoring employer.  Historically, employers in higher education have been viewed as having a strong covenant (or certainly, a stronger covenant than many UK DB scheme sponsors). Having a stronger covenant means that trustees and sponsors are more able to adopt less prudent funding targets, and to allow any funding shortfall to be made good over a longer period. 
  • Most of the schemes in our study have now hedged a significant level of interest rate and inflation risk, but the timing of any change in investment strategy matters.  A scheme that hedged these risks before the “mini-budget” in September 2022 will not have benefitted as much from the subsequent bond yield rises (as other schemes, including USS, have). 
  • Many of the DB schemes considered remain open (three in four still have benefits building up).  That means that there is naturally less focus on the “end-game” of the scheme, which may in turn mean less stringent funding targets are in place.  For example, an open scheme is unlikely to be targeting the very prudent level of funding needed for a buy-out transaction (ie securing benefits with an insurer – an option that is increasingly targeted by closed private sector schemes). 
  • The SAT DB schemes do not represent the most significant pension cost or risk for HE institutions. For some institutions, the level of contributions required for USS is also likely to have constrained their ability to address funding shortfalls in their SAT schemes more quickly.

For many institutions it may well remain the case that they are not as prudently funded as the average DB scheme – and that’s absolutely fine. However, for some universities, scheme trustees may become more uncomfortable with shortfalls and/or weaker funding targets. The general improvement in funding levels across the DB universe, combined with the Pensions Regulator’s new funding code, may mean we see scheme trustees pushing for higher contributions to strengthen their scheme’s funding position more quickly.

Finding 4: Running a DB pension scheme is an expensive business

Running a DB pension scheme is not cheap, but our survey suggests that the costs of running schemes in the HE sector may be particularly high. We might expect the core running costs for schemes to be around 0.33% of assets for smaller schemes and as low as 0.15% of assets for larger schemes. Based on the information in our study, the average running cost for those that disclosed it in their 2024/25 pension note was almost 0.6% of assets, with 1 in 8 spending more than 1% of assets on running their scheme over 2024/25.

There will undoubtedly be reasons for this “overspend”. Benchmarking of costs is likely to be dominated by closed schemes, and it is more complex (and therefore more expensive) to run an open DB scheme. Universities may also have provided additional work beyond the “core service” requirements of a pension scheme. But even taking those factors into account, there may still be scope to improve efficiency and reduce costs. Pension running costs may not receive the same scrutiny as other university expenses, especially if they are met directly by the scheme, but these costs are still ultimately borne by the employer, and so should face the same scrutiny as other costs.

Finding 5: The importance of these arrangements is overshadowed by USS

Across the universities in the survey, less than 10% of total pension expenditure was in respect of benefits building up in a SAT DB scheme. This partly reflects the fact that DB is not the sole method of pension provision for non-academic staff in the sector – many institutions offer a DC option alongside, or in place of, their DB arrangements. But most pension expenditure is on academic staff who, for the institutions in our survey, are mainly members of USS. As a result, the contributions to USS were materially higher than contributions to other arrangements.

In total, the 29 universities considered recognise around £6bn of pension liabilities on their balance sheet.  The USS preliminary funding valuation (for all universities in the scheme) puts USS pension liabilities at over £60bn. There are more employers participating in USS than the 29 universities considered in our study, so we cannot conclude that each university has ten times as much exposure to USS as to its own SAT DB scheme. At a sector level, however, it does illustrate the relative scale of USS – and, given its strong current funding position, the significance of that improvement for the financial outlook of the sector as a whole.

What should universities be thinking about?

From a pension perspective, things have greatly improved in recent years, with an expectation that the position at 31 July 2026 is likely to be even better. Continuing increases in bond yields have improved accounting and funding positions, while also reducing the cost of providing future benefit accrual. There is also good news from the other schemes in the sector (USS, TPS and the Local Government Pension Scheme).  Coming at a time of significant uncertainty for the sector, falling pension costs are certainly welcome.  Decisions made on the ongoing USS valuation will have a material impact on employment costs for the sector.

But it’s not all good news for the sector.  For the 29 universities we have considered it is very much a story of the haves and have-nots. The Pension Schemes Act 2026 is expected to make it much easier for employers to access surplus, although employers and trustees will first need to consider the appropriate balance between using surplus for the benefit of members and returning it to the employer. Those institutions still facing a funding shortfall will need to be aware of the potential implications of the new funding code, and whether that will drive up contribution rates.

In both cases, monitoring and managing running costs will be important. For employers making good a deficit, every pound spent on expenses is a pound not being used to close that gap. For those considering a surplus release, keeping running costs under control will help maximise the surplus available and make a release more achievable.

John Ingoe is an Associate Partner and Head of Employer Actuarial Services at First Actuarial, a Gallagher Company. He has over 19 years’ experience working with employers such as higher education institutions and other not‑for‑profit organisations. He helps them meet strategic pension challenges including defined benefit scheme funding, multi‑employer pension arrangements and complex pension change projects. www.firstactuarial.co.uk. john.ingoe@firstactuarial.co.uk..


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Tell it slant: what universities miss in the joins

by Joanne Irving-Walton

Universities are increasingly good at recording fragments of student experience. But fragments do not automatically make a whole. The work of connecting them remains stubbornly human.

“Tell all the truth but tell it slant —” Emily Dickinson advises.

It is an odd instruction for universities increasingly committed to straight lines: lines of accountability, lines between strategy and delivery, pathways through programmes, and data intended to show, cleanly and conclusively, what is happening. Yet neither learning nor universities behave quite like this. Students move between modules, systems, academic staff, professional services, regulations, placements and increasingly distributed forms of provision. Each part of the institution sees something. None necessarily sees the whole.

At the same time, we are becoming increasingly accomplished at recording these fragments. Attendance is captured. VLE activity can be traced. Marks, referrals, extensions and interventions leave institutional records. Processes generate evidence that something has happened. Reliable systems, fair regulations, consistent decisions and clear routes to support all matter. The problem begins when increasingly detailed representations of student experience are mistaken for increasingly complete understanding of it. A university can know a great deal about what a student has done while understanding remarkably little about what is happening.

Students experience the joins

Students rarely experience universities as organisational charts. They encounter them at the joins: between module and course, academic and pastoral support, partner and university, attendance and wellbeing, policy and circumstance, one team’s responsibility and another’s. This is where seemingly small difficulties can accumulate.

A student is sent the correct information but does not understand what they need to do next. A concern is referred, but nobody knows whether they reached the service. Marks are recorded module by module, while the pattern across them goes unnoticed. One part of the university knows about caring responsibilities; another sees only absence. Nothing necessarily fails. The failure sits in the joins — and those are rarely what institutions measure.

Each part of the institution may have done what it was required to do. The record contains multiple fragments of truth, carefully documented and separately owned. The student lives them all at once. That mismatch will be familiar to anyone who has ever sat with a student while several perfectly reasonable institutional answers fail to add up. But for those fragments to become a coherent experience, someone has to connect them.

Course leaders, personal tutors, module teams, administrators and support staff notice when several small things form a pattern. They translate formal language, recognise when an apparently straightforward case is not quite straightforward, contact another team rather than simply redirecting the student, remember context that is not visible in the current system, and follow up after the formal handover.

This kind of work has a substantial intellectual lineage. Strauss described the ‘articulation work’ required to coordinate complex activity, while Star and Strauss (1999) examined how work essential to keeping systems functioning can remain invisible to formal accounts of what work is. More recent higher education research has similarly drawn attention to boundary-crossing work and the expertise required to operate across institutional divisions (Vere, Verney and Webster-Deakin, 2024).

Pugh (2024) uses the term connective labour for the relational work through which people recognise, read and respond to one another in professions such as teaching and care. The work I am interested in here operates at a different level. It is the interpretive and relational work of connecting the fragments of an institution so that students experience something more coherent than a series of separate systems, teams and processes. I call this institutional connective labour: recognising which elements belong together, what context matters, where apparently routine cases require another look, and when completing a process has not resolved the problem that prompted it.

The work that disappears when it succeeds

Institutional connective labour is remarkably difficult for organisations to see, particularly in the large and distributed environments of higher education. Successful repair tends to leave little trace. An experienced member of staff spots a problem before it escalates, finds the colleague who knows how to resolve it, explains an opaque process, reconciles contradictory information or prevents an administrative delay from affecting progression. Eventually, the student continues. On paper, very little happened. Indeed, the successful outcome can make the system itself appear to have worked. This creates an uncomfortable organisational paradox. The people who are best at compensating for fragmented systems may inadvertently make that fragmentation less visible. Their judgement, memory, relationships and persistence absorb the gaps.

Workload models record formal roles more readily than the accumulation of noticing, translating, connecting and following through around them. Restructures may identify apparent duplication without asking whether overlapping roles are providing connection or resilience. Processes can be streamlined without recognising the informal knowledge through which they were previously made workable.

The visible machinery remains. The less visible capacity that made it function is weakened. As universities become more distributed across campuses, partners, professional services, digital environments and different forms of provision, fragmentation is not necessarily evidence of poor organisation. Complex institutions will always distribute responsibility. But distribution creates a corresponding need for integration. We tend to design carefully for the former and assume the latter will happen.

Greater resolution is not greater understanding

Data and process offer partial institutional views. An attendance pattern can signal a problem without explaining it. A referral records movement through a system without showing whether useful support was reached. A completed process can demonstrate compliance while the original difficulty remains unresolved.

Learning analytics scholars have long warned against treating data as neutral representations of student experience. Signals require interpretation, context and attention to student agency (Slade and Prinsloo, 2013). The same problem extends beyond analytics. Rules, records and processes are representations of reality, not reality itself. Their application still requires decisions about relevance, context and meaning. Informal systems can reproduce inequality, make support dependent on encountering the right person and leave both students and staff unprotected. But neither can the need for context and interpretation simply be designed out.

The difficulty becomes clearest when several individually reasonable accounts do not quite align. Low attendance may indicate disengagement, but it may mean something else entirely. A missed deadline may be habitual delay or the point at which an otherwise successful student can no longer absorb a series of pressures. A student may appear to have been referred successfully while moving repeatedly between services without ever finding somewhere their problem fits. Greater resolution does not necessarily produce greater understanding. Sometimes it simply gives us a more detailed picture of the fragments. The difficulty is not that any one account is false. It is that each tells the truth from only one angle.

Seeing slant

Dickinson’s ‘slant’ offers a useful way of thinking about this. Seeing slant is not an argument for indecision, endless consultation or gathering every possible perspective before acting. It is the recognition that complex experience rarely arrives from one direction. For universities, two questions can help expose the joins:

  • What alternative stories could produce the pattern we can see?
  • Where are staff repeatedly having to translate, reconnect or repair what a formal process has separated?

These are questions about judgement rather than additional process. The aim is not to collect every possible view before acting. Universities must simplify, classify and make decisions. But credible judgement requires some awareness of what has been simplified in doing so. The more useful institutional question may therefore be not simply whether a process has been completed, but what had to happen around that process to make it work.

The institution between the boxes

The point is not simply that this work should be recognised. It should tell us something. If staff repeatedly have to reconnect the same processes, translate the same information or recover context lost at the same handover, those interventions are evidence. They show where the formal account of how the institution works diverges from how it works in practice.

That makes institutional connective labour potentially diagnostic. A workaround may solve an immediate problem, but repeated workarounds reveal something more useful: where information stops travelling, where responsibilities do not quite meet, or where a process produces completion without resolution. That does not mean turning connective labour into another metric. Formalising it too heavily would risk flattening the very context and interpretation that make it useful. The point is to create ways for recurring patterns noticed through this work to inform institutional learning without reducing them to another dashboard. Nor should institutions depend on a few experienced individuals to hold the system together. The challenge is to make the conditions for noticing, connecting and escalating less accidental — and the knowledge generated through that work less private.

A practical starting point is to ask, in course reviews, service reviews or handover discussions: where are staff repeatedly having to reconnect what the formal process separates? Then take one recently ‘resolved’ case and ask a simpler question: what had to happen outside the formal process for this to reach a workable outcome? If the same kinds of repair appear repeatedly, that is not merely evidence of helpful staff. It is evidence about where the institution itself needs attention. For the student, the difference between a fragmented institution and a coherent one may simply be whether somebody noticed the joins.

Joanne Irving-Walton is a Principal Lecturer at Teesside University, working across learning and teaching and international partnerships. She is particularly interested in how academic judgement and professional identity develop through the emotional realities of higher education.


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Bringing GTAs into the career development conversation

by Kristyna Campbell and Kaiyun Feng

The 2026 report published with HEPI by Dr Charlotte Fawcett entitled More than academia: Expanding career support for doctoral researchers sheds light on a timely challenge faced by swathes of PhD graduates. Notably, that much of their studies and professional development is shaped on the premise that an academic profession will be the destination for most. Fawcett weighs in on this issue, illustrating that PhD candidates are increasingly applying themselves to professions both across and beyond academia in today’s fierce employment landscape, and goes on to explain that preparation for this reality is made more difficult by the varied availability of resources to PhD candidates, mainly through their supervisors. While the report calls for better collaboration between stakeholders and more opportunities for work experience, we expand Fawcett’s work by offering insight into professional learning and career development experienced by Graduate Teaching Assistants (GTAs) during the doctoral journey.

Fawcett’s report gives the sector something it has lacked for some time: a clear, current picture of how doctoral researchers experience career support, set against a labour market that no longer resembles the one for which doctoral study was originally built. Its central contribution is not simply diagnostic. By tracking change since 2019, and by pairing survey data with the voices of students and careers professionals, Fawcett provides institutions with meaningful evidence to work from, and a set of recommendations for action. It is essential reading for anyone with a stake in doctoral education, and it sets a benchmark for the kind of practical, student-centred research this space needs. 

Our recent study highlights the significance of the GTA role as a site for doctoral employment, within which wide-ranging skills are fostered pertaining to teaching, research and service. Using a model that explores how GTA career trajectories were shaped by expectations and realities of the role, and aspirations beyond graduation (Feng, 2024), qualitative data from eight GTAs from different disciplinary contexts were explored. The intention of our study was to make explicit the connection between GTA professional development and career development, through consideration for how GTAs understand teaching, research and service roles and their implications for identity.

In the GTA role, doctoral candidates are encouraged to ‘try on’ identities, and to undertake dedicated skills training designed to ensure high-quality teaching and assessment practice.  Their capabilities can include interpersonal, leadership, and organisational skills. Our findings indicated that the GTA role remains oriented toward academic careers, despite the various professional pathways taken by today’s doctoral candidates, a point also raised by Fawcett. Building on this understanding, we consider possible internal institutional and experiential opportunities designed to elevate the competence and confidence of doctoral candidates applying for work beyond academic teaching roles. Like Fawcett, we argued that support in recognising and articulating the transferability of knowledge and skills acquired through employment as GTAs is needed and instead suggest that learnt skillsets should be understood as competencies that can be applied across many careers and contexts.

From our study, we sensed that the expected linear academic career trajectory was falling away as graduate numbers rise and post-doctoral positions become fewer. While, as Fawcett reports, an increasing number of career-focused resources are becoming part of supervisory toolkits, we believe the responsibility extends to other members of the institution (including disciplinary experts, careers teams, and education developers) whose tailored care may have implications for preparedness.  

Our data add another perspective to Fawcett’s work regarding a shared understanding of the benefits of GTA-related competencies. Where Fawcett finds that only 27% of PhD students believe their supervisor has useful advice for non-academic careers, a related opportunity for helping supervisors and GTAs to better articulate GTA competencies was found in our data. We asked GTAs to plot their expected, actual, and hoped-for relationships with teaching, research (doctoral studies), and service, and the participants’ responses scattered without forming any discernible cluster. This variation has been attributed to the challenge of communicating tasks, responsibilities, and learnt skills, which may be a disciplinary concern. Helping PhD students to identify and articulate their current work within Feng’s (2024) framework could, in turn, support supervisors and other significant colleagues to help with locating prospective career paths and understand the transferability of competencies developed as GTAs and doctoral researchers. While the GTA lived reality appears varied, individual-level insight now can prepare for career clarity later, pointing to a single intervention with dual benefit rather than two separate problems to solve. 

We also noted a shift in GTAs’ present-tense identities and their imagined post-graduation selves. Participants who located themselves around teaching and service when reflecting on their reality consistently repositioned themselves towards research when projecting beyond their PhD studies. This shows that participants experienced their current role and future career as distinct rather than continuous. This resonates with Fawcett’s finding that, while 82% of students feel prepared for a research career, only 40% feel similarly prepared for non-research careers, and suggests that building this preparedness is partly about how GTAs are supported to narrate their experiences, not only about the information or resources on offer. This speaks to our earlier diagnosis about how aspects of a role are articulated, and how this is translated for work within and beyond academia. One participant’s insistence that they do not ‘identify as teaching staff’, despite teaching being a substantial commitment, points to an opportunity: helping GTAs to recognise the significance of teaching and service to the development of academic identity, rather than as detours from it, complementing the signposting Fawcett recommends.

Our third contribution sits slightly outside Fawcett’s frame but strengthens the recommendations: the emotional and relational labour GTAs absorb, for example, patience, diplomacy, and responsiveness to ‘unexpected situational cases’, is central to how participants experienced the role, even where it goes unnamed in formal role descriptions and training. This matters for career-readiness in a very practical sense. If pastoral and interpersonal skills are what GTAs are actually developing, naming them explicitly, alongside ‘research’ or ‘teaching experience’, would help doctoral candidates translate what they already do into what they can put on a CV. This lends weight to our suggestion of a competencies glossary, and points to a further opportunity that Fawcett’s supervisor-focused recommendations open up: module conveners, disciplinary leads, and GTA programme teams each hold partial knowledge of what a given candidate has developed, and bringing this knowledge together, alongside continued investment in careers advice, could be a valuable and immediately actionable step for institutions. 

Fawcett’s timely report creates an important point of departure for conversations about career readiness and stakeholder provision. It highlights the myriad resources available to doctoral candidates across the academy, whilst gently drawing attention to areas in need of development. Bringing this together with our study of GTAs offers a more holistic view of careers following doctoral study. Together, the studies describe support structures and the opportunity for work experience, thus initiating a conversation about translation and transition. Instead of asking what additional support is needed, we should emphasise supporting doctoral candidates to articulate the capabilities they are developing.

Dr Kristy Campbell is a Lecturer in Education (GTA Development Programme Lead) at King’s Academy, King’s College London, supporting staff with continued professional development through workshops and professional accreditation. She is also Unit Lead for the PGCHE and MA Creative Education courses at Norwich University of the Arts. Kristy completed her PhD at UCL Institute of Education, specialising in Higher Education. Her interests include adult teaching and learning, transition, and identity. Kristy is also Co-Editor-in-Chief of Postgraduate Pedagogies, a developmental journal dedicated to showcasing the experiences of GTAs.

Dr Kaiyun Feng is a Lecturer in Education at King’s College London, specialising in higher education policy, institutional management, academic identities and professionalisation, and impact and evaluation. Previously a Researcher at the University of Cambridge, she analysed access and participation and evaluated widening participation projects. She earned her PhD from UCL Institute of Education, investigating institutional strategies for undergraduate teaching in world-class universities across China, the UK, and Canada. She leads master’s and undergraduate seminars on social policy, higher education management, and research engagement. Her evidence-based teaching is informed by her research into the teaching-research nexus and academic career development.


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The technical turn and what it means for higher education

by Tim Savage

When I began my career as a photography technician in higher education 25 years ago, the distinction between academic and technical work was clear and helpful. Academics were thought of as the ‘master practitioners’ who taught creative and professional practice, while technicians prepared facilities, maintained equipment, and helped students to use specialist resources safely. However, as my career progressed, I observed the boundaries between academic and technical roles and responsibilities becoming increasingly indistinct and porous.

In my most recent role, as Director of Technical Learning at a leading UK arts university, I led a technical team of 130 staff whose remits became deeply involved in teaching and learning; many gained teaching qualifications and sought professional recognition, and it became increasingly common for technicians to transition their careers into academia, particularly in disciplines driven by new and emerging technologies.

Technical duties at my institution evolved informally to include writing and delivering progressively developmental lesson plans aligned to module outcomes, devising innovative and differentiated ways of teaching advanced disciplinary skills, collaborating with students to define and solve complex problems without precedent, contributing to feedback and assessment, and helping students develop both the skills and mindsets to succeed in their studies and to flourish in their lives and careers beyond university.

The scale and impact of pre-planned technician-led teaching was evident in the timetable and in the quality of work presented for assessment, but remained much less visible during periods of self-directed study, when students sought personalised advice and guidance at the point of need, sometimes seeking assistance to realise an idea, or having reached the limits of their knowledge, or wanting to understand why something in their work had failed or resulted in an unanticipated outcome. Anecdotally, and through formal feedback channels, students consistently reported these unstructured exchanges with technicians as being among the most valuable and impactful pedagogies they experienced, both in terms of the quality of their outputs and in their confidence and abilities to enter their chosen careers.

The workforce, it seemed, was changing. Academic roles were becoming increasingly focused on theory, research, and curriculum development, while technicians took on more responsibility for teaching practical skills. In this new model, technicians increasingly found themselves positioned as the new ‘master practitioners’ holding specialist knowledge and occupying the pedagogic spaces their lecturer colleagues had previously held.

From my position within the university executive, I saw this change driven by role creep rather than strategy, and neither the operating model that funded it nor the structures or policies used to describe, manage, and quality-assure it kept pace. Furthermore, despite the clearly transformed roles and responsibilities, it remained problematic for technicians to claim that they teach.

My new book, Technical Teaching in Higher Education: Insights from the Creative Arts, examines and attempts to make sense of this shift, exploring the rich and textured pedagogies involved, and considering how institutions might use that understanding to improve teaching and learning.

Making sense of the technical turn

In my research, I describe the growing educational responsibilities of technicians as a ‘technical turn’. Within creative arts HE, I attribute this evolution to a combination of political, economic, social, and technological factors shaping the sector, accentuated by the incorporation and mergers of art schools and former polytechnics into universities.

Susan Orr theorises this shift in arts education as an ‘academic turn’ in which institutional missions and the hierarchies of knowledge taught and learned changed. Previously essential practical, tacit, and technical knowledge and skills became deprioritised in the curriculum, even as they remained central to student learning, experiences, and graduate outcomes. Jonathan Harwood’s concept of ‘academic drift’ offers a way of understanding these changes, which some critics associate with the loss of the traditional art school.

But neither the technical nor the academic turns, nor the role convergences they have enabled, are new or restricted to the creative arts. In 1997, the National Committee of Inquiry into Higher Education identified how the formerly clear distinctions between academic and non-academic roles were becoming increasingly blurred across the sector.

More recently, the UKRI-funded TALENT Commission on which I served provided evidence of the extent of technical involvement in teaching. Its national survey found that 95% of creative arts respondents were involved in teaching activities, compared with 75% of respondents from other disciplines. Subsequent studies have illuminated the scale and breadth of technician teaching across UK universities. These findings show that technical teaching extends across the sector, although how technicians understand and approach this work remains comparatively under-examined.


Five conceptions of technical teaching

My doctoral research addressed a related question: how do visual arts technicians understand and approach their teaching? Using phenomenography, I explored variation in participants’ conceptions of their pedagogies. I interviewed technicians across the UK in different kinds of institutions, spanning art, craft, design, and media disciplines. I examined what participants believed they taught, how they taught it, and why. I identified five approaches: Demonstrator, Instructor, Consultant, Collaborator, and Transformer. Each describes a different way of understanding and approaching teaching, and participants drew on several of these approaches depending on the learner and the context.

Demonstrators see their role as introducing learners to safe and competent practice. Their teaching makes equipment, materials, software, and specialist environments accessible through clear explanations and repeatable processes. They use didactic methods, while prioritising the clear understanding of foundations and conventions from which students can begin to work independently.

Instructors help students acquire, develop and apply skills in unique and personalised ways. Their emphasis moves towards adapting techniques to different purposes and developing the practical judgement needed to use them. Learning how to operate a camera, for example, becomes less about controls and functions, and more about understanding how its use can serve a photographic intention.

Consultants see themselves as subject authorities, whose role is to respond to a learner’s particular problem or ambition. A student may arrive with an unsuccessful process, a technical problem, or an idea they cannot realise. Teaching involves diagnostic assessment, questioning, and advice that help the learner evaluate possible solutions, evaluate risks and benefits, and make informed decisions.

Collaborators work alongside students, sharing in their processes of exploration and discovery. Knowledge develops through dialogue, experimentation, iteration, and encounters with materials or processes. These interactions and exchanges can take both student and technician beyond their existing understanding, supporting unknown and uncertain outcomes.

Transformers focus on changes in how learners understand their practice and themselves as practitioners. Their aim is to challenge and expand what students believe they can do, how they approach their work, and to broaden their ambitions and move beyond perceived limits. Transformative pedagogies were also found to influence curricula, more effectively integrate with academia, and inform institutional approaches to practical learning.

These five qualitatively distinct conceptions span a spectrum from teacher-centred content delivery to student-centred, transformative learning. They describe ways of understanding technical teaching, and an individual may adopt different pedagogies at different times. The range was broadly comparable with that identified in research on academics’ conceptions of teaching.


Putting the framework to use

The taxonomy’s practical value lies in making these different educational approaches and purposes more explicit. For individual technicians and their managers, it can provide language to reflect on practice, discuss teaching with peers, and identify development needs. It can also help educational developers connect teaching qualifications, mentoring, and professional recognition, such as Advance HE Fellowships, using authentic technical teaching and learning scenarios and case studies.

For curriculum teams, the framework can help identify where technical learning can be deployed most effectively within a programme and aligned with learning outcomes and assessment criteria. An equipment induction may prepare students to enter a workshop, but subsequent projects may depend on consultation, experimentation, and feedback.

Quality assurance and enhancement follow the same logic. Peer observation, student feedback, and shared reflection could help teams understand how technical pedagogies contribute to learning and where they need improvement.

For senior leaders, a clearer understanding of who teaches the curriculum and how is essential to linking strategy and operations. The academic/technical polarisation of the workforce is becoming increasingly unsustainable, prompting legitimate but challenging questions about workload planning, staffing structures and cost, and curriculum development. Such debates are likely to become more urgent as universities increasingly describe technician pedagogies as evidence of institutional teaching excellence, prioritise technical expertise and skills teaching, and place both at the centre of graduate outcomes and employability agendas.

The framework offers an evidence-based starting point for these conversations at a sector level; for institutions, it can help improve the integration of academic theory and technical practice within a constructively aligned curriculum, strengthen collaboration and understanding across role boundaries and break down longstanding silos in support of student education, experiences, and outcomes.

Dr Tim Savage is a consultant, author, and trainer specialising in technical learning, technicians, and institutional change in higher education. He previously served as Director of Technical Learning at the University for the Creative Arts and is a Principal Fellow of Advance HE, recognised for sustained strategic leadership in technical learning and teaching. His latest book, based on his doctoral research, is Technical Teaching in Higher Education: Insights from the Creative Arts (Routledge, 2026)