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From TPS to USS – is now the time to move pension scheme?

by John Ingoe

Amid increasing financial pressures, a number of universities are considering a move from the Teachers’ Pension Scheme (TPS) to the Universities Superannuation Scheme (USS) for their teaching staff. In this blog post, I take a deep dive into each scheme to explore whether this makes sense.

On the surface, moving university teaching staff from the TPS to the USS seems rational. USS employer contributions are currently 14.5% of salaries, compared with a TPS employer rate of 28.68% of salaries. Even if we also allow for differing benefit structures, we can say that both offer valuable Defined Benefit (DB) pensions. But is cost the only consideration? In this blog post I will argue that it is not…

How USS and TPS work

Let’s take a step back and consider how each scheme operates. While both are DB in nature, they are fundamentally different beasts.

USS – an overview

USS is a multi-employer, private sector, funded hybrid scheme – meaning that it offers both DB and Defined Contribution (DC) benefits – and is managed by trustees. It currently provides members with a DB pension of 1/75th of salary (plus a lump sum of 3/75th) up to the salary threshold, as well as DC benefits on salary above that level.

The contributions paid by both employers and employees are invested, and pensions are paid out of the assets held. Under funding regulations that apply to the scheme, the trustee carries out a funding valuation every three years, or more quickly if needed, with the next one due as at 31 March 2026.

The results of those funding valuations determine the employer and employee contribution rates that will be paid between each valuation cycle – although the process isn’t quite as straightforward as that suggests. The 2026 valuation will determine the contribution rates payable from 2027; these will be reviewed again following completion of the 2029 valuation.

TPS – an overview

TPS is a public service, unfunded, pay-as-you-go DB scheme. It offers members a DB pension of 1/57th of salary. Contributions paid by employers and employees are used to pay current pensioners, with HM Treasury meeting the balance.

TPS isn’t governed by the same funding regulations as USS, but a funding valuation is carried out every four years, in a similar way to USS.

These valuations determine the employer contribution rate. The next valuation is due with an effective date of 31 March 2024 (yes you read that right), with the results of that valuation likely (but not certain) to result in changes to the employer contribution rate from 1 April 2027.

A brief history of the USS

Volatile contribution rates, benefit changes and industrial action

Since April 2016, USS has experienced repeated shifts in contribution rates and benefit design, with the latter often used as a lever to mitigate the former. Employer and member contribution rates rose between 2016 and 2023, before falling significantly in 2024, as shown below:

Effective dateEmployer rateMember rateTotal
April 201618.0%8.0%26.0%
April 201919.5%8.8%28.3%
October 201921.1%9.6%30.7%
October 202121.4%9.8%31.2%
April 202221.6%9.8%31.4%
January 202414.5%6.1%20.6%

However, the above table somewhat downplays both the impact of the 2020 valuation and the contribution rate increases that would have occurred had significant changes to benefits and the valuation methodology not been made. The schedule of contributions dated September 2021 would have seen contribution rates increases to 38.2% of salaries for employers and 18.8% of salaries for employees.

In the end, benefits were reduced to avoid this. From April 2022, this resulted in:

  • A lower rate of future pension build-up
  • A reduced salary threshold, which determines the split of DB and DC benefits earned
  • Capped inflation protection.

The outcome of the 2020 valuation was not particularly well received by either members or employers. The majority of these changes were reversed in 2024.

Impact of financial markets on USS

The USS employer rate is essentially determined by the size of any funding shortfall or surplus, and the cost of future benefit accrual. Changes in the value of pension scheme liabilities and the cost of future benefits are driven by changes in financial markets, and in particular bond yields. Changes in asset values depend on the actual assets held, and these will often move in different ways from the liability value. There can be significant swings in the funding position as a result of this mismatch.

This volatility was demonstrated in the valuations carried out between 2018 and 2023. A funding shortfall of £3.6bn in 2018 increased to £18.4bn in 2020 but then flipped to a surplus of £7.4bn in 2023. These changes were predominantly driven by unprecedented changes in bond yields, which went from c1.7% (2018) down to c0.7% (2020), and then up to c3.8% (2023).

While there were several second order changes (changes to demographic assumptions; changes to the funding target; member experience; asset performance), these tended to be drowned out by the impact of changing bond yields.

A brief history of the TPS

Sharply increasing contribution rates

TPS employer rates have risen sharply since 2019, as shown below:

Effective dateEmployer contribution rate
September 201516.48%
September 201923.68%
April 202428.68%
April 2027Next scheduled change

Since 2019, the TPS employer rate has increased by almost 75%. For many employers (including local authority-maintained schools and academies), grant mechanisms have largely cushioned these increases. Our understanding is that post-1992 universities have not been insulated, prompting difficult strategic choices.

While the employer rate has risen significantly, member rates have remained broadly stable over that period, increasing by around 0.3% of salaries.

Impact of lower expectations of economic growth on the TPS

Pension valuations estimate the size and timing of future pension payments using financial and demographic assumptions. Those future payments are then converted to a present value using a discount rate – which can be thought of as an advance allowance for expected long‑term investment returns.

“I thought the TPS held no assets,” you say. Well, you’d be right. But there is a notional pot of assets, as well as a notional shortfall to be made good. That may be one for another blog, but it does underline that the discount rate remains a key assumption.

A lower discount rate means more money must be set aside today, making pension costs more expensive. Even relatively small movements in the discount rate can have a big impact on contributions. For unfunded public service schemes such as the TPS, the discount rate is the SCAPE rate (Superannuation Contributions Adjusted for Past Experience) – which is linked to long‑term UK GDP growth, as forecast by the Office for Budgetary Responsibility (OBR).

As long‑term GDP forecasts fell over the past decade, the SCAPE rate also fell. This has been the principal driver of higher TPS employer contribution rates, outweighing the impact of other valuation assumptions.

How costs might change in the future

In both schemes, contribution rates are determined by factors beyond the control of sponsoring employers. The table below sets out the key influences on the costs of each scheme:

DriverTPSUSS
Economic assumptionsCosts are driven by the SCAPE discount rate, which is linked to long-term UK GDP forecasts.Liability values can be sensitive to long-term bond yields and inflation expectations.
Investment performanceNo impact as TPS holds no assets. Its notional pot of assets increases in a prescribed way.USS investment performance directly affects funding position. It can move differently from liabilities.
Benefit designBenefits are set out in legislation. Short-term changes are unlikely.Benefit structure directly affects cost of accrual. Changes have been made to avoid large contribution increases.
Demographic trendsLongevity and other member behaviours influence valuations.Longevity and other member behaviours influence valuations.
Regulation and governanceKey valuation assumptions are set by HM Treasury, and are potentially subject to political influence.Valuations must reflect regulatory expectations and employer covenant considerations.

Key drivers of the TPS

The key driver of the TPS contribution rate is the SCAPE discount rate. The current SCAPE methodology based on the GDP approach was introduced in 2011 and reviewed ten years later, as scheduled, during the last completed valuation cycle. This framework remains in place unless HM Treasury directs otherwise.

While recent headline GDP growth has been modest, it’s the long‑term GDP forecasts that determine the SCAPE rate. The OBR’s long‑term projections (published in July 2025) show a material improvement compared with those underpinning the current 28.68% TPS employer rate.

If this improved outlook persists, the SCAPE discount rate will rise (potentially materially) and, all else being equal, from April 2027, the TPS employer rate will go down (potentially materially).

So, all else being equal, we might expect the TPS employer rate to fall from its current rate in 2027. But that’s not guaranteed – “all else being equal” is doing a lot of heavy lifting here, and almost never turns out to be the case.

Other valuation assumptions – including salary growth, inflation, longevity and scheme member behaviour – will also influence the result. Perhaps more importantly, long-term GDP forecasts can change – reflecting changing views due to macroeconomic factors or political pressures. And finally, there is still scope for HM Treasury to tweak the SCAPE rate methodology.

Until the HM Treasury valuation direction is published, nothing is set in stone

Key drivers of the USS

There are more moving parts driving the USS employer rate than there are in the case of TPS. Asset performance, changes in long-term bond yields, covenant strength and benefit changes can all alter employee and employer rates. And while past performance is not an indication of the future, we only have to look back to the 2020 valuation to see the possibility of the employer rate exceeding 30% of salaries.

While we don’t expect to see material changes following the 2026 valuation, there are no guarantees that this will continue to be the case after the 2029 valuation. USS continues to invest heavily in growth assets, so some volatility is to be expected. While the expectation is that these assets will provide higher returns in the long term, a valuation is a snapshot of a moment in time, and a 31 March valuation date often coincides with some financial shock or other.

USS also operates in a regulatory environment that pushes DB schemes to link liability measurements to bond prices. This can result in a funding position (assets v liability value) that can move materially, and any deficit arising from that is likely to result in increased contributions. UCU and UUK are working with USS to put in place a valuation methodology which looks to mitigate this volatility, but this may only soften, rather than remove, future swings in position due to changes in financial markets.

Finally, the higher education sector is facing more financial pressures than ever before. All else being equal, this could lead to a stronger funding target (and therefore increased contribution rates).

Which is better – TPS or USS?

It depends.

Each employer faces a unique set of challenges, and appropriate pension strategies will vary. As with any decision about pensions, it’s about balancing risk and reward. There’s a definite short-term gain for employers from moving to USS (all else being equal), but scheme costs will vary over time. USS may be more cost-effective now, but that may not always be the case. There are signs that the TPS employer rate could fall from 2027 (potentially materially), while costs and benefits in the USS are intrinsically more volatile due to the different funding approach.

In any case, it’s not enough to simply compare the cost of the two schemes. Each offers a different level of benefits for members, at a different cost to members, and with a different perceived value for members.

All in all, not a straightforward decision and one that requires a lot of consideration.

John Ingoe is an Associate Partner and Head of Employer Actuarial Services at First Actuarial, a Gallagher Company. He has over 18 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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Peak Higher Ed: How to Survive the Looming Academic Crisis – by Bryan Alexander

2026, Baltimore, Johns Hopkins University Press, pp195.

Reviewed by Paul Temple

Bryan Alexander is a lucky guy. If his new book had been published a year ago, it would have missed the full impact of the Trump/Vance assault on American higher education, and so would now be considered a historical curiosity: “Listen, children, this is a tale of how, in olden days, the government saw our universities as vital national assets with professors who provided our country with knowledge and learning”.

As it is, with what has the feel of up-against-the-deadline editing, Alexander is able to squeeze in references to President Trump’s “extensive campaign against universities and colleges” and report Vice-President Vance’s encouragement “to honestly and aggressively attack the universities in this country” (p12). And yet, at least to this foreign reader, this last-minute editing (if I’m right about that) causes this unprecedented policy revolution to be seriously downplayed, as if it were just another item in the action list that Alexander suggests American universities need to manage, along with demographic change, the impact of AI, climate breakdown, and cultural changes about progressive values.

The difference, though, surely, is that while universities can plan for and perhaps mitigate the impacts of these various overarching concerns – and the book offers a list of plausible actions in response to them – a direct and very immediate attack on institutional autonomy and evisceration of research funding by your own governments, at federal and sometimes state level, believing that they have a popular mandate, is a here-and-now existential crisis

At the very end of his concluding chapter, just one page from the end of the book, Alexander returns to consider this threat and notes the less-than-impressive response to it by a disunited, uncertain American higher education sector. But the book, without seeming to realise it, highlights the problem of creating an effective university response. This is because quite a few of the actions that Alexander proposes to help higher education “[climb] back to Peak” (p139) – embedding climate activism in the curriculum, doing more “to redress historical wrongs” (p154), better recognition of issues of gender identity, and more – are precisely the type of “woke” issues that so enrage the supporters of the Trump/Vance project. Hence my sense that some late editing hasn’t been enough to pull together a pre- and post-Trump analysis of the future of US higher education.

Are there any lessons from this story of national self-harm for those of us not suffering under the Trump/Vance lash? An obvious one is for higher education to cultivate as wide a constituency of supporters as possible, as a protection from populist politicians looking for easy targets. Yet Alexander argues that American universities spend a fortune on communications specialists, lobbyists at national and state levels, and others working in the “academic narrative industry [which is still] apparently falling short” (p141). It has, it seems, not prevailed over the counter-narrative, “that higher education indoctrinates young minds into…transgenderism, cultural Marxism, censorship, and antisemitism” (p142). Assuming that Reform UK is the nearest British equivalent to MAGA Republicanism, its higher education policies (for now) seem to be rather tame and, apart from those aimed at reducing student debt, unlikely to be overwhelmingly popular with younger voters, anyway: reducing overall student numbers, narrowing rather than widening participation, encouraging two-year degrees, and generally telling young people, and their parents, that the government knows best.

What is striking is that Reform does not seem to be taking a culture wars approach towards higher education, nor even a traditional right-wing, small-state line, but plans to involve itself in the detail of higher education organisation. (There’s surely an opening here for a centre-right political response on the lines of, “unlike Reform, we don’t think it’s the government’s job to tell universities what to do”.) We shall have to see how popular this “Robbins got it wrong” line is with potential Reform voters when they realise that it will be their families’ children not going to university.

SRHE Fellow Dr Paul Temple is Honorary Associate Professor in the Centre for Higher Education Studies, UCL Institute of Education.


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From ad hoc to constructive: the ABC levels of GenAI integration in business education

by Qianqian Chai and Xue Zhou

Introduction – the challenge of GenAI integration in business education

Since the release of ChatGPT, Generative Artificial Intelligence (GenAI) has rapidly entered higher education. Business schools, with their strong ties to industry and emphasis on applied skills, provide a particularly important setting for examining GenAI’s role in curriculum design. Yet, while adoption has expanded quickly, the educational outcomes of GenAI integration have not been consistent (Kurtz et al, 2024). Across cases, educators identified both benefits and risks, including engagement, skills development, and overreliance. This unevenness suggests that rather than reflecting a single trajectory of adoption, early practice appears to involve different approaches to integration. The central issue is not whether GenAI is used, but how different approaches shape outcomes.

This blog draws on our recent study of GenAI integration in business modules at a UK Russell Group university (Zhou et al, 2026). Through a qualitative analysis of 17 educator cases across 24 modules, we examined how GenAI was incorporated into curriculum design, and how different approaches were associated with distinct benefits and challenges. Using the lens of constructive alignment (Biggs, 1996), we identified three patterns of integration: ad hoc, blended, and constructive, which together form the ABC levels for understanding how GenAI is integrated into curriculum practice. We use these levels to explore why some approaches appear more educationally effective than others. In particular, this blog will offer research-informed insights into how GenAI can be integrated more effectively and sustainably in business higher education. While the cases are drawn from business education, the patterns identified and principles of constructive integration have wider relevance across disciplines where GenAI is increasingly embedded in curriculum design.

ABC levels of GenAI integration in the business curriculum

Our analysis identified three levels of GenAI integration: ad hoc, blended, and constructive. Table 1 outlines these distinctions across key dimensions.

Table 1 ABC levels of GenAI curriculum integration

Constructive integration represents a qualitatively different approach, grounded in constructive alignment, where intended learning outcomes, teaching activities, and assessment are deliberately designed to develop and evaluate students’ ability to use GenAI critically and effectively. At this level, GenAI is not an optional or supporting tool, but an integral component of disciplinary learning, with a clear pedagogical purpose and coherent role across the curriculum.

By contrast, ad hoc integration is characterised by occasional and isolated use, where GenAI is introduced as an optional or experimental tool without being planned into the broader curriculum design. Blended integration moves beyond this by incorporating GenAI into selected learning activities or tasks, giving it a more purposeful pedagogical role, but its use remains only partially embedded. Both approaches therefore fall short of the coherence and strategic alignment that define constructive integration.

The distinction between these patterns is therefore not simply a matter of more or less GenAI use, but of how GenAI is positioned within the curriculum: as an experiment, as a support, or as a capability to be deliberately developed. Although developed from business education contexts, this typology offers a lens that can be applied more broadly to understand how GenAI is positioned within different disciplinary curricula.

Why constructive integration matters

Across the cases, GenAI integration generated benefits and challenges across students, educators, and institutions. At the student level, reported benefits included stronger engagement, confidence, and employability-related skills, while the main risks centred on overreliance, inequality, ethical concerns, and ineffective outputs. For educators, benefits included efficiency gains, professional learning, and improved teaching performance, but these were accompanied by increased workload and the need to redesign activities and assessments. At the programme level, GenAI enhanced curriculum relevance but raised concerns about academic standards.

Figure 1 shows that these benefits and challenges were not distributed evenly across the three patterns of integration. Constructive integration displayed the strongest and broadest benefits, while ad hoc and blended approaches showed narrower gains alongside more exposed challenges. In other words, the issue is not whether GenAI brings value or risk, but how curriculum design shapes the balance between them.

Figure 1 Trade-offs of GenAI integration: challenges (red) vs benefits (green)

What makes constructive integration different is not the removal of challenge, but the stronger presence of educational value. In the study, constructively integrated cases were linked more clearly to student engagement, capability development, employability, and curriculum relevance because GenAI was embedded through aligned outcomes, activities, and assessment, rather than added on as a tool or support. Importantly, these cases also showed stronger educator development, including pedagogical reflection and confidence, despite workload pressures. This suggests constructive integration enhances both student outcomes and educator learning by embedding AI within coherent curriculum design.

How constructive integration is achieved

Table 2 presents examples from the modules in this study, showing how GenAI was constructively integrated into existing pedagogical strategies without requiring curriculum redesign.

Table 2 Constructive GenAI Integration into Existing Pedagogical Strategies

Taken together, the cases suggest several practical principles for integrating GenAI more coherently within the curriculum. These principles are not specific to business education, but reflect broader curriculum design considerations that can be adapted across disciplines with different pedagogical traditions.

  • Integration builds on existing pedagogical strategies: GenAI should be embedded within approaches already familiar to the discipline, such as project-based or simulation-based learning, without requiring curriculum redesign (Chugh et al, 2023).
  • Sharpen the role of GenAI by disciplinary purpose: In different contexts, GenAI supported strategic analysis, research and synthesis, reflective thinking, or data interpretation. Its value depends on alignment with module aims (Zhou & Milecka-Forrest, 2021).
  • Make AI use purposeful through assessment and evaluative tasks: In stronger cases, GenAI was connected to tasks that required students to interpret, justify, compare, or critique AI-supported outputs, rather than simply using AI to complete tasks (Biggs & Tang, 2010).
  • Support deeper student engagement through scaffolding: Structured guidance, such as prompting strategies, comparison activities, and reflective tasks, enabled more critical and purposeful use (Cukurova & Miao, 2024).

Overall, constructive integration is less about introducing new tools than about redesigning existing curriculum elements so that GenAI is meaningfully aligned with disciplinary learning.

Conclusion

The ABC levels developed in our study show that GenAI integration in business education does not follow a single trajectory but ranges from ad hoc and blended use to constructive integration. The key difference lies in approach: constructive integration embeds GenAI through aligned outcomes, activities, assessment, and scaffolding. The challenges observed across GenAI integration practices suggest an urgent shift from ad hoc GenAI integration toward strategic and constructive integration in business education. In this way, higher education can support students’ employability and capability development, strengthen educators’ professional and pedagogical confidence, and enable institutions to sustain coherent, future-facing curricula.

Dr Qianqian Chai is a Lecturer in Business and Management at Queen Mary University of London and Chair of the AI in Education Innovation Sub-committee in the School of the Arts. Her research focuses on AI in higher education, including curriculum design, academic integrity, and policy. q.chai@qmul.ac.uk

Professor Xue Zhou is a Professor in AI in Business Education and Dean of AI at the University of Leicester. Her research interests include digital literacy, digital technology adoption, cross-cultural adjustment, and online professionalism. xue.zhou@le.ac.uk


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In defence of SoTL: anchoring educational evaluation and educational research

by Liz Austen

By the end of 2025 I had attended three HE related conferences: Euro SoTL, the Wonkhe Festival of HE and the SRHE Annual Conference. I presented on similar topics at all three events; what evidence do we generate to help us understand and act to enhance student experiences and outcomes in higher education? During the Wonkhe panel and my SRHE session, I defined two approaches at the disposal of HE practitioners:

Higher Education Evaluation: an approach which helps to understand and explore what works and doesn’t work in a given context and is of value to stakeholders. The aim of evaluation is to generate actionable evidence-informed learning, which encourages, informs and supports continuous improvement of process and impact (Evaluation Collective 2025)

Higher Education Research: to extend knowledge and understanding in all areas of educational activity and from a wide range of perspectives, including those of learners, educators, policymakers and the public (adapted from BERA, 2024)

At the Wonkhe panel, Clare Loughlin-Chow (CEO of SRHE) helpfully outlined the higher education research topics that were most prevalent in the SRHE journals. Omar Khan (CEO of TASO) then outlined the scope and priories of TASO, an affiliate member of the government’s What Works Network which focuses on higher education evaluation. My conceptual discussion of evidence generation brought the two together.

At EuroSoTL earlier in the year, my colleagues and I outlined our new institutional approach to the Scholarship of Teaching and Learning:

Scholarship of Teaching and Learning (SoTL): to improve student learning through engagement in the existing knowledge of teaching and learning, developing contextual ideas and innovation in practice, reflecting on practice, applying methodological rigour, working in partnership with students, and sharing of scholarship publicly (adapted from Felton (2013))

When I attended SRHE in December 2025, SoTL appeared in only one session I attended and some of this discussion focused on the challenges of bringing SoTL into spaces for educational research. My hand in the air comment – that criticism of SoTL by educational researchers was an example of ‘academic snobbery’ – certainly raised a few eyebrows. This blog post considers the relationship between these three approaches and whether, for the good of our students, it’s time for some reconciliation.

Educational evaluation, SoTL and educational research

Educational research in higher education has developed over the last 60 years. Interestingly, research into teaching and learning is cited as the most theorised by this type of research (Tight, 2012). Higher education evaluation, sometimes considered as applied research, was recently propelled by the Office for Students’ agenda to ‘evaluate, evaluate, evaluate (Office for Students, 2022). SoTL has developed alongside HE research and evaluation, emerging from Boyer’s work in 1990.

The aims of each endeavour are distinct, tied by the notion of ‘enquiry’. Research seeks to build new knowledge, and evaluation seeks to provide judgment on a contextual problem. SoTL has a narrower focus on teaching and learning than the broader scope of research and evaluation but incorporates prior knowledge and contextual problem solving through focused enquiry (Gray, 2025). SoTL builds on the foundation of social sciences methodology and can integrate disciplinary methodology into practitioner’s teaching and learning enquiry (Riddell, 2026). Educational evaluation often asks questions of the effectiveness of interventions, but in some teaching and learning spaces, the evaluative language of ‘intervention’ isn’t appropriate (Austen (2025) in Austen and McCaig (2025)). Exploring what works through SoTL enquiry aligns better. Often the bridging term ‘pedagogic research’ is used as integral to SoTL (close to practice) but distinct from educational research (broader anticipated impact). Our chosen SoTL definition uses neither research nor evaluation terminology, but has component parts – knowledge, innovation, method, dissemination – that are central to all.

The essential agents in educational research, evaluation and SoTL are the same – individual students (as partners, as participants and as voice givers), individual staff (academic and professional services), institutional groups or clusters, collaborating HEIs, and third space organisations. Reasons for enquiry are also similar and include sector expectations and shared learning, the desire for institutional enhancement and impact, personal development and career progression. Or as Ashwin & Trigwell (in Evans et al, 2021) note:  to inform a wider audience; to inform a group within context; to inform oneself. All research, evaluation and SoTL agents must navigate the practical and ethical considerations of ‘insider’ enquiry if they are exploring their own practices or within their institutional contexts (BERA, 2018; Barnett & Camfield, 2016).

Output pathways are also interconnected. The SoTL staircase (Beckingham, 2023) recognised the variety of outputs encouraged by SoTL and includes those traditionally aligned with research and evaluation (reports and journal articles). Research outputs may be guided by REF criteria, and evaluation outputs by readership. The conclusions in research articles frequently state that more research needed, and evaluation reports often sit unread in metaphorical desk draws. In comparison, SoTL practitioners benefit from publications which are close to practice, quicker to publish, and more likely to influence change.

Both educational evaluation and educational research are inherently theoretical, grounded by educational or pedagogic theory or a theory of change. SoTL is more action focused, less theoretical than research yet can be more exploratory than evaluation. In 2011, Kanuka questioned SoTL’s credibility due to the lack of theoretical underpinning or reference to existing scholarship. At times, I suggest that educational research can be positioned too far in the opposite direction. The presentations at SRHE were heavily theoretical and sometimes I was left thinking ‘so how would this work actually improve the learning experiences of students’? In contrast, the breadth of SoTL includes both theory and action, albeit in more pragmatic ways.

There are values and specific skill sets of educational researchers and evaluators (and often epistemological disagreements occur between the two). This commitment to identity can be excluding and may help to understand why SoTL has been challenged. Canning & Masika (2022) caution us on the ‘threat to serious scholarship’ posed by SoTL, which they believe risks devaluing research into higher education learning and teaching. Their criticism of ‘anything goes’ I would frame as an important approach to inclusion. Their criticism of the ‘watered-down version of teaching and learning research’ I frame as SoTL’s recognition of the developmental, particularly in building staff confidence. Where confusion over definitions and scope still occur, I question whether institutional SoTL has been well grounded or well led.

Conclusion

There is clearly a divide between higher education research and SoTL. There are few recent SRHE blog posts which reference SoTL at all and one that does advises against flag-in-the-sand nomenclature (Sheridan, 2019). Having spent a lot of time in these circles, I believe higher education evaluators are more agnostic, but I include them in this discussion as they bring a new dynamic to this debate.

In this blog I have identified the ways in which research, evaluation and SoTL have their own agendas and yet have much in common. I argue that SoTL emerges as a grounding anchor between higher education research and higher education evaluation. SoTL borrows from both. SoTL feeds into both. SoTL is more than both (Potter, 2025). SoTL’s inherent value is the ability to build a community which improves student experiences and outcomes in an enquiry led and timely way.

For more details on the approach to SoTL at Sheffield Hallam University see: https://lta.shu.ac.uk/scholarship

Reference

Riddell, J (2026) ‘Hope circuits in practice: how the scholarship of teaching and learning fuels pedagogical courage and systemic change’ Guest Lecture, Sheffield Hallam University

Liz Austen is Professor of Higher Education Evaluation and Associate Dean Learning, Teaching and Student Success at Sheffield Hallam University. She has worked as an independent Evaluation Consultant on HE sector contracts and is a regular keynote speaker on all things evaluation in HE. Her focus is on evidence informed practice across the student lifecycle. Liz also leads a cross sector HE network called the Evaluation Collective.


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End of the road for higher education student loans?

by Gavin Moodie

Although we’ve come to the end of the road

Still, I can’t let go

As an expat Aussie I have been sad to see the unremitting erosion of public support for what is arguably Australia’s most innovative modern higher education export, income contingent student loans. While Australian student financing may not yet be as ‘unsustainable’ as England’s, the former Labor leader and current vice chancellor of the University of Canberra Bill Shorten argued that Australian universities are in a ‘political cul-de-sac’, with ‘a tired funding model’.

This blog seeks to understand how Australia’s higher education finance reached its Neighbourly cul-de-sac Ramsay Street, why it is perhaps not yet quite unsustainable, and the difficult choices confronting policy makers over the next 5 to 10 years in Australia, England, and elsewhere in the UK.

Introduction and early years: 1989 – 1996

The national Australian Labor Government introduced substantial tuition fees accompanied by income contingent loans in 1989 on the recommendation of the Committee on Higher Education Funding chaired by the astute late Neville Wran, former Labor premier of Australia’s biggest state, New South Wales. A consultant to the committee was Bruce Chapman, an advocate for income contingent loans for higher education and a range of other public policy problems such as farmers’ drought relief and penalties for insider trading and other white collar crimes.

The Wran committee recommended that total student charges should be about 20% of estimated costs in 8 categories of disciplines, which it aggregated into 3 contribution levels. While the Government agreed that student contributions should be about of 20% of system costs, it introduced a single price that avoided the complexities of students paying different rates for different subjects, and the possibility that higher charges may discourage students from enrolling in higher cost courses.

The Wran Committee also recommended an employers’ training levy, which was similar to the UK’s apprenticeship levy except that it could be spent on any form of employee training. Australia’s training guarantee was reasonably successful, but it was vociferously opposed by at least some employers and the Government discontinued it in 1994 after only 4 years of operation. This is consistent with employers’ long term substantial cuts to their induction and development of their own employees in Australia and Canada, as well as the UK.

Government charges by cost and expected income: 1997 – 2004

In 1997 the newly elected conservative Australian Government cut funding to higher education and increased student charges to about 40% of the presumed cost of higher education. The Government established 3 bands of student charges based on a combination of the presumed cost of subjects and graduates’ expected earnings.

In the lowest band were humanities and social sciences that were funded at a lower rate and whose graduates had lower earnings. Also in band 1 were languages and the creative arts. These were higher-cost disciplines, but most graduates’ earnings were lower than average.

In band 3 were disciplines with high costs and high graduates’ earnings: dentistry, medicine, and veterinary science. Also in band 3 was law: it was a low-cost discipline but graduates had high earnings. Band 3 charges were 1.7 times band 1 charges. All other disciplines were in band 2 which were charged 1.4 more than band 1: health, science, and engineering because they were high cost; and business because graduates had high incomes.

University fees by cost, expected income, and Government priorities: 2005 – 2020

From 2005 the Conservative Government changed student contributions from Government charges to institutions’ fees, and established four maximum fee amounts. It introduced a new fee band for the expensive STEM disciplines and for business which is funded at the base rate but has high graduate incomes, though lower than law which was still in the top band.

The Government also published government contribution amounts in 12 bands. The combination of maximum fee amounts and government contribution amounts gave total financing in eight bands. Agriculture, dentistry and medicine were in the highest financing band, which was 2.6 times the lowest band for business and humanities.

The Government also sought to influence students’ behaviour by setting low maximum fee amounts and to influence institutions’ behaviour by setting higher government contribution amounts for the ‘national priority’ disciplines of education and nursing. Institutions’ total revenue for education was 1.2 times the base rate and 1.5 times for nursing.

Job-ready graduates: 2021 –

In 2021 the then Conservative government further cut higher education funding and increased maximum student fees to be a weighted average of about half of total teaching financing, although this differs markedly by discipline, as we shall see. The Government also extended its attempts to influence both students and institutions’ behaviour with financial incentives intended to create ‘job-ready graduates’.

The Government set the lowest student fees for agriculture, education, English, foreign languages, mathematics and statistics, and nursing. It added humanities and most social sciences to business and law in the top fee rate of 3.7 times the base rate. Humanities and social sciences students now pay annual fees 28% higher than dentistry and medicine students, whose maximum fees are 2.9 the base rate.

The Government’s contribution is lowest for business, humanities and social sciences, and law; it is highest for agriculture, dentistry, medicine, and veterinary science, which is 24.6 times the base rate.

The combination of maximum student fees and Government contributions generates total financing for dentistry, medicine, and veterinary science 2.5 times the base rate for business, humanities and social sciences, and law. Total financing for engineering and science is 1.6 times the base rate.

Students pay markedly different proportions of the total financing for their courses. Business, humanities and social sciences, and law students pay 93% of the total financing of their course; engineering, science and medicine pay around 30% of their course’s financing; and education, languages, mathematics, and nursing students pay around 20% of their course’s financing.

These differences are widely considered unfair. It is also doubtful that they have changed students’ and institutions’ behaviour as they were designed to. Humanities and social sciences enrolments have fallen since the introduction of job-ready graduates, but that has continued a long established trend likely influenced by other factors such as prospective students’ interests and perceptions of employment prospects.

Enrolments in English and other languages have fallen even more than the humanities and social sciences, despite the government cutting their fees by 40%. An econometric study concluded ‘Overall, we estimate that the studied policy change led 1.52% of students to demand courses they wouldn’t have demanded under the old fee structure’.

This is entirely consistent with economic theory and Australia’s experience with its previous changes to students’ fees. The whole point of income-contingent loans is to insulate students from the up-front price of education, and that is just what they do, even when humanities’ students fees were increased by 113% and creative arts students’ fees were increased by 64%.

The Australian Labor government was elected in 2022 on a platform that included reversing job-ready graduates, and it was re-elected in 2025 with the same commitment. Yet Labor has kept job-ready graduates for longer than the previous conservative government, to the intense annoyance of many students and staff.

Debts, interest rates, return on investment

The size of Australian Government debt was a concern in the early years of income contingent loans when enrolments and thus accumulated unpaid debt was growing strongly and there were relatively few graduates yet in well-paying jobs repaying their debt. It is not such a big concern now: outstanding student debt is equivalent to 8% of all Australian government debt, which is around 50% of gross domestic product (in contrast to the UK where government debt is 101% of GDP).

The proportion of student debt not expected to be repaid increased from 16% to 25% from 2010 to 2016. However, this is sensitive to repayment conditions, and for 2024 the proportion of new debt not expected to be repaid was 12%.

The size of students’ debts has been concerning. Graduates’ average debt is currently about 30% of average annual earnings and takes just over 10 years to repay. But this varies greatly by individual circumstances. We have seen that the Government has set the maximum fee for arts subjects in the top band, meaning that arts graduates are likely to incur a total debt of half average annual earnings. Arts graduates have lower incomes than other graduates, and many are women who work part time at times during their career. Many are likely to take up to 40 years to repay their debt, if at all.

Graduates’ expected earnings was one of the Australian Government’s criteria for setting maximum student fees, and that remains the only explicitly progressive part of Australia’s student loans. The Australian Government charges interest on student debts, but only to preserve the debts’ real value. Australia does not charge higher income earners higher interest on their student debts, although they may have to repay their debt more quickly than lower paid graduates, as higher paid graduates are required to repay higher amounts each year than lower paid graduates.

Nevertheless, as in England, during a period of high inflation there has been controversy over which of the several measures of inflation to use, and when indexation should be assessed. Also as in England, there have been reports of new graduates’ annual repayments not even covering annual interest charges so their debt continues to increase. Accordingly the Labor Government promised to make repayment conditions more favourable to students, and to cut graduates’ debts once by 20%. Cutting graduates’ debt has been popular, despite being arbitrary and regressive, and arguably contributed to Labor’s re-election in a landslide in 2025.

Despite Australian students’ concerns about fees, debts, and interest rates, graduates have high economic returns, although these vary by gender and discipline.

Substantial differences from England

Further substantial differences between Australian and English higher education have important implications for student fees and loans in each country. The Australian Government retains student number controls. It removed number controls in 2012, some three years before most student number controls were removed for England in 2015/16. Australia introduced its so-called ‘demand driven system’ after a period of pent up demand for higher education, which saw very big increases in enrolments as enrolment caps were removed.

At the time the Australian Government provided about 60% of the financing for each student place, and of course it provided all of the up-front funds for student loans, so the demand driven system substantially increased Government spending on higher education. This was too much for the Australian Government, which ended the demand driven system and reintroduced number controls in 2017. One of the outcomes is that the Australian Government may limit increased expenditure on higher education by limiting its expansion, and not just by worsening students’ loan conditions.

Most Australian higher education students live with their parents. Nearly 80% of Australian higher education students commute from home, even to elite universities. While the proportion of UK 18-year-olds commuting from home is increasing, it is still only 30%. Living in purpose built student housing is a very different experience from commuting from home, and is probably one of the reasons for the UK’s unusually and commendably high student retention and completion rates. Commuting rates also has implications for institutions’ range of programs, which need to be reasonably comprehensive to meet the needs of local students. But a great advantage of commuting is that it greatly reduces students’ living costs, and thus their need for income support.

Universities’ social licence

An important limitation of Australian universities’ financing is their erosion of their social licence. A longstanding concern has been with Australian vice chancellors’ very high pay, amongst the highest in the world for public universities. The average Australian vice chancellor’s pay is almost double the Prime Minister’s. More recently there has been concern at the number of highly paid executives employed by universities: ‘More than 300 Australian university executives make more money than state premiers’. Closely related are complaints at universities’ changed governance, known broadly as the imposition of managerialism.

The very high pay and conditions of Australian universities’ senior executives is in almost feudal contrast to the very high number of academics they engage on precarious employment conditions. Australian universities’ staffing data collection and reporting are very weak on this issue, but the union estimates that about 45% of public Australian university employees are on casual contracts. This is related to widespread underpayment of casual staff.

As in the UK, Canada, and elsewhere, Australian universities have relieved their public funding pressures by recruiting high numbers of international students. Some 35% of Australia’s higher education students are international, 80% of whom live in Australia on a temporary entry permit. There are the familiar concerns that Australian universities lower standards to recruit and graduate large numbers of international students who crowd locals out of accommodation, all to fund senior executives’ lavish pay. Accordingly, the Australian Government is cutting the number of international students. Regardless of the merits of these arguments, there is little public sympathy for increasing universities’ funding from their current three main sources: government grants, domestic student fees, and international student fees.

Difficult choices for policy makers

The late great sociologist of higher education Martin Trow observed that:

“No society, no matter how rich, can afford a system of higher education for 20 or 30 percent of the age grade at the cost levels of the elite higher education that it formerly provided for 5 percent of the population.”

That observation applies equally as we transition to universal participation of more than 50% in post-school education, from mass participation of from 16% to 50% which our countries financed by income contingent loans. That is to say, the current pressures on higher education financing will not be relieved, as some have suggested, just by cutting the number of senior university administrators and their pay, allocating more funds to higher education from increasing taxes on the rich or on companies, or by increasing student fees.

I suggest that there are two main options. The most frequently suggested, especially in England, is to retreat from universal and even mass participation in higher education by cutting greatly the number of higher education students. A second commonly suggested option is to cut radically the cost of providing higher education.

Advocates of each option need to address two consequential issues. To what extent would the much smaller or cheaper system retain stratified elite, mass and universal parts, as Trow envisaged? Secondly, how would access to the elite, mass and universal parts of the system be allocated? I would answer these questions by considering the relative importance I would give to egalitarianism, and to expensive forms of higher education such as research intensity.

Gavin Moodie is Honorary Research Fellow, University of Oxford Department of Education. He worked at 6 Australian universities over 38 years. @GavinMoodie. https://www.researchgate.net/profile/Gavin-Moodie


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Digital accessibility debt in higher education: how institutional decisions create structural exclusion

by Kevin Andrews

Digital accessibility in higher education is often discussed as a compliance requirement. Universities are expected to meet legal duties, publish accessibility statements, and make reasonable adjustments when barriers arise. Over the last decade the policy environment has strengthened considerably. In the UK the Equality Act 2010 established obligations to remove barriers for disabled students and staff, while the Public Sector Bodies (Websites and Mobile Applications) Accessibility Regulations introduced additional scrutiny of institutional digital services. More recently the Jisc Accessible Digital Futures project has highlighted the opportunities created by emerging digital and AI technologies when accessibility is considered early in design and procurement.

These developments signal an important shift. Accessibility is no longer treated solely as a matter of compliance or accommodation. Instead it is increasingly recognised as part of the digital infrastructure that supports teaching, research, and student experience. Yet despite this growing awareness, accessibility failures remain common across higher education systems. Students still encounter inaccessible learning platforms, course materials that cannot be used with assistive technologies, and institutional processes that place the burden of adaptation on disabled individuals.

This persistence of barriers suggests that the problem is not simply one of awareness or policy guidance. Rather, accessibility failures often arise from the way institutions adopt and govern digital technologies. To understand why the problem continues even where intentions are good, it is useful to think in terms of accessibility debt.

Accessibility debt in higher education systems

Accessibility debt accumulates when digital systems are adopted without accessibility being fully considered from the outset. Like technical debt in software development, the consequences may not be immediately visible. Platforms may appear to function adequately until disabled students begin to rely on them at scale. At that point the cost of remediation becomes significant, requiring workarounds, retrofitting, or labour-intensive accommodations.

Universities are particularly susceptible to this problem because their digital environments are complex and layered. Institutional technology ecosystems typically include learning management systems, student portals, library platforms, assessment tools, lecture capture technologies, and a growing number of AI enabled services. Many of these systems are supplied by external vendors and integrated with one another over time.

Even when individual platforms claim compliance with standards such as WCAG 2.2 AA, accessibility problems can emerge through the interaction between systems. A student may navigate successfully through one platform only to encounter barriers when moving to another tool that forms part of the same learning environment. Over time these problems accumulate as institutions add new tools without addressing existing accessibility limitations.

How institutions create accessibility debt

Procurement and governance decisions are central to the accumulation of accessibility debt. In principle, accessibility requirements can be evaluated during vendor selection. In practice this process is often difficult. Suppliers may provide incomplete or ambiguous accessibility documentation, and institutional buyers may lack the expertise needed to interpret those claims.

The Accessible Digital Futures project identified procurement as a major challenge for the UK higher education sector. Universities frequently struggle to obtain reliable information about whether digital and AI powered products meet accessibility standards. Even when accessibility statements are available, they may not reflect how the system performs in real institutional contexts.

Institutional governance structures add another layer of complexity. Universities are typically decentralised organisations where digital decisions are made in multiple places. Academic departments adopt specialised teaching tools. Administrative units introduce new platforms to manage services. Individual instructors experiment with emerging technologies. Each decision may appear limited in scope, but collectively they shape the accessibility of the institutional digital environment.

Under these conditions accessibility responsibility can become fragmented. Policies may exist at the institutional level, yet practical decisions about technology adoption occur across many different teams. Without clear governance and accountability, accessibility considerations are often introduced late in the process rather than guiding decisions from the beginning.

Why retrofitting accessibility rarely works

When accessibility barriers become visible, institutions often respond by attempting to retrofit accessibility into existing systems. This approach is understandable but rarely efficient. By the time problems are identified, platforms may already be embedded in teaching and administrative processes.

The scale of institutional digital infrastructure makes remediation difficult. Learning management systems may contain thousands of courses with legacy materials. Institutional websites can consist of hundreds of thousands of pages. Third party platforms may require vendor cooperation to address technical barriers. Even well-resourced remediation efforts can take years to complete.

In the meantime new technologies continue to be introduced. Emerging tools such as generative AI, immersive learning environments, and advanced analytics systems offer significant potential benefits. The Jisc project highlights how these technologies could support more inclusive forms of digital learning if accessibility is considered early. However the same technologies may create new barriers if institutions repeat existing patterns of procurement and governance.

The result is a cycle in which accessibility problems are repeatedly addressed after the fact rather than prevented through earlier decision making.

What structural change would look like

Reducing accessibility debt requires institutions to treat accessibility as a governance issue rather than a purely technical one. Responsibility cannot sit solely with disability services or specialist accessibility teams. Decisions about procurement, platform adoption, and digital strategy shape the accessibility of the entire institutional environment.

Procurement practices are one important lever. Universities need clearer processes for evaluating vendor accessibility claims and stronger expectations that suppliers provide reliable documentation. The European Accessibility Act having recently gone into force may increase pressure on technology providers to meet accessibility standards, but institutional buyers will still need the expertise and governance structures necessary to interpret those requirements.

Accessibility expertise must also be integrated earlier in institutional decision making. Too often accessibility specialists are consulted only after technology has already been selected. Involving accessibility expertise during planning and procurement can significantly reduce the need for costly remediation later.

Finally, institutions must recognise the cumulative nature of accessibility debt. Addressing individual barriers is necessary but not sufficient. Progress depends on examining the institutional systems that produce those barriers in the first place.

The higher education sector is currently undergoing rapid technological transformation. Digital platforms and AI driven tools are reshaping how universities teach, assess, and support students. Initiatives such as Accessible Digital Futures rightly emphasise the opportunity to ensure these changes produce more inclusive educational environments.

Whether that opportunity is realised will depend less on technological capability than on institutional governance. Accessibility failures rarely arise from a single decision. They emerge gradually as accessibility debt accumulates across procurement choices, platform ecosystems, and fragmented responsibility. Recognising and addressing that structural dynamic may be one of the most important steps universities can take toward genuinely accessible digital futures.

Kevin Andrews is a Certified Web Accessibility Specialist working at the intersection of digital accessibility, technology governance, and higher education systems. His work focuses on how institutional technology decisions shape accessibility outcomes for disabled students and staff. He brings both professional expertise and lived experience of disability to his work on accessible digital infrastructure.  


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The missing middle ground between research-led and practice-led education

by Saeed Talebi and Nick Morton

A peer reviewer recently challenged our pedagogical approach. We had described embedding an industry-led research project on Digital Twin development into our built environment curriculum as ‘research-informed teaching’. The reviewer disagreed: this was ‘practice-led rather than research-informed,’ they argued, because students weren’t producing research outputs themselves.

The comment revealed a conceptual confusion we suspect is widespread in higher education. We often assume that if students aren’t producing original research, then any industry-focused teaching must simply be vocational training with academic window-dressing. This leaves practice-facing disciplines in an awkward position: industry engagement is essential to what we do, but it risks being dismissed as less scholarly. There is, however, a middle ground.

Healey and Jenkins’ (2009) model offers a useful way through this confusion. They identify four modes of engaging undergraduates with research: research-led (learning about current scholarship), research-oriented (learning research methods), research-based (undertaking inquiry), and research-tutored (engaging in research discussions). These are mapped across two dimensions: whether students are positioned as audience or participants, and whether the emphasis falls on research content or processes. The model’s key insight is that students can be meaningfully engaged with research even when they aren’t producing research outputs themselves. The question isn’t simply whether students are ‘doing research’, it’s whether they’re positioned as passive recipients of established knowledge or as active participants in scholarly inquiry.

Practice-led teaching operates on different logic, though that logic has a closer relationship to applied research than is sometimes acknowledged. Its primary aim is developing professional competence through authentic engagement with messy problems and competing stakeholder priorities. The distinction isn’t whether industry is involved – it can be present in both approaches. The distinction lies in how students are positioned in relation to knowledge. In practice-led education, knowledge tends to be treated as relatively settled. In research-informed education, knowledge is contested, evolving, and open to question. An opportunity arises when these approaches coincide without conscious design, and a risk emerges when they collapse into one another. Research-informed teaching can become performative, referencing staff publications without changing how students learn. Practice-led teaching can slip into employability theatre, where live briefs are added without interrogating what knowledge students are actually developing.

As Professor Hanifa Shah OBE recently argued in Times Higher Education, STEAM education at its best equips students to “move fluidly between analytical and imaginative modes of thinking“, asking critical questions, considering ethical implications, and bringing meaning to innovation. This is precisely the disposition that research-informed teaching seeks to develop. In STEAM disciplines, including architecture, built environment, computing and engineering, emerging technologies create spaces where research and practice intersect meaningfully. Digital Twins and real-time monitoring tools, for example, allow students to work with live systems while engaging critically with the assumptions and ethics embedded within them. Students aren’t merely applying research after the fact, nor mimicking professional routines. They’re learning to question how data is generated, how models simplify reality, and how decisions are shaped by both evidence and judgement. Practice becomes a site of inquiry.

There’s an institutional dimension here too. Across the sector, promotion frameworks, workload models, and teaching quality metrics often reward research visibility and industry engagement without asking how either is translated pedagogically. Academics are encouraged to ‘bring research into teaching’ and ‘embed employability’, yet rarely supported in doing the difficult design work that meaningful integration requires. Recent discussions within the sector have highlighted how delivery models shape the possibilities for integrating academic and workplace learning. These are sector-wide conversations, and they reflect shared challenges around diverse learner cohorts, blended delivery, and the risk of compliance overtaking genuine learning. As a result, many innovative practices remain dependent on individual effort rather than structural support.

None of this means practice-led and research-informed approaches are mutually exclusive. The most effective curricula often blend elements of both. But blending deliberately is quite different from conflating accidentally.

When designing industry-engaged teaching, it’s worth asking honest questions. Are students positioned as inquirers or executors? Are they engaging with contested knowledge or settled practice? Does assessment reward critical reflection or merely competent performance? Is the industry project a vehicle for scholarly inquiry, or is scholarly framing a veneer over vocational training?

The answers won’t always be clear-cut, and that’s fine. But asking the questions helps us design with intention rather than stumbling into confusion – and helps us articulate what we’re doing when a peer reviewer, a sceptical colleague, or a university committee asks us to justify our approach.

Dr Saeed Talebi is an Associate Professor in the Department of Architecture and Built Environment at Birmingham City University and a Senior Fellow of the Higher Education Academy (SFHEA). He has held a number of T&L leadership roles, including Departmental Lead, Course Leader, and Academic Lead for Teaching Excellence and Student Experience. He has a keen interest in pedagogy in higher education, with particular interest in research-informed teaching and the integration of emerging technologies and practice-led projects into built environment curricula to enhance student outcomes and experience. He has also led the delivery of large STEAM research projects.

Professor Nick Morton is the Academic Director of Partnerships and STEAM at Birmingham City University. A Principal Fellow of the Higher Education Academy (PFHEA), he was awarded a National Teaching Fellowship in recognition of his track record in curriculum development. He has held a number of senior leadership roles at BCU, including Associate Dean for Teaching Education and Student Experience, overseeing Computing, Engineering and the Built Environment. He was elected Vice-Chair of the Council of Heads of the Built Environment (CHOBE) in 2012 and is a Fellow of the Royal Institution of Chartered Surveyors (FRICS).


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Reconceptualising transnational education through decolonial approaches

by Nilakshi Das

Transnational Education (TNE) represents a rapidly expanding form of cross-border provision, underpinned by an economic imaginary that positions the UK as a ‘key player’ in the global higher education market. While earlier internationalisation strategies focused primarily on bringing overseas students to Britain, TNE reflects a shift towards delivering British higher education to students globally through offshore provisions. The rapid expansion of TNE has elicited growing academic debate about its potential to reproduce the political, economic and epistemic hegemony of the Global North, reinscribing earlier colonial hierarchies and patterns of dependency.

Before the emergence of TNE, the internationalisation of UK higher education was primarily organised through the academic mobility of overseas students, shaped by Britain’s imperial and post-imperial educational networks. During the 1960s, technical assistance programmes and scholarship schemes, such as the Commonwealth Scholarship programme, facilitated student mobility to British universities. In the 1970s, as overseas student numbers gradually increased, differential student fees were introduced alongside tightening immigration regulations, a shift that continued throughout the Thatcher government in the 1980s into the present day. By the late 1990s, the University of Nottingham had established one of the earliest overseas branches, with the general idea that the curriculum in the host country would largely mirror the home institution. From the 2000s onwards, TNE expanded in scope and provision through franchised programmes, joint and dual degrees, distance and online courses in new markets, particularly across the Middle East and Asia. These arrangements allowed UK universities to expand their global presence and competitiveness beyond traditional overseas student recruitment.

Political economy of TNE and latest policy ambitions

The latest articulation of TNE goals includes the Labour government’s new strategy for national renewal by ‘turbocharging education’ as an engine for economic growth, with a target of £40 billion in education exports by 2030. This decision reflects increasing political pressure to cut net migration by reducing overseas student recruitment. Recent policy recommendations have radically argued that universities should be ‘selling education, not immigration’, with growing concerns that student visas are being used as a backdoor route into the UK labour market. Against broader anxieties around immigration in which international students are repositioned as migration liabilities, TNE offers a politically viable solution by exporting education in favour of substituting inward student mobility. As universities’ budgets shrink due to ongoing visa restrictions for international students, TNE engagements are expected to further increase (Hartmann and Lee, 2026).

According to the latest data, the number of students studying entirely overseas through UK TNE increased by 8% in 2024/25, and has risen by 37% since 2020/21. TNE student numbers are now close to the number of international students studying in the UK, with approximately one in six students in UK HE being educated across overseas campuses. Yet, despite this rapid growth, there is a lack of public data on student experiences at TNE. While aggregate data records the number of students enrolled in TNE programmes and level of education, there is limited publicly available information on student progression, degree outcome and labour market prospects. The experiences of students and educators involved in these programmes often tend to remain marginal within UK policy debates.

As higher education increasingly operates through a ‘big business’ model, institutional priorities have rapidly shifted towards generating revenue and maintaining competitiveness. In doing so,commitments to uphold student welfare, equality, and meaningful international collaboration are often sidelined in favour of positional advantage. Therefore, the expansion of TNE under growing market competition raises further pressing questions about equity and power within global HE systems.

Towards a decolonial approach to TNE

Most of the latest policy and institutional analyses of TNE tend to adopt an instrumentalist perspective focusing on business indicators, such as risk assessment, return on investment, international branding and reputation, quality assurance and transnational management strategies. As a result, TNE is mainly understood through frameworks of foreign investment and transnational service delivery further entrenching the logic of the market that frames education as a tradable commodity rather than a global public good (Lauren Clarke, 2021). While these considerations are important for universities operating in a competitive global environment, they risk overshadowing broader questions about equity, inclusion and the social purposes of higher education.

Adopting a decolonial approach to TNE can help address questions around embedding Western systems and structures of education in the Global South. Debates around the coloniality of TNE are not entirely new. Some scholars have drawn parallels between contemporary TNE arrangements and colonial models of education between the 16th and the 19th centuries. Colonial education systems were often characterised by limited access for the local population, a lack of relevance to local realities, the marginalisation of indigenous knowledge systems, the exclusive use of English as the primary medium of instruction, institutional authority with control originating from colonial centres and limited curricula featuring vocational degrees (Teferra and Altbach, 2004). TNE arrangements have been articulated as operating through a similar binary model in which a ‘core’ of sending institutions from the West sets the agenda for a marginalised ‘periphery’ of receiving partners, perpetuating historical legacies of colonialism (Caruana and Montgomery, 2015).

Against this background, there are growing calls for more introspective approaches to TNE that challenge post-colonial structures of dependence and compliance, while remaining attentive to the risk that transnational partnerships may reproduce these hierarchies through networks of alliance with local elites in host countries. Ravindra Sidhu advocates an ‘engaged pedagogy’ and an ‘ethics of care’ in the design and governance of TNE partnerships, emphasising the need to recognise the histories, aspirations and agency of local communities involved in these programmes.

Drawing insights from postcolonial and decolonial scholarship that emphasise justice, inclusion and agency will enable TNE strategies to better examine their implications on student success, outcomes and experiences as well as their wider impact on local communities and higher education systems in host countries. At the same time, greater attention to the national and cultural contexts in which TNE operates, particularly where differing political and institutional norms raise ethical challenges around academic freedom, governance and accountability, can support more informed institutional decision-making and partnerships. These perspectives will ensure that the expansion of TNE is not guided by short-term commercial imperatives but by broader commitments to equity and responsible global engagement, avoiding polarised strategic approaches (Sanderson, 2023).

Nilakshi Das has recently completed her PhD in History of Science. Her PhD was funded by the ESRC and jointly undertaken at the University of Leicester and the University of Warwick. Nilakshi holds an MSc in Education from the University of Oxford and an MA in Sociology from the University of Manchester, funded by a Commonwealth Scholarship. She is a Fellow of the Institute for Historical Research.


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Inclusion without structural change: what neurodiverse academics tell us about inequality in higher education

by Eleni Meletiadou

The blog is based on the recent outputs from our COST ACTION CA20137 VOICES project entitled: ‘Lived Experiences of Neurodiverse Academics and Early Career Researchers in Higher Education: Identifying Structural Barriers and Pathways to Inclusion.’

Introduction

Higher education institutions across Europe increasingly emphasise equality, diversity, and inclusion, yet neurodiversity remains unevenly addressed within academic career structures. While universities have developed policies to support disabled and neurodiverse students, far less attention has been given to the experiences of neurodiverse academics, particularly early-career researchers and women. As a result, inclusion is often framed as an issue of individual adjustment rather than institutional design.

This blog presents findings from a qualitative survey conducted as part of the work of the VOICES COST Action, which examines inequalities affecting Young Researchers and Innovators (YRIs) from a gender perspective and promotes dialogue between researchers, institutions, and policy-makers across the European research ecosystem. The project explored the lived experiences of neurodiverse academics in higher education to identify structural barriers and inform policy and practice.

The findings suggest that, despite the growing visibility of neurodiversity, academic systems continue to be shaped by narrow assumptions about productivity, communication, and career progression. These assumptions can disadvantage those whose cognitive styles or working patterns do not align with dominant norms, particularly in contexts characterised by high workload, precarity, and informal career expectations.

Academic norms and the persistence of the ‘ideal worker’ model

Participants in the survey frequently described academic work as governed by implicit norms rather than transparent criteria. Expectations related to teaching, research, administration, and collegiality were often perceived as unclear, variable, and dependent on departmental culture.

Previous research has shown that academic environments tend to reward constant productivity, long working hours, and high levels of self-management, reflecting what has been described as the ‘ideal worker’ model. These expectations can create difficulties for academics whose cognitive styles involve fluctuating concentration, sensory sensitivity, or the need for predictable routines.

Respondents highlighted challenges associated with unpredictable workloads, competing deadlines, and the pressure to remain constantly available. Conferences, meetings, and networking events were also described as demanding environments, particularly when participation required rapid communication, multitasking, or tolerance of sensory overload.

Such findings indicate that the difficulties experienced by neurodiverse academics are not simply individual challenges but are closely linked to the way academic work is organised. Consistent with the objectives of the VOICES COST Action, inequalities affecting early career researchers often arise from structural features of the research environment rather than from personal limitations.

Intersectional inequalities: neurodiversity, gender, and career stage

The survey also revealed that neurodiversity intersects with gender and career stage in ways that intensify disadvantage. Early career researchers on temporary or part-time contracts reported reluctance to disclose neurodivergence or request adjustments due to concerns about job security and professional reputation.

These findings align with existing research showing that precarious employment conditions are common in early academic careers and can discourage individuals from seeking support. When career progression depends on short-term contracts, publication output, and informal recommendations, academics may feel pressure to conform to existing expectations even when these expectations are difficult to meet.

Female participants described additional pressures related to expectations of organisation, emotional labour, and collegial behaviour, which have been widely documented in studies of gender inequality in academia. These expectations were perceived as particularly demanding in environments where workloads are high and institutional support is limited.

From the perspective of the VOICES COST Action, such findings highlight the importance of examining inequalities through an intersectional lens. Neurodiversity does not operate in isolation but interacts with gender, career stage, and employment conditions to shape academic experiences.

Inclusion policies and the gap between commitment and practice

Most respondents reported that their institutions had formal equality, diversity, and inclusion strategies. However, the implementation of these policies was often described as inconsistent. Support frequently depended on individual supervisors, line managers, or departmental cultures rather than on clear institutional procedures.

Participants noted that adjustments were typically provided only after formal disclosure, placing responsibility on individuals to identify their needs and request support. This approach can be particularly challenging for neurodiverse academics, for whom disclosure may involve perceived professional risks.

Research on disability and inclusion in higher education has shown that institutional responses often focus on individual accommodations rather than structural change. While accommodations are important, they do not address the underlying assumptions about productivity, communication, and career progression that shape academic work. The findings of this study suggest that inclusion policies may have a limited impact if they are not accompanied by changes to workload models, evaluation criteria, and organisational culture.

Implications for policy and institutional practice

The qualitative data point to several areas where institutional policy could better support neurodiverse academics and early career researchers.

Greater transparency in workload and progression criteria

Clear expectations regarding teaching, research, and administrative responsibilities can reduce uncertainty and support fairer evaluation. When criteria are implicit, those who struggle with informal norms may be disadvantaged.

Flexible and predictable working practices

Flexible deadlines, structured communication, and predictable scheduling can support a wider range of working styles without lowering academic standards. Such measures may benefit many staff, not only those who identify as neurodivergent.

Training for academic leaders

Supervisors, heads of department, and line managers play a key role in implementing inclusion policies. Training that increases understanding of neurodiversity can help ensure that support is applied consistently and fairly.

Mentoring and peer support networks

Structured mentoring programmes can reduce isolation, particularly for early-career researchers and women. Informal support networks were frequently described by participants as essential for coping with the demands of academic work.

Dialogue beyond the institutional level

In line with the aims of the VOICES COST Action, improving inclusion requires discussion not only within universities but also at national and European policy levels. Research evaluation frameworks, funding structures, and career pathways all shape the conditions in which academics work and therefore influence who is able to succeed.

Towards more inclusive research environments

The findings presented here indicate that neurodiversity in higher education continues to be addressed primarily through individual adjustments rather than structural reform. Academic career systems remain shaped by implicit norms that privilege particular working styles and disadvantage those who do not conform to them.

If universities are to meet their commitments to equality, diversity, and inclusion, neurodiversity must be considered within broader discussions of precarity, gender inequality, and the sustainability of academic careers. Creating more inclusive environments requires not only support for individuals but also critical reflection on the assumptions that underpin academic work.

As initiatives such as the VOICES COST Action emphasise, meaningful change depends on sustained dialogue between researchers, institutions, and policy-makers. Without such dialogue, there is a risk that inclusion will remain a policy aspiration rather than an everyday reality.

Higher education has the opportunity to move beyond reactive accommodations towards more inclusive structures. Doing so is not only a matter of fairness but also essential for retaining talented researchers whose contributions may otherwise be lost to systems that remain insufficiently flexible to support diverse ways of thinking and working.

Dr Eleni Meletiadou is Associate Professor (Teaching) at London Metropolitan University and a Principal Fellow of the Higher Education Academy. Her research focuses on equity, diversity, and inclusion in higher education, with particular emphasis on neurodiversity, gender equality, digital pedagogy, and education for sustainable development. She leads the Research Group on Inclusive Learning, Transnational Education and Academic Sustainability (RILEAS) and the Gender Equity, Diversity, and Inclusion (GEDI) Research Group. Her work has been recognised with the British Academy of Management Education Practice Award (2023) and includes several European and international projects on inclusive assessment, AI in higher education, and widening participation. She is an active member of COST Actions examining inequalities in the research ecosystem and the impact of digital transformation on higher education. Her research interests include organisational change in universities, inclusive curriculum design, early career researcher development, and socially just approaches to teaching and learning.

LinkedIn: https://www.linkedin.com/in/dr-eleni-meletiadou/


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Judgement under pressure: generative AI and the emotional labour of learning

by Joanne Irving-Walton

What AI absorbs and why that matters

Most debates about generative AI in higher education fixate on what it produces: essays, summaries, answers, paraphrases. I find myself increasingly interested in something else – what it absorbs. Over the past year, as conversations about AI have threaded through seminars and tutorials, a pattern has gradually become visible. In those discussions, students rarely begin with content production; instead, they talk about how it helps them get started and steadies them enough to keep going. They use it when the blank page paralyses, when feedback stings and when uncertainty feels exposing. One student described asking AI to “make it feel possible”. Another spoke of feeding tutor comments into the system so they could be “explained more kindly”. A third reflected, almost apologetically, “I don’t want it to do my work… I just need something to push against before I say it out loud and risk looking stupid”.

In each case, AI is not replacing thinking. It is absorbing part of the emotional labour involved in it, and as that labour is redistributed, the texture of judgement shifts. Academic judgement does not tend to emerge from comfort. It develops in the stretch between knowing and not knowing, when confidence dips, stakes feel heightened, and your sense of competence is quietly tested (Barnett, 2007). Staying in that stretch long enough for thinking to clarify demands more than intellectual effort; it requires emotional steadiness, time, space and the capacity to tolerate uncertainty without rushing to resolution (Biesta, 2013). Traditionally, that steadying work has been shared across learning relationships: tutors reframing feedback, peers normalising confusion, supervisors encouraging persistence through doubt. Generative AI now occupies part of that terrain.

I do not think this is inherently a problem. For some students, it is transformative. It marks a shift in where the labour of learning takes place and that change deserves examination rather than alarm.

Four modes of engagement and emotional labour

When students talk about how they use AI, their practices tend to cluster into four overlapping orientations. These are not moral categories so much as shifts in where emotional and cognitive labour is undertaken.

Instrumental engagement appears when students use AI to summarise readings, refine phrasing or impose structure. Here the friction lies in form-making and shaping thought into something communicable. The judgement at stake is procedural: what is proportionate or efficient in this context?

Dialogic engagement emerges when students test interpretations or rehearse arguments. AI becomes a low-stakes sounding board, absorbing some of the vulnerability of articulating something half-formed. The question beneath it is interpretive: what does this mean, and how far do I trust my reading and myself?

Metacognitive engagement is evident when students ask AI to critique their reasoning or compare approaches. What is absorbed here is evaluative tension and the discomfort of examining one’s own argument. The judgement in play is comparative and strategic: which option is stronger, and why? And then there is affective-regulatory engagement. Here, AI absorbs the anxiety that precedes judgement itself. It breaks tasks into steps, softens feedback, lowers the threshold for beginning, offers reassurance before submission and quietens the internal ruminations and rehearsals of everything that might go wrong. This is not peripheral to learning. It is increasingly central.

Figure: Where the labour of learning now lives

Accessibility, safety and the risk of smoothing too much

For many students, particularly those navigating anxiety, executive dysfunction, neurodivergence or heavy external commitments, this emotional buffering is not indulgence but access (Rose & Meyer, 2002). Breaking tasks into steps or privately rehearsing ideas before speaking can widen participation rather than diminish it.

We should not romanticise struggle. Nor should we imagine that institutional structures have ever been able to hold every student perfectly. For some learners, AI offers another place to rehearse thinking, one that sits alongside, rather than replaces, human dialogue.

But there is a tension here. If AI consistently absorbs the strain of uncertainty before ideas encounter resistance, if feedback is softened before it unsettles, if structure replaces the slow work of wrestling thought into form, then something quieter begins to shift. Much of this work happens privately, in browser tabs and late-night prompts, in spaces students do not always feel comfortable admitting to. That makes it harder for us to see what is being strengthened and what may be thinning. The danger is not comfort, but the quiet disappearance of formative strain.

By formative strain, I do not mean suffering for its own sake, nor simply the “desirable difficulties” described in cognitive load theory (Bjork & Bjork, 2011) or the stretching associated with a Vygotskian zone of proximal development (Vygotsky, 1978). I am referring to the lived experience of remaining with ambiguity, critique and partial understanding long enough for judgement to consolidate; the emotional as well as cognitive work of staying with a problem. If that work is always pre-processed, it may narrow the rehearsal space where judgement forms.

Scaffold or substitute

Much depends on whether AI remains a scaffold or begins to function as a substitute. Used as scaffold, it lowers the emotional threshold just enough for deeper engagement, absorbing anxiety without displacing judgement. Used as substitute, it reduces not only strain but evaluation itself; the work of deciding and committing shifts elsewhere. The distinction lies less in the tool than in how it is woven into the learning environment.

Individual awareness and institutional responsibility

It would be easy, and unfair, to frame this as a matter of individual discernment. Students already carry a great deal. But nor is this simply a matter of institutional correction. We are all navigating new terrain in real time, without a settled script.

If we are serious about judgement formation, then responsibility is shared — and it is evolving. This is less about detection or prohibition than about openness. AI engagement is happening whether we discuss it or not. The question is whether we bring it into the light. That might mean inviting students to reflect on how they used AI in a task, not as confession, but as analysis. It might mean modelling, in our own teaching, what it looks like to question or refine an AI response rather than accept it wholesale. It certainly means acknowledging the emotional labour of learning openly (Newton, 2014), recognising that starting can be harder than finishing and that this, too, is part of learning.

At a structural level, we also need some candour. Systems built on speed, metrics and visible output inevitably amplify the appeal of friction-reducing tools. If polish is rewarded more consistently than process, we should not be surprised when students bypass the stretch between uncertainty and articulation. Cultivating discernment, then, is not a matter of allocating blame. It is a collective project of making the shifting terrain of AI use visible, discussable and educative.

Where the emotional work now lives

Generative AI has not diminished the importance of human judgement. If anything, it has made visible how emotionally mediated that judgement has always been (Immordino-Yang & Damasio, 2007). The interior work of learning – the hesitation, the rehearsal, the private negotiation of uncertainty – has never been fully observable. It has always unfolded, at least in part, elsewhere.

What AI changes is not the existence of that interior space, but its texture. Some of that labour now takes place in dialogue with a system that can stabilise, extend or subtly redirect thinking. That creates an opportunity: we are at a juncture where the emotional dimensions of learning can be surfaced and examined more deliberately than before.

It also carries risk. Students can disappear down an AI rabbit hole just as easily as they once disappeared into rumination. The question is not whether the interior work exists, but how it is shaped and whether it ultimately strengthens judgement or thins it.

References

Barnett, R (2007) A will to learn: Being a student in an age of uncertainty Open University Press

Biesta, GJJ (2013) The beautiful risk of education Paradigm Publishers

Bjork, EL & Bjork, RA (2011) ‘Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning’ in MA Gernsbacher, RW Pew, LM Hough & JR Pomerantz (eds), Psychology and the real world: Essays illustrating fundamental contributions to society (pp. 56–64) Worth Publishers

Newton, DP (2014) Thinking with feeling: Fostering productive thought in the classroom Routledge

Vygotsky, LS (1978) Mind in society: the development of higher psychological processes Harvard University Press Rose, DH & Meyer, A (2002) Teaching every student in the digital age: universal design for learning ASCD

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.