Category: Teaching & Learning

  • Reclaiming the narrative of educational excellence despite the decline of educational gain

    Reclaiming the narrative of educational excellence despite the decline of educational gain

    There was a time when enhancement was the sector’s watchword.

    Under the Higher Education Funding Council for England (HEFCE), concepts like educational gain captured the idea that universities should focus not only on assuring quality, but on improving it. Teaching enhancement funds, learning and teaching strategies, and collaborative initiatives flourished. Today, that language has all but disappeared. The conversation has shifted from enhancement to assurance, from curiosity to compliance. Educational gain has quietly declined, not as an idea, but as a priority.

    Educational gain was never a perfect concept. Like its cousin learning gain, it struggled to be measured in ways that were meaningful across disciplines, institutions, and student journeys. Yet its value lay less in what it measured than in what it symbolised. It represented a shared belief that higher education is about transformation: the development of knowledge, capability, and identity through the act of learning. It reminded us that the student experience was not reducible to outcomes, but highly personal, developmental, and distinctive.

    Shifting sands

    The shift from HEFCE to the Office for Students (OfS) marked more than a change of regulator; it signalled a change in the state’s philosophy, from partnership to performance management. The emphasis moved from enhancement to accountability. Where HEFCE invested in collaborative improvement, OfS measures and monitors. Where enhancement assumed trust in the professional judgement of universities and their staff, regulation presumes the need for assurance through metrics. This has shaped the sector’s language: risk, compliance, outcomes, baselines – all necessary, perhaps, but narrowing.

    The latest OfS proposals on revising the Teaching Excellence Framework mark a shift in their treatment of “educational gain.” Rather than developing new measures or asking institutions to present their own evidence of gain, OfS now proposes removing this element entirely, on the grounds that it produced inconsistent and non-comparable evidence. This change is significant: it signals a tighter focus on standardised outcomes indicators. Yet by narrowing the frame in this way, we risk losing sight of the broader educational gains that matter most to students, gains that are diverse, contextual, and resistant to capture through a uniform set of metrics. It speaks to a familiar truth: “not everything that counts can be counted, and not everything that can be counted counts”.

    And this narrowing has consequences. When national frameworks reduce quality to a narrow set of indicators, they risk erasing the very distinctiveness that defines higher education. Within a framework of uniform metrics, where does the space remain for difference, for innovation, for the unique forms of learning that make higher education a rich and diverse ecosystem? If we are all accountable to the same measures, it becomes even more important that we define for ourselves what excellence in education looks like, within disciplines, within institutions, and within the communities we serve.

    Engine room

    This is where the idea of enhancement again becomes critical. Enhancement is the engine of educational innovation: it drives new methods, new thinking, and the continuous improvement of the student experience. Without enhancement, innovation risks becoming ornamental: flashes of good practice without sustained institutional learning. The loss of “educational gain” as a guiding idea has coincided with a hollowing out of that enhancement mindset. We have become good at reporting quality, but less confident in building it.

    Reclaiming the narrative of excellence is, therefore, not simply about recognition and reward; it is about re-establishing the connection between excellence and enhancement. Excellence is what we value, enhancement is how we realise it. The Universitas 21 project Redefining Teaching Excellence in Research-Intensive Universities speaks directly to this need. It asks: if we are to value teaching as we do research, how do we define excellence on our own terms? What does excellence look like in an environment where metrics are shared but missions are not?

    For research-intensive universities in particular, this question matters. These institutions are often defined by their research outputs and global rankings, yet they also possess distinctive educational strengths: disciplinary depth, scholarly teaching, and research-informed curricula. Redefining teaching excellence means articulating those strengths clearly, and ensuring they are recognised, rewarded, and shared. It also means returning to the principle of enhancement: a commitment to continual improvement, collegial learning, and innovation grounded in scholarship.

    Compass point

    The challenge, and opportunity, for the sector is to rebuild the infrastructure that once supported enhancement. HEFCE-era initiatives, from the Subject Centres to the Higher Education Academy, created national and disciplinary communities of practice. They gave legitimacy to innovation and space for experimentation. The dismantling of that infrastructure has left many educators working in isolation, without the shared structures that once turned good teaching into collective progress. Reclaiming enhancement will require new forms of collaboration, cross-institutional, international, and interdisciplinary, that enable staff to learn from one another and build capacity for educational change.

    If educational gain as a metric was flawed, educational gain as an ambition is not. It reminds us that the purpose of higher education is not only to produce measurable outcomes but to foster human and intellectual development. It is about what students become, not just what they achieve. As generative AI reshapes how students learn and how knowledge itself is constructed, this broader conception of gain becomes more vital than ever. In this new context, enhancement is about helping students, and staff, to adapt, to grow, and to keep learning.

    So perhaps it is time to bring back “educational gain,” not as a measure, but as a mindset; a reminder that excellence in education cannot be mandated through policy or reduced to data. It must be defined and driven by universities themselves, through thoughtful design, collaborative enhancement, and continual renewal.

    Excellence is the destination, but enhancement is the journey. If we are serious about defining one, we must rediscover the other.

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  • High quality learning means developing and upskilling educators on the pedagogy of AI

    High quality learning means developing and upskilling educators on the pedagogy of AI

    There’s been endless discussion about what students do with generative AI tools, and what constitutes legitimate use of AI in assessment, but as the technology continues to improve there’s a whole conversation to be had about what educators do with AI tools.

    We’re using the term “educators” to encompass both the academics leading modules and programmes and the professionals who support, enable and contribute to learning and teaching and student support.

    Realising the potential of the technologies that an institution invests in to support student success requires educators to be willing and able to deploy it in ways that are appropriate for their context. It requires them to be active and creative users of that technology, not simply following a process or showing compliance with a policy.

    So it was a bit worrying when in the course of exploring what effective preparation for digital learning futures could look like for our Capability for change report last year, it was noticeable how concerned digital and education leaders were about the variable digital capabilities of their staff.

    Where technology meets pedagogy

    Inevitably, when it comes to AI, some HE staff are enthusiastic early adopters and innovators; others are more cautious or less confident – and some are highly critical and/or just want it to go away. Some of this is about personal orientation towards particular technologies – there is a lively and important critical debate about how society comes into a relationship with AI technology and the implications for, well, the future of humanity.

    Some of it is about the realities of the pressures that educators are under, and the lack of available time and headspace to engage with developmental activity. As one education leader put it:

    Sometimes staff, they know that they need to change what they’re doing, but they get caught in the academic cycle. So every year it’s back to teaching again, really, really large groups of students; they haven’t had the time to go and think about how to do things differently.

    But there’s also an institutional strategic challenge here about situating AI within the pedagogic environment – recognising that students will not only be using it habitually in their work and learning, but that they will expect to graduate with a level of competence in it in anticipation of using AI in the workplace. There’s an efficiency question about how using AI can reprofile educator working patterns and workflows. Even if the prospect of “freeing up” lots of time might feel a bit remote right now, educators are clearly going to be using AI in interesting ways to make some of their work a bit more efficient, to surface insight from large datasets that might not otherwise be accessible, or as a co-creator to help enhance their thinking and practice.

    In the context of learning and teaching, educators need to be ready to go beyond asking “how do the tools work and what can I do with them?” and be prepared to ask and answer a larger question: “what does it mean for academic quality and pedagogy when I do?”

    As Tom Chatfield has persuasively argued in his recent white paper on AI and the future of pedagogy, AI needs to have a clear educative purpose when it is deployed in learning and teaching, and should be about actively enhancing pedagogy. Reaching this halcyon state requires educators who are not only competent in the technical use of the tools that are available but prepared to work creatively to embed those tools to achieve particular learning objectives within the wider framework and structures of their academic discipline. Expertise of this nature is not cheaply won – it takes time and resource to think, experiment, test, and refine.

    Educators have the power – and responsibility – to work out how best to harness AI in learning and teaching in their disciplines, but education leaders need to create the right environment for innovation to flourish. As one leader put it:

    How do we create an environment where we’re allowing people to feel like they are the arbiters of their own day to day, that they’ve got more time, that they’re able to do the things that they want to do?…So that’s really an excitement for me. I think there’s real opportunity in digital to enable those things.

    Introducing “Educating the AI generation”

    For our new project “Educating the AI generation” we want to explore how institutions are developing educator AI literacy and practice – what frameworks, interventions, and provisions are helpful and effective, and where the barriers and challenges lie. What sort of environment helps educators to develop not just the capability, but also the motivation and opportunity to become skilled and critical users of AI in learning and teaching? And what does that teach us about how the role of educators might change as the higher education learning environment evolves?

    At the discussion session Rachel co-hosted alongside Kortext advisor Janice Kay at the Festival of Higher Education earlier this month there was a strong sense among attendees that educating the AI generation requires universities to take action on multiple fronts simultaneously if they are to keep up with the pace of change in AI technology.

    Achieving this kind of agility means making space for risk-taking, and moving away from compliance-focused language to a more collaborative and exploratory approach, including with students, who are equally finding their feet with AI. For leaders, that could mean offering both reassurance that this approach is welcomed, and fostering spaces in which it can be deployed.

    In a time of such fast-paced change, staying grounded in concepts of what it means to be a professional educator can help manage the potential sense of threat from AI in learning and teaching. Discussions focused on the “how” of effective use of AI, and the ways it can support student learning and educator practice, are always grounded in core knowledge of pedagogy and education.

    On AI in assessment, it was instructive to hear student participants share a desire to be able to demonstrate learning and skills above and beyond what is captured in traditional assessment, and find different, authentic ways to engage with knowledge. Assessment is always a bit of a flashpoint in pedagogy, especially in constructing students’ understanding of their learning, and there is an open question on how AI technology can support educators in assessment design and execution. More prosaically, the risks to traditional assessment from large language models indicate that staff may need to spend proportionally more of their time on managing assessment going forward.

    Participants drew upon the experiences of the Covid pivot to emergency remote teaching and taking the best lessons from trialling new ways of learning and teaching as a useful reminder that the sector can pivot quickly – and well – when required. Yet the feeling that AI is often something of a “talking point” rather than an “action point” led some to suggest that there may not yet be a sufficiently pressing sense of urgency to kickstart change in practice.

    What is clear about the present moment is that the sector will make the most progress on these questions when there is sharing of thinking and practice and co-development of approaches. Over the next six months we’ll be building up our insight and we’d love to hear your views on what works to support educator development of AI in pedagogy. We’re not expecting any silver bullets, but if you have an example of practice to share, please get in touch.

    This article is published in association with Kortext. Join Debbie, Rachel and a host of other speakers at Kortext LIVE on Wednesday 11 February in London, where we’ll be discussing some of our findings – find out more and book your place here.

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  • Student engagement does not work if institutions are stuck in survival mode

    Student engagement does not work if institutions are stuck in survival mode

    The current state of UK higher education in 2025 is marked by an existential crisis, rather than merely a series of difficult challenges.

    This crisis comes from the inherent tension of attempting to operate a 20th century institutional model within the complex realities of the 21st century. This strain is exacerbated by complex socio-economic difficulties facing students, coupled with the immense pressures experienced by staff.

    A city under siege

    Conceptualising UK HE as a “city”, it becomes evident that while valuable as centres of learning, community and potential, this “city” is currently under siege and there is a “dragon at the gates”. The “dragon” represents a multifaceted array of contemporary pressures. These include, but are not limited to, funding reductions, evolving regulatory demands and the escalating cost-of-living crisis. Empirical research indicates that the cost-of-living crisis profoundly impacts students’ capacity for engagement.

    Furthermore, this “dragon” is continuously evolving. With the rapid ascent of artificial intelligence (AI) and the distinct characteristics of Gen Z learners representing two of its newest and most salient “heads”. While AI offers opportunities for personalised learning, simultaneously, it presents substantial challenges to academic integrity and carries the risk of augmenting student isolation if not balanced with human connection. Concurrently, Gen Z learners have learned a state of “continuous partial attention” through constant exposure to multiple information streams. This poses a unique challenge to pedagogical design.

    Defence, survival and the limits of future-proofing

    In response to these multifaceted challenges, the prevalent institutional instinct is to defend the city. This typically involves retreating behind existing structures, consolidating operations, centralising processes, tightening policies and intensifying reliance on familiar metrics such as Key Performance Indicators (KPIs), National Student Survey (NSS) action plans, attendance rates and overall survey scores.

    However, survival mode often means the sacrifice of genuine student engagement. This refers not to the easily quantifiable forms of engagement, but the relational, human dimension, wherein students develop a sense of belonging, perceive their contributions as meaningful and feel integrated into a valuable community. Research consistently demonstrates that this sense of belonging is paramount for psychological engagement and overall student success. Consequently, an exclusive focus on defending established practices, reliant on systemically imposed metrics, risks reinforcing barriers that actually impede connection, wellbeing and the institutional resilience that is critically needed.

    While the concept of “future-proofing” is often invoked, it is imperative to question the feasibility of achieving perfect preparedness against unknowable future contingencies.

    Attack strategies

    Given the limitations of a purely defensive stance, a different strategic orientation is warranted: a proactive “attack” on the challenges confronting HE. Genuine engagement should be reconceptualised not merely as a student characteristic, but as an institutional design choice. Institutions cannot expect students to arrive with pre-existing engagement; rather, they must actively design for it.

    This proactive engagement strategy aligns precisely with the University of Cumbria’s commitment to people, place, and partnerships. These themes are woven through the university’s new learning, teaching and assessment plan, providing a framework for institutional pedagogic transformation.

    Relationships as the bedrock of community

    The “citizens” of our HE “city” – students and staff – constitute its absolute bedrock. Strong relationships between these stakeholders are fundamental to fostering a resilient academic community. A critical institutional challenge lies in ensuring that existing systems, policies and workload models adequately support these vital connections. It is imperative to grant staff the requisite time, flexibility and recognition for their crucial relational work. This represents a shift in focus from a transactional interaction to a relationship-centric approach.

    Understanding the distinct experiences of diverse groups of students (e.g. apprentices, online learners and commuter students) is of critical importance for building meaningful and authentic engagement. Fundamentally, ensuring that students feel “seen, heard and valued” is a key determinant of psychological engagement and a prerequisite for all other forms of learning to take root.

    Designing for inclusive environments

    The concept of “place” encompasses the entire physical and digital environment of the HE institution. Belonging, rather than being an abstract sentiment, possesses a strong spatial and environmental dimension. For institutions like the University of Cumbria, intentional design of consistent environments that cultivate a sense of “This is my place” is paramount. An important tactic in this regard is to build belonging by design, particularly at critical transition points such as induction.

    This notion of “place” is particularly vital for commuter students, who often lack the built-in community afforded by residential halls. For this cohort, the physical campus serves as the primary site of their university experience. A critical assessment of their campus experience between scheduled classes is needed. Are institutional spaces designed to encourage students to remain, study and connect? When students choose to utilise them, these spaces facilitate spontaneous conversations, the formation of friendships, and the organic development of belonging.

    This kind of intentionality is required for digital learning environments. Are virtual learning environments (VLEs) merely content repositories, or are they designed as welcoming community hubs? The creation of inclusive, supportive environments – both physical and virtual – where students feel genuinely connected, is absolutely fundamental to effective engagement. Moreover, clear opportunities exist to strengthen recognition of how an individual’s sense of place can positively impact learning experiences primarily delivered online.

    Partnerships in fostering genuine student experiences

    The final pillar, “partnerships,” refers to the cultivation of alliances within the HE “city”. While “student voice” is frequently championed, research strongly indicates a necessity to move beyond mere collection of voice towards fostering genuine student influence and co-creation. The distinction is crucial: “student voice” may involve an end-of-module survey, whereas “student influence” entails inviting students to co-design assessment questions for subsequent iterations of that module.

    The University of Cumbria’s recent consistent module evaluation approach serves as an exemplary model. Achieving a 34.2% response rate in the first semester of 2024/25, which exceeds sector averages, and, critically, delivering 100% “closing the loop” reports to students, demonstrates a commitment to acknowledging and acting upon all feedback. This provides a concrete illustration of making student influence visible.

    From strategy to action

    This approach is a fundamental paradigm shift: from a reactive, defensive posture focused on metrics to a proactive engagement strategy. This “attack” on the challenges, framed by the University of Cumbria’s distinctive strategic approach, is predicated on three core actions: prioritising People by enabling relational work, designing a sense of Place to foster belonging, and building authentic Partnerships that transform student voice into visible influence. Translating this strategy into actionable practice does not necessitate additional burdens, but rather the integration of five practical tactics into existing workflows:

    1. Rethink what you measure and why: Transition from a “data-led” to a “data-informed” approach. This involves utilising data for meaningful reflection and making deliberate choices to enhance the student experience, rather than reacting defensively to metrics such as KPIs, NSS scores and attendance data.
    2. Build belonging at transitions: Recognising belonging as a critical component of psychological engagement and overall student success, this tactic underscores the importance of intentionally designing key junctures in the student journey, such as induction and progression points, to be inherently inclusive.
    3. Enable relational work: Acknowledging that strong student-staff relationships form the “bedrock” of a resilient academic community, and that staff often face conflicts between fostering these connections and workload pressures, this tactic advocates for formally enabling “relational work”.
    4. Turn voice into influence: Meaningful partnership necessitates moving beyond mere collection of student “voice” to cultivating their genuine “influence”. The critical determinant is not simply whether the institution is listening, but whether substantive changes are being implemented based on student feedback. This can be achieved through the establishment of “visible feedback loops” that demonstrate the impact of student input and leveraging technology to complement, rather than replace, human interaction.
    5. Partnership by design: This final tactic advocates for embedding co-creation with students as an intrinsic element from the initial stages. Rather than being an occasional or supplementary activity, authentic partnership should be structurally integrated, with students actively involved in key decision-making processes.

    The fundamental question facing HE in 2025 – “What is a university for?” – is increasingly met with the unsettling realisation that conventional answers no longer suffice. However, a cautiously optimistic outlook prevails. The answer to this pivotal question lies not in defending existing paradigms, but in actively and courageously constructing a new institutional reality.

    This article has been adapted from a keynote address delivered by Dr Helena Lim at the University of Cumbria Learning and Teaching Conference on 18 June 2025, and has been jointly authored with Dr Jonathan Eaton, Pro Vice Chancellor (Learning & Teaching) at the University of Cumbria.

    For further insights into the research underpinning these arguments, the “Future-proofing student engagement” report is available here.

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  • AI is unlocking insights from PTES to drive enhancement of the PGT experience faster than ever before

    AI is unlocking insights from PTES to drive enhancement of the PGT experience faster than ever before

    If, like me, you grew up watching Looney Tunes cartoons, you may remember Yosemite Sam’s popular phrase, “There’s gold in them thar hills.”

    In surveys, as in gold mining, the greatest riches are often hidden and difficult to extract. This principle is perhaps especially true when institutions are seeking to enhance the postgraduate taught (PGT) student experience.

    PGT students are far more than an extension of the undergraduate community; they represent a crucial, diverse and financially significant segment of the student body. Yet, despite their growing numbers and increasing strategic importance, PGT students, as Kelly Edmunds and Kate Strudwick have recently pointed out on Wonkhe, remain largely invisible in both published research and core institutional strategy.

    Advance HE’s Postgraduate Taught Experience Survey (PTES) is therefore one of the few critical insights we have about the PGT experience. But while the quantitative results offer a (usually fairly consistent) high-level view, the real intelligence required to drive meaningful enhancement inside higher education institutions is buried deep within the thousands of open-text comments collected. Faced with the sheer volume of data the choice is between eye-ball scanning and the inevitable introduction of human bias, or laborious and time-consuming manual coding. The challenge for the institutions participating in PTES this year isn’t the lack of data: it’s efficiently and reliably turning that dense, often contradictory, qualitative data into actionable, ethical, and equitable insights.

    AI to the rescue

    The application of machine learning AI technology to analysis of qualitative student survey data presents us with a generational opportunity to amplify the student voice. The critical question is not whether AI should be used, but how to ensure its use meets robust and ethical standards. For that you need the right process – and the right partner – to prioritise analytical substance, comprehensiveness, and sector-specific nuance.

    UK HE training is non-negotiable. AI models must be deeply trained on a vast corpus of UK HE student comments. Without this sector-specific training, analysis will fail to accurately interpret the nuances of student language, sector jargon, and UK-specific feedback patterns.

    Analysis must rely on a categorisation structure that has been developed and refined against multiple years of PTES data. This continuity ensures that the thematic framework reflects the nuances of the PGT experience.

    To drive targeted enhancement, the model must break down feedback into highly granular sub-themes – moving far beyond simplistic buckets – ensuring staff can pinpoint the exact issue, whether it falls under learning resources, assessment feedback, or thesis supervision.

    The analysis must be more than a static report. It must be delivered through integrated dashboard solutions that allow institutions to filter, drill down, and cross-reference the qualitative findings with demographic and discipline data. Only this level of flexibility enables staff to take equitable and targeted enhancement actions across their diverse PGT cohorts.

    When these principles are prioritised, the result is an analytical framework specifically designed to meet the rigour and complexity required by the sector.

    The partnership between Advance HE, evasys, and Student Voice AI, which analysed this year’s PTES data, demonstrates what is possible when these rigorous standards are prioritised. We have offered participating institutions a comprehensive service that analyses open comments alongside the detailed benchmarking reports that Advance HE already provides. This collaboration has successfully built an analytical framework that exemplifies how sector-trained AI can deliver high-confidence, actionable intelligence.

    Jonathan Neves, Head of Research and Surveys, Advance HE calls our solution “customised, transparent and genuinely focused on improving the student experience, “ and adds, “We’re particularly impressed by how they present the data visually and look forward to seeing results from using these specialised tools in tandem.”

    Substance uber alles

    The commitment to analytical substance is paramount; without it, the risk to institutional resources and equity is severe. If institutions are to derive value, the analysis must be comprehensive. When the analysis lacks this depth institutional resources are wasted acting on partial or misleading evidence.

    Rigorous analysis requires minimising what we call data leakage: the systematic failure to capture or categorise substantive feedback. Consider the alternative: when large percentages of feedback are ignored or left uncategorised, institutions are effectively muting a significant portion of the student voice. Or when a third of the remaining data is lumped into meaningless buckets like “other,” staff are left without actionable insight, forced to manually review thousands of comments to find the true issues.

    This is the point where the qualitative data, intended to unlock enhancement, becomes unusable for quality assurance. The result is not just a flawed report, but the failure to deliver equitable enhancement for the cohorts whose voices were lost in the analytical noise.

    Reliable, comprehensive processing is just the first step. The ultimate goal of AI analysis should be to deliver intelligence in a format that seamlessly integrates into strategic workflows. While impressive interfaces are visually appealing, genuine substance comes from the capacity to produce accurate, sector-relevant outputs. Institutions must be wary of solutions that offer a polished facade but deliver compromised analysis. Generic generative AI platforms, for example, offer the illusion of thematic analysis but are not robust.

    But robust validation of any output is still required. This is the danger of smoke and mirrors – attractive dashboards that simply mask a high degree of data leakage, where large volumes of valuable feedback are ignored, miscategorised or rendered unusable by failing to assign sentiment.

    Dig deep, act fast

    When institutions choose rigour, the outcomes are fundamentally different, built on a foundation of confidence. Analysis ensures that virtually every substantive PGT comment is allocated to one or more UK-derived categories, providing a clear thematic structure for enhancement planning.

    Every comment with substance is assigned both positive and negative sentiment, providing staff with the full, nuanced picture needed to build strategies that leverage strengths while addressing weaknesses.

    This shift from raw data to actionable intelligence allows institutions to move quickly from insight to action. As Parama Chaudhury, Pro-Vice Provost (Education – Student Academic Experience) at UCL noted, the speed and quality of this approach “really helped us to get the qualitative results alongside the quantitative ones and encourage departmental colleagues to use the two in conjunction to start their work on quality enhancement.”

    The capacity to produce accurate, sector-relevant outputs, driven by rigorous processing, is what truly unlocks strategic value. Converting complex data tables into readable narrative summaries for each theme allows academic and professional services leaders alike to immediately grasp the findings and move to action. The ability to access categorised data via flexible dashboards and in exportable formats ensures the analysis is useful for every level of institutional planning, from the department to the executive team. And providing sector benchmark reports allows institutions to understand their performance relative to peers, turning internal data into external intelligence.

    The postgraduate taught experience is a critical pillar of UK higher education. The PTES data confirms the challenge, but the true opportunity lies in how institutions choose to interpret the wealth of student feedback they receive. The sheer volume of PGT feedback combined with the ethical imperative to deliver equitable enhancement for all students demands analytical rigour that is complete, nuanced, and sector-specific.

    This means shifting the focus from simply collecting data to intelligently translating the student voice into strategic priorities. When institutions insist on this level of analytical integrity, they move past the risk of smoke and mirrors and gain the confidence to act fast and decisively.

    It turns out Yosemite Sam was right all along: there’s gold in them thar hills. But finding it requires more than just a map; it requires the right analytical tools and rigour to finally extract that valuable resource and forge it into meaningful institutional change.

    This article is published in association with evasys. evasys and Student Voice AI are offering no-cost advanced analysis of NSS open comments delivering comprehensive categorisation and sentiment analysis, secure dashboard to view results and a sector benchmark report. Click here to find out more and request your free analysis.

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  • How to build smarter partnerships and become digitally mature

    How to build smarter partnerships and become digitally mature

    Across higher education, the conversation about digital transformation has shifted from connection to capability. Most universities are digitally connected, yet few are digitally mature

    The challenge for 2026 and beyond is not whether institutions use technology, but whether their systems and partnerships enable people and processes to work together to strengthen institutional capacity, learner outcomes, and agility.

    Boundless Learning’s 2025 Higher Education Technology and Strategy Survey underscored this transition: 95 per cent of leaders said education management partners are appealing, and one in three described them as extremely so. Yet preferences are changing: modular, fee-for-service models now outpace traditional revenue-sharing arrangements, signalling a desire for flexibility and control.

    Leaders also identified their top digital priorities: innovation enablement (53 per cent), streamlined faculty workflows (52 per cent), and integrated analytics (49 per cent). In other words, universities are no longer chasing the next platform; they want systems that think.

    Why systems thinking matters

    That idea is central to Suha Tamim’s workAnalyzing the Complexities of Online Education Systems: A Systems Thinking Perspective. Tamim frames online education as a dynamic ecosystem in which a change in one area, such as technology, pedagogy, or management, ripples through the whole. She argues that institutions need a “systems-level” view connecting the macro (strategy), meso (infrastructure and management), and micro (teaching and learning) layers.

    Seen this way, technology decisions become design choices that shape the culture and operations of the institution. Adopting a new platform is not just an IT project; it influences governance, academic workload, and the student experience. The goal is alignment across those levels so that each reinforces the other.

    Boundless Learning’s Learning Experience Suite (LXS) embodies this approach. Rather than adding another application into an already crowded environment, LXS helps institutions orchestrate existing systems; linking learning management, analytics, and support functions into a cohesive, secure, learner-centred framework. It is a practical application of systems thinking: connecting data flows, surfacing insights, and simplifying faculty and learner experiences within one integrated ecosystem.

    From outsourcing to empowering

    The shift toward integration also reflects how universities engage external partners. Jeffrey Sun, Heather Turner, and Robert Cermak, in the American Journal of Distance Education, describe four main reasons universities outsource online programme management:

    1. Responding quickly to competitive pressures
    2. Accessing upfront capital
    3. Filling capability gaps
    4. Learning and scaling in-house

    Their College Curation Strategy Framework shows that institutions partner with external providers not just to cut costs, but to build strategic capacity. Yet the traditional online programme management (OPM) model anchored in long-term revenue-share contracts has drawn criticism for limited transparency and loss of institutional control.

    Our own data suggest that this critique is reshaping practice. Universities are moving from outsourcing to empowerment: seeking education-management partners who enhance internal capability rather than replace it. This evolution from OPMs to Education Management Partners (EMPs) marks a decisive turn toward collaborative, capacity-building relationships.

    The Learning Experience Suite fits squarely within this new model. It is not an outsourced service but a connective layer that enables institutions to manage their digital ecosystems with greater visibility and confidence, while benefiting from enterprise-grade integration and security. It exemplifies partnership as a mechanism for capability development, a move from vendor management to shared strategic growth.

    From fragmentation to fluency

    Many institutions remain caught in what might be called digital fragmentation. According to our survey, nearly half of leaders cite data silos, disconnected platforms, and inconsistent learner experiences as obstacles to progress. These are not isolated technical issues; they are systemic barriers that affect pedagogy, governance, and institutional trust.

    Tamim’s framework describes such misalignment as a state of “disequilibrium.” Overcoming it requires coordinated action across levels, strategic clarity from leadership, adaptive management structures, and interoperable tools that make integration intuitive. The objective is to move from digital accumulation to digital fluency: an environment where technology amplifies, rather than fragments, institutional purpose.

    Learning Experience Suite was designed precisely to address this. By connecting data across systems, enabling real-time analytics, and ensuring accessibility through a mobile-first design, it allows institutions to build coherence and confidence in their digital operations.

    Building partnerships

    The next phase of higher education technology will be defined not by the tools universities choose but by the quality of their partnerships. As scholars like Sun have cautioned, outsourcing core academic functions without transparency can erode autonomy. Conversely, partnerships grounded in shared governance, open data, and aligned values can strengthen the academic mission.

    For Boundless Learning, this is the central opportunity of the coming decade: to reimagine partnership as co-evolution. Universities, platforms, and providers function best as interconnected actors within a wider learning system, each contributing expertise to advance learner success and institutional resilience.

    When viewed through a systems lens, the key question is no longer whether universities should outsource, but how they orchestrate. The challenge is to combine the right mix of internal capability, external expertise, and interoperable technology to achieve measurable impact.

    That, ultimately, is what digital maturity requires and what the Learning Experience Suite was designed to deliver.

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  • In learning, AI must become a co-creator, not a shortcut

    In learning, AI must become a co-creator, not a shortcut

    AI in all its multitudinous forms is here, it is here to stay, and its impacts are accelerating.

    At a basic level, we see shifts in personal office practices with the tentative, steady adoption of large language models. We see AI being used alongside MS Teams or its equivalents to quickly produce summary transcripts of meetings or to generate starting places for documents which are then reworked.

    As university educators and researchers, we also see debates regarding the ethics of AI adoption and a splintering ability of the collective and the individual to be able to discern fact from fiction. We are at the start of a long and unpredictable trajectory of impacts.

    But, as we shape the skills, knowledge and abilities in our students that will see them thrive in an increasingly disrupted future world of work, where that track takes us is a subject of debate. What is consistently clear across various predictions is that the adoption of AI and increasing automation will deliver seismic changes to the world (of work).

    Machine meets human

    86 per cent of employers surveyed for the World Economic Forum’s 2025 Future of Jobs Survey saw AI and information processing technologies as being the dominant technology driver for workplace change through to 2030, affecting workplaces across all sectors, not just those welcoming students from STEM disciplines. Similarly, the same survey indicates the greatest rise in demand in the workplace through to 2030 is for the ability to work with AI and big data.

    Noting the dominance of AI in the WEF survey findings, we are reminded of the 1998 interview between Jeremy Paxman and David Bowie, happening just as the internet was forming. Paxman queries the internet as being anything more than a “different delivery system,” while Bowie asserts that it is an alien life form:

    I don’t think we’ve even seen the tip of the iceberg – the things it will do, both good and bad are unimaginable right now. I actually think we’re on the cusp of something exhilarating and terrifying…

    Looking back at what has happened to society in the quarter of a century since that interview, Bowie is unnervingly accurate in his foresight.

    It seems that right now we are navigating similarly uncharted territories of an epoch-defining transition as the world starts to play in earnest with the next gen version of Bowie’s “alien lifeform.” Higher education is not immune – it is grappling with the challenges across its core activities.

    However, what is of particular interest beyond the specific AI skills is the other in-demand skills that occupy the places immediately following the top three noted above. Fourth is creative thinking, followed by resilience, flexibility and agility, curiosity and lifelong learning and leadership and social influence. These skills are high value cognitive competencies inherently human in their nature – an equalising “soft” counterbalance to the “hard” technological literacies of the top three.

    Reflecting on the duality between technological literacy and social, emotional and cognitive skills in this overall picture, it is clear that AI is not a replacement for the work of thought, deduction, critical reasoning and curiosity. Instead, it is a powerful augmentation to the already formidable arsenal of technological capability at our fingertips.

    From efficiency to co-creation

    With education and the student experience in mind, we see two AI “swim lanes” forming out of the early stages of ubiquity ushered in by the popularisation of ChatGPT and other LLMs. These swim lanes should also acknowledge the broader mix of new and emergent technologies at play in tandem with AI – for instance AR/VR and data visualisation.

    The first swim lane speaks to the need to optimise the complex wiring behind the institutional operations of higher education which provide our students with a world class experience. With efficiency, effectiveness and scale in mind, adoption of AI to underpin the crucial in-person experience with wider algorithmic personalisation becomes a highly desirable direction of travel. For instance, we can easily envisage a world in which AI is used to aid student navigation of module choice, tailoring the availability of elective courses and complementary extra-curricular and developmental activities.

    The second swim lane is one of invention and co-creation, arguably pushing AI and the wider ecosystem of technological innovation to be the best it can be – far beyond the deployment of convenience or efficiency. At its best, AI can become a partner in creativity: an inspirer and a critical collaborator offering new perspectives. We are seeing promising points of innovation and departure in the early work at Loughborough as the range of technological capabilities within our DigiLabs continues to be adopted at pace.

    However, to swim confidently in this lane we must dispel myths and fears with rigour and a critical navigation of AI as a co-creator. Scaffolding and skills development for staff and students are essential in order that we all might partner effectively with our new playmate.

    Thinking together

    Two points of skills development show themselves as a useful starting place towards consistency, innovation and collaboration in AI partnership. First, a good place to start would be recognition and development of prompt engineering as a fundamental digital skill and a shared structured practice. Second, it would be useful to focus on development of a consistent and structured means to better understand, interrogate and critically evaluate what the AI has generated in response to our prompting.

    With frameworks for these two essentials of effective AI partnership in place, we can move beyond the cut-and-paste AI-as-shortcut, and beyond the simple fact checking of generated material. These two skills move us towards conversing and exchanging perspectives with AI, making content better together. The vantage point of having embedded these two AI partnership skills helps us then systematically inculcate the true value of AI by recognising the human skillset with which to strategically cocreate with it, rather than shortcut with it.

    As our use of AI evolves, we should continually remind ourselves that understanding is not gained in the endpoint, but in travelling to that place (no student learns that much in the moment of a final assessment). AI becomes a meaningful companion on that journey, not a replacement for the experience of travelling. To shortcut the pleasure and frustration of our own creative and critical journeys by virtue of AI laziness is to deny ourselves the experience of our own essence – the struggle and the unknowing of what it means to question, to be alive and to be human.

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  • Securing educational excellence may demand a new leadership compact

    Securing educational excellence may demand a new leadership compact

    When education leaders describe their institutions as being in “existential crisis” or on a “wartime footing,” you know that something important is happening.

    A new report, “Securing educational excellence in higher education at a time of change,” from Wonkhe and Advance HE, based on roundtable discussions with 11 institutional leaders, 15 principal fellows of Advance HE, and three student representatives held in March 2025, explores institutional interpretation of and responses to change, and asks what measures should be taken to secure educational excellence for what could be quite a different future.

    While institutions are understandably focused on managing their immediate pressures, with, in some cases, institutional survival at stake, sustainability means little without the long-term mission of inclusive, high-quality learning that prepares students for their future lives. While financial security would help, the changes higher education is navigating require a deeper consideration of how institutions make decisions, deploy expertise, and engage their communities.

    The report maps four critical tensions that leaders are navigating across the political, economic, social and technological domains: public trust versus sector autonomy; public good versus private return on investment; traditional academic community versus new student models; pace of technological change versus institutional capacity. A fifth tension emerges from this complex environment: a need for distributed leadership that allows for a deep knowledge of the issues versus clear lines of accountability for decisions. These tensions play out daily in everything that higher education institutions do.

    A wave of change

    In the political dimension, higher education is implicated in broader losses of confidence in institutions. Though not technically public services, universities occupy a distinctive position in British civic life: historically connected to the state, still partly publicly funded, yet operating with considerable autonomy. That hybrid status leaves higher education uniquely vulnerable to simultaneous public and policymaker scrutiny.

    Higher education institutions are not insulated from the broader political landscape. Student representatives in the research raised questions about institutional awareness: “Universities believe that students are exempt from the effects of public austerity…they believe we are creating a community of highly educated people, therefore they cannot fall for the tricks and stories that the media or certain political parties are trying to tell.”

    The economic tension is similarly complex. Universities are expected to deliver public benefits without reliable public funding, creating what one participant called a “competing interest” space where higher education struggles for resources against health and compulsory education. Meanwhile, students increasingly question whether their investment yields genuine value. “Students are being taught how to meet learning objectives, but they’re not being taught how to transfer the skills that they get during their time at university, or sometimes it feels like they’re not even being taught the skills that they need just by meeting the learning objectives,” one student representative observed.

    Principal fellows echoed some of this anxiety: “Students, particularly those from a widening participation background, can put generational money into getting an education which then doesn’t give them a job.” When the compact between investment and outcome seems to break down, trust may fracture, not just between students and institutions but also between society and the higher education project.

    Socially, traditional higher education campus communities are under pressure, with students increasingly time-poor, working to afford their studies, and many commuting rather than living on campus. Participants observed that many students approach higher education more transactionally – not necessarily because they’re mercenary, but possibly because they’re exhausted. As one principal fellow observed, “student” seems to have shifted from being a core identity to something people do alongside other things.

    Meanwhile, technology raises a host of strategic questions, not only in mustering the “right” response to generative AI but also in confronting how the pace of technological change reshapes the collective imaginary of how humans and machines interact in physical and digital spaces. This has implications for curriculum and pedagogy, equity and inclusion, and infrastructure and resources.

    Staff communities appear to have fractured, too. Professional services are “somewhere else in the university,” quick informal conversations have disappeared, and academics feel “fed up and tired and exhausted.” One principal fellow described what they saw as a vicious cycle: “We do not have communities in our universities anymore, and that then impacts the students as well…we don’t have engagement from the students. But also we don’t have engagement from the academics, because they’re in a mood all the time.”

    This fragmentation has strategic implications. When communities fragment, institutions may lose the collective capacity to sense problems, develop solutions, and sustain change. Everyone risks becoming reactive rather than proactive, protective rather than collaborative.

    Change as a capability

    Rather than seeking solutions or silver bullets, our conversations explored the institutional capabilities required to navigate these complex tensions and map out a sustainable way forward.

    One key insight emerging was about the diversity and richness of knowledge and expertise held within institutions that may not be routinely accessed in efforts to think about the future. Small executive teams may struggle to retain a grip on every aspect of the changing landscape or simply become bogged down in maintaining the day-to-day flow of decisions that keep institutions running. Under this kind of pressure, it might not be surprising that, as one principal fellow put it, “Leaders often talk too much and listen too little.”

    The report suggests leaders need to become curators of inclusive processes rather than authorities on every challenge. This would require the confidence to admit when situations are difficult and to seek help – a cultural shift that, if modelled from the top, could potentially reduce pressure on others to hide their struggles.

    Student representatives echoed this sense that efforts to consult or engage, if not well conceived, can sometimes be more alienating than empowering. One student leader suggested involving students in shaping the collective understanding of problems from the beginning, at which their experience and knowledge are most likely to make a meaningful contribution, rather than asking student representatives to comment on pre-developed expert solutions. The same principle could apply to higher education staff and stakeholders.

    There were also clear themes of the need for authenticity when professing an appetite for change and a pragmatic approach to resourcing it. Participants noted that institutions advertise for “innovators” and “change agents” but may not truly want them, or don’t adequately support them when they arrive. Change might require investment: stable contracts, professional development, and time for pedagogic innovation. “You can’t shift pedagogy if you don’t create time,” observed one principal fellow.

    In the technological domain, where there may be a belief that the issues are fundamentally about resourcing and retaining technical expertise, part of the question has to be about how technology reshapes staff and student experience and sustains or fragments human connection. One principal fellow observed that higher education’s “killer service” might be personal connection, not consumer-grade content production in an attention economy. However, delivering that would require investing in people, not just platforms.

    A question of purpose

    Among education leaders, there was a real recognition that higher education staff are “the most precious resource,” as one put it. Yet the changing landscape for higher education seems to be broadening the range of possible purposes for higher education, along with the range of stakeholders who feel entitled to a view about what educational excellence looks like.

    It is not hard to see how this changing dynamic can alienate academics working in disciplines who may perceive some of their core “knowledge stewardship” values and purposes as being under threat from political, economic, social, and technological changes in the external landscape driving different expectations of higher education.

    With an unknowable future, the answer is less about seeking certainties to cling to as about finding collective ways to navigate uncertainty. That might open up some uncomfortable propositions: that higher education’s purpose itself may need rearticulating; that trade-offs between competing goods must be explicitly managed; that excellent pedagogy might require resource investment even when budgets are tight; and that sustainable change may emerge more from dialogue than from executive decision-making.

    The full report repays careful reading, not just for its PEST analysis framework, which could help guide your own institutional conversations about change, but for the candour of participants grappling with genuine complexity. Higher education may face a “pivot point” – though the sector’s breadth, diversity, and expertise remain a considerable strength. Weathering the changes here right now and those on the horizon will depend to no small degree on institutional leadership capability to draw on that expertise to build a shared and collectively owned sense of educational excellence.

    This article is published in association with Advance HE. You can read and download the full Securing educational excellence at a time of change report here.

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  • Preparing students for the world of work means embracing an AI-positive culture

    Preparing students for the world of work means embracing an AI-positive culture

    When ChatGPT was released in November 2022, it sent shockwaves through higher education.

    In response, universities moved at pace during the first half of 2023 to develop policy and good practice guidance for staff and students on appropriate use of GenAI for education purposes; the Russell Group’s Principles on the use of generative AI tools in education are particularly noteworthy. Developments since, however, have been fairly sluggish by comparison.

    The sector is still very much at an exploratory phase of development: funding pilots, individual staff using AI tools for formative learning and assessment, baseline studies of practice, student and staff support, understanding of tools’ functionality and utilisation etc. The result is a patchwork of practice not coherent strategy.

    Yet AI literacy is one of the fastest growing skills demanded by industry leaders. In a survey of 500 business leaders from organisations in the US and UK, over two-thirds respondents considered it essential for day-to-day work. Within AI literacy, demand for foundation skills such as understanding AI-related concepts, being able to prompt outputs and identify use cases surpassed demand for advanced skills such as developing AI systems.

    Students understand this too. In HEPI’s Student generative AI survey 2025 67 per cent of student respondents felt that it was essential to understand and use AI to be successful in the workplace whereas only 36 per cent felt they had received AI skill-specific support from their institution.

    There is a resulting gap between universities’ current support provision and the needs of industry/ business which presents a significant risk.

    Co-creation for AI literacy

    AI literacy for students includes defining AI literacy, designing courses aligned with identified learning outcomes, and assessment of those outcomes.

    The higher education sector has a good understanding of AI literacy at a cross disciplinary level articulated through several AI literacy frameworks. For example, UNESCO’s AI Competency Framework for Students or the Open University in the UK’s own framework. However, most universities have yet to articulate nuanced discipline-specific definitions of AI literacy beyond specialist AI-related subjects.

    Assessment and AI continues to be a critical challenge. Introducing AI tools in the classroom to enhance student learning and formatively assess students is fairly commonplace, however, summative assessment of students’ effective use of AI is much less so. Such “authentic assessments” are essential if we are serious about adequately preparing our students for the future world of work. Much of the negative discourse around AI in pedagogy has been around academic integrity and concerns that students’ critical thinking is being stifled. But there is a different way to think about generative AI.

    Co-creation between staff and students is a well-established principle for modern higher education pedagogy; there are benefits for both students and educators such as deeper engagement, shared sense of ownership and enhanced learning outcomes. Co-creation in the age of AI now involves three co-creators: students, educators and AI.

    Effective adoption and implementation of AI offers a range of benefits specific to students, specific to educators and a range of mutual benefits. For example, AI in conjunction with educators, offers the potential for significantly enhancing the personalisation of students’ experience on an on-demand basis regardless of the time of day. AI can also greatly assist with assessment processes such as marking turnaround times and enhanced consistency of feedback to students. AI also allows staff greater data-driven insights for example into students at risk of non-progression, areas where students performed well or struggled in assessments allowing targeted follow up support.

    There is a wealth of opportunity for innovation and scholarship as the potential of co-creation and quality enhancement involving staff, students and AI is in its infancy and technology continues to evolve at pace.

    Nurturing an AI-positive culture

    At Queen Mary University of London, we are funding various AI in education pilots, offering staff development programmes, student-led activities and through our new Centre for Excellence in AI Education, we are embedding AI meaningfully across disciplines. Successfully embedding AI within university policy and practice across the breadth of operations of the institution (education, research and professional practice), requires an AI-positive culture.

    Adoption of AI that aligns with the University’s values and strategy is key. It should be an enabler rather than some kind of add-on. Visible executive leadership for AI is critical, supported by effective use of existing champions within schools and faculties, professional services and the student body to harness expertise, provide support and build capacity. In some disciplines, our students may even be our leading institutional AI experts.

    Successful engagement and partnership working with industry, business and alumni is key to ensure our graduates continue to have the necessary skills, knowledge and AI literacy to achieve success in the developing workplace.

    There is no escaping the fact that embedding AI within all aspects of a university’s operations requires significant investment in terms of technology but also its people. In our experience, providing practical support through CPD, case studies, multimedia storytelling etc whilst ensuring space for debate are essential for a vibrant, evolving community of practice.

    A key challenge is trying to maintain oversight and co-ordinate activities in large complex institutions in a field that is evolving rapidly. Providing the necessary scaffolding in terms of strategy and policy, regulatory compliance and appropriate infrastructure whilst ensuring there is sufficient flexibility to allow agility and encourage innovation is another key factor for an AI-positive culture to thrive.

    AI is reshaping society and building an AI-positive culture is central to the future of higher education. Through strategic clarity and cultural readiness, universities need to effectively harness AI to enhance student learning, support staff, improve productivity and prepare students for a changing world.

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  • For students, the costs of failure are far too high

    For students, the costs of failure are far too high

    Back in May, I argued that the UK’s “pace miracle” – the system that produces the youngest, fastest-completing graduates in Europe – is damaging students’ learning and health.

    Our system’s efficiency, I suggested, comes at the cost of pressure, exhaustion, and a creeping normalisation of distress.

    But what happens when students fall behind in that miracle? What happens when someone breaks the rhythm that the entire funding and regulatory framework assumes to be normal?

    For our work with SUs, Mack Marshall and I have been looking in detail at the rules and funding that surround “retrieval”.

    From what we can see, UK higher education doesn’t just expect rapid completion – it punishes deviation from it.

    When students stumble, the architecture designed to retrieve them from failure taxes disadvantage and rewards privilege.

    The illusion of generosity

    Pretty much every university we’ve looked at has policies designed to look fair. There is almost always a promise of one reassessment opportunity, and increasingly a public line about not charging resit fees. On paper, that sounds humane – but in practice, the design is economically brutal.

    When a student fails a module and resits within the same academic year, the direct cost may be zero. But there’s no maintenance support for any extra study they need to do. And if that student is placed on reassessment-only status for the following year – allowed to resit assessments without attending teaching – they become ineligible for maintenance funding for much, much longer.

    That means no support for rent, bills, or food for months. The student who can rely on family help revises in comfort. The student who can’t works full-time through summer and fails again, or drops out entirely.

    The sector calls the resit “free” and congratulates itself on removing barriers. But the barrier was never the invoice – it was the maintenance cliff.

    This is not a marginal anomaly – it’s the structural product of the same system that glorifies pace. It’s a logic that insists most degrees must be achieved within three years – one that also dictates that recovery from failure must happen outside the funded frame.

    To understand what happens to students who fail, students need to navigate a maze of regulations, finance policies, visa rules, and handbooks – each written in its own dialect of compliance.

    Students from professional families likely know where to look and what questions to ask. They have the vocabulary, the contacts, the confidence, while first-generation students rarely do. They may well discover “compensation” rules only after exam boards meet, and learn about extenuating circumstances after the deadline passes.

    The result is an information economy that mirrors the class system. The retrieval framework may be universal, but its navigation costs are socially distributed.

    The poverty penalty v pedagogy

    When students pass a module on reassessment, their mark is often capped at the pass threshold – 40 per cent for undergraduates, maybe 50 per cent for postgraduates. The principle sounds rigorous, but the reality is punitive.

    A student who failed once because they were caring for a parent, working nights, or suffering mental ill-health can never escape the academic scar tissue unless it’s a complex and approved mit-circs application. The capping rule converts a temporary difficulty into a permanent credential penalty.

    It is the same ideology that underpins the pace miracle – a meritocracy of difficulty that romanticises struggle and treats rest as weakness. Only it is encoded in assessment policy rather than culture.

    For international students, the same logic takes on a bureaucratic form. Those who fail a single module often face a choice between reassessment-only status – which ends their visa – or repeating with attendance purely to remain sponsored.

    Repeating with attendance can cost thousands of pounds in tuition and visa fees. Many have no realistic option but to pay. The system enforces what looks like a market choice – but is in practice compulsion.

    The Lifelong Learning Entitlement – fix or mirage

    In England at least, the forthcoming Lifelong Learning Entitlement (LLE) ought to usher in flexibility. Funding will finally be linked to credits rather than years. Students will be able to study, pause, and return across their lifetimes. In theory, that should dismantle the rigid three-year cage.

    But in practice, everything will depend on how universities classify students, and how they’re allowed to resit. If reassessment-only learners are still coded as “not in attendance”, they still fall outside maintenance entitlement. The policy will have modernised the vocabulary of exclusion without addressing its cause.

    And even when students do qualify, the LLE’s promise of proportional maintenance means something subtle but serious – flexibility is offered as additional debt, not as forgiveness. Students who fall behind because of illness or bereavement will borrow more, not owe less.

    Unless maintenance is reconceived as a right to recovery rather than a privilege of progression, the LLE risks becoming a faster, more efficient version of the same trap.

    Across Europe, completion frameworks are slower and more forgiving. Some countries permit students a decade to complete a bachelor’s degree without financial penalty. Temporary setbacks don’t trigger existential crises – because variations in time are built into the design.

    As I referenced here, the HEDOCE project found that students in systems with longer completion horizons are less likely to drop out entirely and more likely to recover from setbacks. Those systems treat time as a pedagogical resource, not an efficiency problem.

    In contrast, our compressed model leaves no room for error. Once you stumble, the treadmill doesn’t slow down – it throws you off.

    Beyond efficiency

    Our systems for “retrieval” are not an isolated bureaucracy. They’re the endpoint of a philosophy – the same one I explored in the “pace miracle” piece. Both the speed and the punishment are symptoms of a culture that prizes output over understanding, and throughput over humanity.

    When the system is calibrated around efficiency, every deviation becomes failure, and every failure becomes costly. The student who needs time is framed as wasteful – and the institution that supports them risks financial loss.

    I suspect that is why academic pressure now appears so often in mental health reviews. The structure of funding itself generates the anxiety we later medicalise – what looks like individual struggle is really systemic design.

    If we genuinely wanted a system that supports learning rather than policing pace, we would start by aligning time, funding, and compassion.

    Maintenance support would continue for students on reassessment-only status. Resit marks would reflect achievement, not past misfortune. Compensation and extenuating circumstances policies would be clear, accessible, and generous.

    And more profoundly, universities would stop treating recovery as inefficiency. Every student who fails and returns would be evidence of persistence, not profligacy.

    In England, the LLE could be a turning point – a framework that finally recognises learning as cyclical and non-linear. Or it could simply re-brand the same cruelty in the language of flexibility.

    When I wrote about the UK’s “pace miracle”, I argued that we have built a higher education system that prizes speed and punishes delay – a model that achieves impressive completion rates at the cost of wellbeing, mastery, and fairness.

    Our retrieval systems are the mirror image of that miracle. One governs what happens when students move too slowly during the race – the other governs what happens when they fall altogether. Both reveal the same problem – UK HE mistakes motion for progress, and speed for success.

    A humane higher education system would not just help students recover from failure – it would stop treating recovery as failure in the first place.

    Until then, our miracle of efficiency will continue to hide a quiet cruelty. The students least able to afford failure will remain those the system punishes most heavily – not because they lacked talent or effort, but because we built a structure that makes time itself the privilege they can rarely get a loan for.

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  • Not knowing is the start of learning

    Not knowing is the start of learning

    “I don’t know” is an underrated student response.

    If viewed in a positive light instead of as a lack of understanding or a fault, it can become a catalyst for enquiry, supporting students with their research and knowledge building skills.

    What does “I don’t know” mean?

    Picture yourself as a student who has been asked a direct question during a lecture.

    This was a position I found myself in on several occasions during my own undergraduate science degree course. Sometimes I would know the answer and be able to respond confidently – relieved. On other occasions, perhaps not coming up with the answer immediately, I would default to “I don’t know”.

    Many academics recall a particular lecturer who motivated them to succeed. For me, this lecturer emerged as a mentor during my own MSc in chemistry. He used to hold challenging tutorials, if  I asked a difficult question, there was nowhere to go. I simply had to stay with the moment and work through the question.

    I didn’t realise it at the time, but this helped me find a starting point for figuring out things I didn’t understand and embracing the discomfort that comes with not understanding something…yet!

    More questions

    Why do we ask students questions? Questions can be posed to the entire room, known as open questioning. This type of question can work well at the beginning of a session or when we want to offer choice in terms of who wishes to answer a question. We can also ask objective, subjective or speculative questions.

    Or we can pose direct questions to specific, individual, students. Their use may seem like quite an intense approach but can offer benefits. Directed questions can create a “high pressure, high stakes” atmosphere, it is often one that is more memorable for the individual involved and allows the lecturer to assess whether that individual understands the topic at hand. It presents a mechanism for the student to check their understanding and to build resilience by answering under pressure.

    It can also act as a gateway to Socratic questioning, which can allow the student or wider attending group to explore the topic being studied in more depth and with greater thought.

    Working as a lecturer in both further education (with BTEC students) and higher education institutions, I have gained experience with how to support students through these moments and how to make the questioning process less daunting.

    It is easy to take “I don’t know” at face value, believing that a student really does not know the answer to a given question. However, “I don’t know” could be a default answer for something completely different.

    “I don’t know” could mean: “I need time to think about that”, “I didn’t hear what you asked”, “I don’t want to answer in front of…”, “I don’t like being put on the spot”, “I’m not interested”, “I’m not sure if the answer I’m thinking of is correct” or even… “I don’t know”.

    How we respond is something to think about.

    Conversational, not confrontational

    As universities (across the UK and globally) embrace active collaborative learning approaches, the traditional lecture has sometimes come to be viewed as didactic in a negative sense.

    Evidence presented following a 2019 report by Nottingham Trent University, Anglia Ruskin University and University of Bradford has shown that active collaborative learning methods such as team-based learning create engaging learning environments with positive links to progression and attainment. Nottingham Trent University has followed up through a university-wide TBL pilot study during the 2024-25 academic year.

    Interactive lectures can act as a “half-way house” between traditional lectures and active collaborative learning sessions. Effective questioning strategy can make them more engaging. When lectures are interactive, open, directed and Socratic questioning can be sprinkled in using a non-confrontational approach, such that the questions become part of the conversation and are no longer perceived as an unwelcome assessment of knowledge.

    The important thing is how the lecturer approaches this; an effective application being one where students can feel comfortable answering the questions posed. Importantly, asking the correct questions, will help students to leap from where they currently are, with a project for example, to what they could potentially explore next, or to what their results could possibly mean. “How do you think that process happens?”, “What do you think about that?” or “What would it mean if you got the opposite result?”, are a few examples of questions we could ask to encourage a student to dig deeper.

    Using questions to frame conversations can create this exploratory environment where an initial not knowing can lead to the confidence to learn more about the topic being studied, moving further into Vygotsky’s zone of proximal development.

    Enjoy the silence

    Whether in a large lecture theatre, an active collaborative learning room, a small workshop session or an online session, questions can be posed and time given for the answers to come.

    As lecturers posing questions to students, we need to remember to give students time to answer the question or to think about a possible answer. It is common to only allow a few seconds before jumping back in to prompt the student, to bounce the question to someone else or even for us to answer it yourself.

    Building in thinking time can make the difference. Feeling even stranger in a silent, online environment, it’s important to allow the silence and discomfort to fill the space and wait for an answer – any answer – even “I don’t know” to break through! Then, there is something to work with.

    Turning the heat up – or down

    As lecturing academics, we also have the responsibility to turn down the heat if we can see that a questioning experience is becoming too intense for a given student or group of students. Questioning should be challenging but not traumatic – know when to pull back.

    Having knowledge of your students is the best way of managing this as one can be aware of a student’s profile, background and temperament or how much they enjoy engaging with an interactive questioning approach. For some students, it may not be effective to pose directed questions, particularly in front of a large audience. Think “How will this student respond if I ask them a directed question?” “Will it help them develop their understanding and build resilience, or will it be too much for them?”

    For such students, weaving in discussion during group or individual activities in a conversational way may be the best approach to gauge their understanding. For larger cohorts, where we may not know the temperament or preference of all students, intuition and experience can be the key, allowing us to pose questions and then decide whether to persist or perhaps back off and move on – potentially returning to discuss the topic with that student later or in a different session.

    And it is important to return to the reason we pose questions. Questioning is more than transactional. If used effectively, it can help us to understand what our students are learning and thinking about, and that can generate real discussion. “Do my students understand this topic?”, “Can my students explain what is happening in this experiment?” or “Are they enjoying it?”.

    Taking a question path approach, students can also learn to use this process, applying enquiry-based learning as they explore their subjects of study independently

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