Tag: knowledge

  • My Posts from Jarche’s Personal Knowledge Mastery Workshop – Teaching in Higher Ed

    My Posts from Jarche’s Personal Knowledge Mastery Workshop – Teaching in Higher Ed

    As part of participating in Harold Jarche’s Personal Knowledge Mastery workshop, we were given lessons and activities three times a week for six weeks. I had been blogging perhaps once or twice a year for a while now, never feeling like I had found my voice with those posts. Doing that much sharing via the written form seemed daunting, yet I had a strong suspicion that the discipline would pay off. I was not wrong at all on that front.

    Here are the various posts I wrote, along with an overview of the concepts explored in each one.

    01 – Getting Curious About Network Mapping

    Great insight lies in visualizing and analyzing the relationships that surround our work and learning. Networks are fundamental lenses for how we connect, influence, and grow.

    Key themes:

    • Network mapping and the difference between strong ties and weak ties (and how both kinds are essential to a thriving learning network).
    • The habit of giving first and nurturing relationships as network fuel.

    Quote:

    “Most intuitive notions of the “strength” of an interpersonal tie should be satisfied by the following definition: the strength of a tie is a (probably linear) combination of the amount of time, the emotional intensity, the intimacy (mutual confiding), and the reciprocal services which characterize the tie.” — Mark S. Granovetter (1973)

    Both strong and weak ties are vital to our learning.

    02 – Let’s Get Curious

    Allowing ourselves to wonder opens up our capacity to learn, connect, and co-create more deeply.

    Key themes:

    • Sparking curiosity means we tap into a power well beyond certainty (as illustrated so well through this beloved clip from Ted Lasso).
    • The world of work is increasingly complex; the very skills that matter now include creativity, imagination, empathy and curiosity.

    Quote:

    “The skills required to live in a world dominated by complex and non-routine work requires — creativity, imagination, empathy, and curiosity.” — Harold Jarche

    Stay curious, widen our lenses, and lean into the discomfort of not-knowing as the gateway to meaningful growth.

    03 – Connecting Birds, Grief, and Communities

    Grief, networks, and belonging are deeply intertwined in shaping the places where we learn, grow, and support one another.

    Key themes:

    • The isolation that grief can bring creates a powerful invitation to community when we’re willing to show up with vulnerably.
    • Communities (using Mastodon) and how we sustain communities when the baskets we placed our eggs in (platforms, networks) change or disappear and what that means for our learning ecosystems (I didn’t write about this in the post, but many say the answer is federated networks)

    Quote:

    “If we put our metaphorical eggs in one basket and something happens to that basket, there’s no putting Humpty Dumpty back together again.” — Bonni Stachowiak

    Invest in communities that embrace complexity, invite connection across networks, and hold space for both loss and belonging.

    04 – Engaging with Intentionality and Curiosity

    As I reflected on intentionality this week, I realized that showing up with purpose—not just going through the motions—significantly shapes what I notice, how I respond, and who I become in the process.

    Key themes:

    • Intentionality helps clarify why something matters and helps resist the pull of the urgent and focus on the important.
    • Analyzing who Harold Jarche follows on Mastodon offered an opportunity to reflect on my aims for the network.

    Quote:

    “Show up for the work.” — Bonni Stachowiak

    Jarche also gave some examples of the practices on which PKM is built upon, such as narrating our work and sharing half-baked ideas.

    05 – Scooping Up Adulting and the Benefits of Being Curious

    Moving through life’s messy, liminal spaces requires curiosity, humility, and movement.

    Key themes:

    • The relevance of the Cynefin framework in helping us learn in the complex domain.
    • The value of formal and informal communities and open knowledge and formal knowledge networks as our learning ecology.
    • Curiosity as a pathway through liminality: staying attuned to what is becoming.

    Quote:

    “In a crisis it is important to act but even more important to learn as we take action.” — Harold Jarche

    This Learning in the Complex Domain post by Jarche is likely the most important one for me to revisit from all that I read throughout these six weeks, as I’m still struggling to understand the Cynefin framework.

    06 – Why Isn’t RSS More Popular By Now?

    It’s still wild to me that RSS isn’t as common as navigating websites.

    Key themes:

    • A well-curated set of feeds via an RSS aggregator turns passive reading into active sense-making.
    • RSS remains undervalued in the age of algorithmic feeds, yet when we control our own feed-ecosystem we reclaim agency over where our attention goes.

    Quote:

    However, I’m picky about my reading experience and have gotten particular about being able to read via Unread on my iPad and navigate everything with just one thumb. — Bonni Stachowiak

    I was also glad to learn from Jarche about subscribing to Mastodon feeds and hashtags via RSS, though I haven’t experimented with that much, yet, since the Tapestry app does a lot of that for me.

    07 – Can You Keep a Secret?

    Understanding the frameworks behind our media tools unlocks far deeper insights than simply reacting to what comes our way.

    Key themes:

    • Exploring Marshall McLuhan’s Media Tetrad helped me see every medium as doing four things: extending, retrieving, obsolescing, and reversing.
    • Applying the tetrad to the smartphone made visible how it extends access and connection, obsolesces older single-purpose devices, retrieves communal spaces, and reverses into distraction and isolation when pushed too far.
    • This kind of analysis invites me to pause, notice, and interrogate the media I use daily rather than assume they’re neutral or benign.

    Quote:

    “The reversals are already evident — corporate surveillance, online orthodoxy, life as reality TV, constant outrage to sell advertising. The tetrads give us a common framework to start addressing the effects of social media pushed to their limits. Once you see these effects, you cannot un-see them.” — Harold Jarche

    Analyzing these media tools heps us choose how to engage with them, rather than passively being shaped by them.

    08 – Fake News Brings Me to an Unusual Topic for this Blog

    It is critical to engage in ways to increase the likelihood of us being able to identify fake news. .

    Key themes:

    • The articulation of four primary types of fake news — propaganda, disinformation, conspiracy theory, and clickbait — as outlined by Harold Jarche.
    • How propaganda intentionally spreads ideas to influence or damage an opposing cause; disinformation deliberately plants falsehoods to obscure truth.
    • The persistence of conspiracy theories despite lacking evidence, and how clickbait uses sensationalism to manipulate attention and action.

    Quote:

    Misinformation implies that the problem is one of facts, and it’s never been a problem of facts. It’s a problem of people wanting to receive information that makes them feel comfortable and happy. – Renée DiResta, as quoted in El País

    Our identities get so wrapped up in what we believe, it can be so challenging to consider how we might be part of combating fake news in our various contexts.

    09 – From Half-Baked to Well-Done: Building a Sensemaking Practice

    It can be so generative to share thoughts before they’re polished and this openness fuels learning, creativity, and connection.

    Key themes:

    • Half-baked ideas make space for iteration: they invite others in, rather than presenting a finished product that shuts conversation down.
    • Sharing early thinking helps me stay curious, flexible, and less attached to being “right.”
    • When we release ideas in progress, we give our networks something to build on, remix, or nudge in new directions.

    Quote:

    If you don’t make sense of the world for yourself, then you’re stuck with someone else’s world view. — Harold Jarche

    Let ideas be emergent rather than complete so that learning can unfold collaboratively.

    10 – The Experts in My Neighborhood

    Jarche introduces us to various PKM roles for this topic.

    Key themes:

    • Our learning ecosystems benefits from curating a diverse set of experts to help navigate complexity.
    • Through my PKMastery practices (bookmarking, sense-making, sharing), I can engage with expert ideas over time.
    • The real value comes not from one “expert,” but from a network of thinkers whose disagreements and different perspectives stretch our own thinking.

    Quote:

    “Writing every day is less about becoming someone who writes, and more about becoming someone who thinks.” — JA Westenberg

    The value of PKM is in curating many voices, cultivating a “neighborhood” of experts to follow, listen, question, and to build a rich, networked sensemaking practice rather than rely on single voices alone.

    11 – Network Weaving as an Antidote to Imposter Syndrome

    Turning toward connection can be one of our strongest antidotes to imposter syndrome.

    Key themes:

    • Network weaving reframes “Do I belong here?” to “Who can I bring together?” — shifting the energy from proving my worth to creating belonging.
    • Connecting people, ideas, and stories becomes my purpose: not to be the smartest person in the room, but to serve as a bridge, curator, and connector.
    • Vulnerability matters: acknowledging I don’t have all the answers, but inviting others to learn out loud anyway.

    Quote:

    A triangle exists between three people in a social network. An “open triangle” exists where one person knows two other people who are not yet connected to each other — X knows Y and X knows Z, but Y and Z do not know each other. A network weaver (X) may see an opportunity or possibility from making a connection between two currently unconnected people (Y and Z). A “closed triangle” exists when all three people know each other: X-Y, X-Z, Y-Z. – Valdis Krebs

    This reminder feels like fuel for the next leg of my PKMastery journey — leaning into weaving networks as practice not just for growth, but for belonging and shared strength.

    12 – I Can See Clearly Now The Frogs Are Here

    Growth often comes not from jumping to answers but from staying curious, experimenting, and traveling alongside fellow learners.

    Key themes:

    • Fellow seekers offer empathy, solidarity, and space to wrestle with ideas, often more supportively than experts alone.
    • As described by Harold Jarche, combining curiosity with connection can help transform seekers into knowledge catalysts, nodes in our networks who learn, curate, and contribute meaningfully.
    • Innovation and insight often emerge through playful experiments (half-baked ideas) from the beginner’s mind held by seekers.

    Quote:

    Your fellow seekers can help you on a journey to become a Knowledge Catalyst, which takes parts of the Expert and the Connector and combines them to be a highly contributing node in a knowledge network. We can become knowledge catalysts — filtering, curating, thinking, and doing — in conjunction with others. Only in collaboration with others will we understand complex issues and create new ways of addressing them. As expertise is getting eroded in many fields, innovation across disciplines is increasing. We need to reach across these disciplines. — Harold Jarche

    Seeking is not a sign of weakness, but as a source of collective curiosity, connection, and growth.

    13 – What Happens When We Start Making the Work Visible

    There is strength in making invisible processes and decisions visible.

    Key themes:

    • When we narrate our work, we open up pathways for real-time collaboration and shared learning rather than one-way transmission.
    • Narration allows for experimentation: sharing work in progress de-commodifies knowledge.
    • It shifts the emphasis from polished deliverables to ongoing learning — not just focusing on the final product, but how we got there, and what we learned along the way.

    Quote:

    The key is to narrate your work so it is shareable, but to use discernment in sharing with others. Also, to be good at narrating your work, you have to practice. — Harold Jarche

    Narrating our work offers a window into our process of learning.

    14 – No Frogs Were Actually Harmed in Describing Systems Thinking

    As I reflected on systems thinking, I found myself returning to how challenging (and how necessary) it is to see beyond events and into the structures that shape them. Revisiting Senge’s The Fifth Discipline reminded me just how often we can slip into reacting instead of zooming out to notice patterns.

    Key themes:

    • How easy it is to fall into organizational “learning disabilities,” like assuming I am my position rather than part of a larger whole.
    • Chris Argyris describes the phenomenon of “skilled incompetence,” where groups of individuals who get super good at making sure to prevent themselves from actually learning.
    • The invitation to practice systems thinking collectively, not just individually.

    Quote:

    You can only understand the system of a rainstorm by contemplating the whole, not any individual part of the pattern. – Peter Senge

    Sitting with this reminded me that lest we fall victim to skilled incompetence, we need to continually nurture the humility and curiosity to keep looking wider, deeper, and more generously at the forces shaping our organizations and our work.

    15 – Asking as a Way of Knowing: PKM Embodied By Bryan Alexander

    The potential for adding value through PKM helps make our contributions much richer when paired with curiosity, generosity, and intentional sharing.

    Key themes:

    • PKM isn’t just about what I read or bookmark — it’s about how I transform that input through asking questions, sense-making, and offering what I learn into shared spaces.
    • Public sharing (through podcasting, writing, conversation) complements private learning — the two together deepen meaning and foster connection.
    • “Adding value” can look like holding space for others’ learning — asking curious questions, offering resources, and modeling openness rather than trying to prove expertise.

    Quotes:

    Every person possessing knowledge is more than willing to communicate what he knows to any serious, sincere person who asks. The question never makes the asker seem foolish or childish — rather, to ask is to command the respect of the other person who in the act of helping you is drawn closer to you, _likes you better_ and will go out of his way on any future occasion to share his knowledge with you. — Maria Popova

    It was great getting to see this all in action, through a dinnertime conversation with Bryan Alexander.

    16 – The Gap

    Fear and self-doubt often keeps us from beginning and from recognizing how much value we hold even before we “arrive.”

    Key themes:

    • There’s often a gap between where we are now and where we want to be — but that gap doesn’t diminish the worth of what we’re already learning and creating.
    • True learning requires embracing vulnerability: pursuing new practices.
    • Public sharing matters: showing work in progress reminds me (and others) that learning is ongoing and that we don’t need to wait until we’re “expert enough” to contribute something meaningful.

    Quote:

    “The biggest gap is between those doing nothing and those doing something.” — Tim Kastelle

    Commit to practice, to sharing, and to staying open to becoming someone who learns out loud.

    17 – Walking With PKM: Reflections From Six Weeks of Practice

    Stepping away from busyness — even just to wander — creates the space for real insight and creative thinking.

    Key themes:

    • Walking becomes a practice of reflection: giving my brain space to wander and surface ideas.
    • Learning isn’t always quantifiable.
    • The value in a consistent PKM practice allows me to my own capacity to notice, wonder, and ultimately learn.

    Quote:

    Creative work is not routine work done faster. It’s a whole different way of work, and a critical part is letting the brain do what it does best — come up with ideas. Without time for reflection, most of those ideas will get buried in the detritus of modern workplace busyness. — Harold Jarche

    PKM is part discipline, part letting go of the busyness, and part listening to whatever emerges.

    18 – The Last Step Toward the First Step

    “Mastery” is not an endpoint, but a habitual practice of learning, sharing, and growing.

    Key themes:

    • Value lies not in perfection, but in consistency: the small acts of sharing half-baked ideas and imperfect work.
    • What I do contributes to a larger learning ecosystem: by sharing what I learn, I contribute to collective sense-making and encourage others to do the same.

    Quote:

    It is not being in the know, but rather having to translate between different groups so that you develop gifts of analogy, metaphor, and communicating between people who have difficulty communicating to each other. — Ronald Burt

    The real power of PKM shows up not at the end, but in the consistent rhythm of seeking, sensing, and sharing.

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  • Can we close the “knowledge gap”?

    Can we close the “knowledge gap”?

    Can we close the “knowledge gap”?

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  • The new AI tools are fast but can’t replace the judgment, care and cultural knowledge teachers bring to the table

    The new AI tools are fast but can’t replace the judgment, care and cultural knowledge teachers bring to the table

    by Tanishia Lavette Williams, The Hechinger Report
    November 4, 2025

    The year I co-taught world history and English language arts with two colleagues, we were tasked with telling the story of the world in 180 days to about 120 ninth graders. We invited students to consider how texts and histories speak to one another: “The Analects” as imperial governance, “Sundiata” as Mali’s political memory, “Julius Caesar” as a window into the unraveling of a republic. 

    By winter, our students had given us nicknames. Some days, we were a triumvirate. Some days, we were Cerberus, the three-headed hound of Hades. It was a joke, but it held a deeper meaning. Our students were learning to make connections by weaving us into the histories they studied. They were building a worldview, and they saw themselves in it. 

    Designed to foster critical thinking, this teaching was deeply human. It involved combing through texts for missing voices, adapting lessons to reflect the interests of the students in front of us and trusting that learning, like understanding, unfolds slowly. That labor can’t be optimized for efficiency. 

    Yet, today, there’s a growing push to teach faster. Thousands of New York teachers are being trained to use AI tools for lesson planning, part of a $23 million initiative backed by OpenAI, Microsoft and Anthropic. The program promises to reduce teacher burnout and streamline planning. At the same time, a new private school in Manhattan is touting an AI-driven model that “speed-teaches” core subjects in just two hours of instruction each day while deliberately avoiding politically controversial issues. 

    Marketed as innovation, this stripped-down vision of education treats learning as a technical output rather than as a human process in which students ask hard questions and teachers cultivate the critical thinking that fuels curiosity. A recent analysis of AI-generated civics lesson plans found that they consistently lacked multicultural content and prompts for critical thinking. These AI tools are fast, but shallow. They fail to capture the nuance, care and complexity that deep learning demands. 

    Related: A lot goes on in classrooms from kindergarten to high school. Keep up with our free weekly newsletter on K-12 education.  

    When I was a teacher, I often reviewed lesson plans to help colleagues refine their teaching practices. Later, as a principal in Washington, D.C., and New York City, I came to understand that lesson plans, the documents connecting curriculum and achievement, were among the few steady examples of classroom practice. Despite their importance, lesson plans were rarely evaluated for their effectiveness.  

    When I wrote my dissertation, after 20 years of working in schools, lesson plan analysis was a core part of my research. Analyzing plans across multiple schools, I found that the activities and tasks included in lesson plans were reliable indicators of the depth of knowledge teachers required and, by extension, the limits of what students were asked to learn. 

    Reviewing hundreds of plans made clear that most lessons rarely offered more than a single dominant voice — and thus confined both what counted as knowledge and what qualified as achievement. Shifting plans toward deeper, more inclusive student learning required deliberate effort to incorporate primary sources, weave together multiple narratives and design tasks that push students beyond mere recall. 

     I also found that creating the conditions for such learning takes time. There is no substitute for that. Where this work took hold, students were making meaning, seeing patterns, asking why and finding themselves in the story. 

    That’s the transformation AI can’t deliver. When curriculum tools are trained on the same data that has long omitted perspectives, they don’t correct bias; they reproduce it. The developers of ChatGPT acknowledge that the model is “skewed toward Western views and performs best in English” and warn educators to review its content carefully for stereotypes and bias. Those same distortions appear at the systems level — a 2025 study in the World Journal of Advanced Research and Reviews found that biased educational algorithms can shape students’ educational paths and create new structural barriers. 

    Ask an AI tool for a lesson on westward expansion, and you’ll get a tidy narrative about pioneers and Manifest Destiny. Request a unit on the Civil Rights Movement and you may get a few lines on Martin Luther King Jr., but hardly a word about Ella Baker, Fannie Lou Hamer or the grassroots organizers who made the movement possible. Native nations, meanwhile, are reduced to footnotes or omitted altogether. 

    Curriculum redlining — the systematic exclusion or downplaying of entire histories, perspectives and communities — has already been embedded in educational materials for generations. So what happens when “efficiency” becomes the goal? Whose histories are deemed too complex, too political or too inconvenient to make the cut? 

    Related: What aspects of teaching should remain human? 

    None of this is theoretical. It’s already happening in classrooms across the country. Educators are under pressure to teach more with less: less time, fewer resources, narrower guardrails. AI promises relief but overlooks profound ethical questions. 

    Students don’t benefit from autogenerated worksheets. They benefit from lessons that challenge them, invite them to wrestle with complexity and help them connect learning to the world around them. That requires deliberate planning and professional judgment from a human who views education as a mechanism to spark inquiry. 

    Recently, I asked my students at Brandeis University to use AI to generate a list of individuals who embody concepts such as beauty, knowledge and leadership. The results, overwhelmingly white, male and Western, mirrored what is pervasive in textbooks.  

    My students responded with sharp analysis. One student created color palettes to demonstrate the narrow scope of skin tones generated by AI. Another student developed a “Missing Gender” summary to highlight omissions. It was a clear reminder that students are ready to think critically but require opportunities to do so.  

    AI can only do what it’s programmed to do, which means it draws from existing, stratified information and lags behind new paradigms. That makes it both backward-looking and vulnerable to reproducing bias.  

    Teaching with humanity, by contrast, requires judgment, care and cultural knowledge. These are qualities no algorithm can automate. When we surrender lesson planning to AI, we don’t just lose stories; we also lose the opportunity to engage with them. We lose the critical habits of inquiry and connection that teaching is meant to foster. 

    Tanishia Lavette Williams is the inaugural education stratification postdoctoral fellow at the Institute on Race, Power and Political Economy, a Kay fellow at Brandeis University and a visiting scholar at Harvard University. 

    Contact the opinion editor at [email protected].  

    This story about male AI and teaching was produced by The Hechinger Report, a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for Hechinger’s weekly newsletter.  

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  • Who gets to decide what counts as knowledge? Big tech, AI, and the future of epistemic agency in higher education

    Who gets to decide what counts as knowledge? Big tech, AI, and the future of epistemic agency in higher education

    by Mehreen Ashraf, Eimear Nolan, Manual F Ramirez, Gazi Islam and Dirk Lindebaum

    Walk into almost any university today, and you can be sure to encounter the topic of AI and how it affects higher education (HE). AI applications, especially large language models (LLM), have become part of everyday academic life, being used for drafting outlines, summarising readings, and even helping students to ‘think’. For some, the emergence of LLMs is a revolution that makes learning more efficient and accessible. For others, it signals something far more unsettling: a shift in how and by whom knowledge is controlled. This latter point is the focus of our new article published in Organization Studies.

    At the heart of our article is a shift in what is referred to epistemic (or knowledge) governance: the way in which knowledge is created, organised, and legitimised in HE. In plain terms, epistemic governance is about who gets to decide what counts as credible, whose voices are heard, and how the rules of knowing are set. Universities have historically been central to epistemic governance through peer review, academic freedom, teaching, and the public mission of scholarship. But as AI tools become deeply embedded in teaching and research, those rules are being rewritten not by educators or policymakers, but by the companies that own the technology.

    From epistemic agents to epistemic consumers

    Universities, academics, and students have traditionally been epistemic agents: active producers and interpreters of knowledge. They ask questions, test ideas, and challenge assumptions. But when we rely on AI systems to generate or validate content, we risk shifting from being agents of knowledge to consumers of knowledge. Technology takes on the heavy cognitive work: it finds sources, summarises arguments, and even produces prose that sounds academic. However, this efficiency comes at the cost of profound changes in the nature of intellectual work.

    Students who rely on AI to tidy up their essays, or generate references, will learn less about the process of critically evaluating sources, connecting ideas and constructing arguments, which are essential for reasoning through complex problems. Academics who let AI draft research sections, or feed decision letters and reviewer reports into AI with the request that AI produces a ‘revision strategy’, might save time but lose the slow, reflective process that leads to original thought, while undercutting their own agency in the process. And institutions that embed AI into learning systems hand part of their epistemic governance – their authority to define what knowledge is and how it is judged – to private corporations.

    This is not about individual laziness; it is structural. As Shoshana Zuboff argued in The age of surveillance capitalism, digital infrastructures do not just collect information, they reorganise how we value and act upon it. When universities become dependent on tools owned by big tech, they enter an ecosystem where the incentives are commercial, not educational.

    Big tech and the politics of knowing

    The idea that universities might lose control of knowledge sounds abstract, but it is already visible. Jisc’s 2024 framework on AI in tertiary education warns that institutions must not ‘outsource their intellectual labour to unaccountable systems,’ yet that outsourcing is happening quietly. Many UK universities, including the University of Oxford, have signed up to corporate AI platforms to be used by staff and students alike. This, in turn, facilitates the collection of data on learning behaviours that can be fed back into proprietary models.

    This data loop gives big tech enormous influence over what is known and how it is known. A company’s algorithm can shape how research is accessed, which papers surface first, or which ‘learning outcomes’ appear most efficient to achieve. That’s epistemic governance in action: the invisible scaffolding that structures knowledge behind the scenes. At the same time, it is easy to see why AI technologies appeal to universities under pressure. AI tools promise speed, standardisation, lower costs, and measurable performance, all seductive in a sector struggling with staff shortages and audit culture. But those same features risk hollowing out the human side of scholarship: interpretation, dissent, and moral reasoning. The risk is not that AI will replace academics but that it will change them, turning universities from communities of inquiry into systems of verification.

    The Humboldtian ideal and why it is still relevant

    The modern research university was shaped by the 19th-century thinker Wilhelm von Humboldt, who imagined higher education as a public good, a space where teaching and research were united in the pursuit of understanding. The goal was not efficiency: it was freedom. Freedom to think, to question, to fail, and to imagine differently.

    That ideal has never been perfectly achieved, but it remains a vital counterweight to market-driven logics that render AI a natural way forward in HE. When HE serves as a place of critical inquiry, it nourishes democracy itself. When it becomes a service industry optimised by algorithms, it risks producing what Žižek once called ‘humans who talk like chatbots’: fluent, but shallow.

    The drift toward organised immaturity

    Scholars like Andreas Scherer and colleagues describe this shift as organised immaturity: a condition where sociotechnical systems prompt us to stop thinking for ourselves. While AI tools appear to liberate us from labour, what is happening is that they are actually narrowing the space for judgment and doubt.

    In HE, that immaturity shows up when students skip the reading because ‘ChatGPT can summarise it’, or when lecturers rely on AI slides rather than designing lessons for their own cohort. Each act seems harmless; but collectively, they erode our epistemic agency. The more we delegate cognition to systems optimised for efficiency, the less we cultivate the messy, reflective habits that sustain democratic thinking. Immanuel Kant once defined immaturity as ‘the inability to use one’s understanding without guidance from another.’ In the age of AI, that ‘other’ may well be an algorithm trained on millions of data points, but answerable to no one.

    Reclaiming epistemic agency

    So how can higher education reclaim its epistemic agency? The answer lies not only in rejecting AI but also in rethinking our possible relationships with it. Universities need to treat generative tools as objects of inquiry, not an invisible infrastructure. That means embedding critical digital literacy across curricula: not simply training students to use AI responsibly, but teaching them to question how it works, whose knowledge it privileges, and whose it leaves out.

    In classrooms, educators could experiment with comparative exercises: have students write an essay on their own, then analyse an AI version of the same task. What’s missing? What assumptions are built in? How were students changed when the AI wrote the essay for them and when they wrote them themselves? As the Russell Group’s 2024 AI principles note, ‘critical engagement must remain at the heart of learning.’

    In research, academics too must realise that their unique perspectives, disciplinary judgement, and interpretive voices matter, perhaps now more than ever, in a system where AI’s homogenisation of knowledge looms. We need to understand that the more we subscribe to values of optimisation and efficiency as preferred ways of doing academic work, the more natural the penetration of AI into HE will unfold.

    Institutionally, universities might consider building open, transparent AI systems through consortia, rather than depending entirely on proprietary tools. This isn’t just about ethics; it’s about governance and ensuring that epistemic authority remains a public, democratic responsibility.

    Why this matters to you

    Epistemic governance and epistemic agency may sound like abstract academic terms, but they refer to something fundamental: the ability of societies and citizens (not just ‘workers’) to think for themselves when/if universities lose control over how knowledge is created, validated and shared. When that happens, we risk not just changing education but weakening democracy. As journalist George Monbiot recently wrote, ‘you cannot speak truth to power if power controls your words.’ The same is true for HE. We cannot speak truth to power if power now writes our essays, marks our assignments, and curates our reading lists.

    Mehreen Ashraf is an Assistant Professor at Cardiff Business School, Cardiff University, United Kingdom.

    Eimear Nolan is an Associate Professor in International Business at Trinity Business School, Trinity College Dublin, Ireland.

    Manuel F Ramirez is Lecturer in Organisation Studies at the University of Liverpool Management School, UK.

    Gazi Islam is Professor of People, Organizations and Society at Grenoble Ecole de Management, France.

    Dirk Lindebaum is Professor of Management and Organisation at the School of Management, University of Bath.

    Author: SRHE News Blog

    An international learned society, concerned with supporting research and researchers into Higher Education

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  • Universities Are Curators of Knowledge, Not Chaos (opinion)

    Universities Are Curators of Knowledge, Not Chaos (opinion)

    In a year already defined by polarization and violence, the assassination of Charlie Kirk at Utah Valley University plunged higher education into crisis. The killing of one of the nation’s most prominent conservative activists on a college campus has been weaponized by political factions, prompting administrative crackdowns and faculty firings. What were once familiar battles in the campus culture wars have escalated into something more dangerous: a struggle over the very conditions of inquiry, where violence, scandal and political pressure converge to erode academic freedom. And now, a proposed “compact” with higher education institutions would seek to condition federal funding on requirements that colleges ensure a “broad spectrum of viewpoints” in each academic department and that they abolish “institutional units that purposefully punish, belittle, and even spark violence against conservative ideas.”

    At the center of this struggle lies a persistent illusion: that the university should provide a platform for “every perspective.” Critics claim campuses suppress conservative voices or silence dissenting students, arguing institutions should resemble open marketplaces where all viewpoints compete for attention. Enticing as this rhetoric may be, the expectation is both unworkable and misguided. No university can present every possible outlook in equal measure, nor should it. The mission of higher education is more demanding: to cultivate, critique and transmit knowledge while attending to perspectives that have shaped history and public life. The contrast between an endless marketplace of opinion and the rigorous pursuit of knowledge is crucial to understanding what universities are for.

    Karl Mannheim once distinguished between ideology and knowledge, cautioning against their uncritical conflation. That warning remains essential. Universities are not platforms for unchecked ideology but institutions dedicated to showing how knowledge emerges through observation, interpretation, critique and debate. Perspectives matter, but exposure alone is insufficient; they must be contextualized and weighed against evidence. Free speech and academic freedom overlap but are not the same. Free speech protects individuals from state repression in public life. Academic freedom protects scholars in their pursuit of inquiry and ensures students gain the tools to test claims critically. The distinction is central: The university has an obligation not to amplify all voices equally, but to cultivate discernment.

    This does not mean shielding students from offensive or discredited ideas. On the contrary, a serious education requires grappling with perspectives that once commanded influence, however abhorrent they may now appear. Students of American history must study the intellectual justifications once advanced for slavery—not because they deserve validation, but because they shaped institutions and legacies that continue to structure society. Students of religious history should encounter theological controversies that once divided communities, whether or not they resonate today, because they explain enduring traditions and conflicts. To include such perspectives is not to offer them equal standing with contemporary knowledge, but to illuminate their historical weight and consequences.

    Confusing exposure with endorsement—or opinion with knowledge—risks leaving students adrift in noise. Universities are not megaphones for any thesis but arenas where students learn how to evaluate sources, test claims and trace the consequences of ideas over time. Academic freedom does not mean a free-for-all. Instead, it allows scholars to curate, critique and contextualize knowledge—including ideas that are controversial, even offensive or (as in the study of slavery or fascism) historically consequential. Education that multiplies opinions without cultivating methods of judgment undermines critical capacity; education that fosters discernment equips students to enter public debates wisely and responsibly.

    Recent events in higher education reveal how fragile these principles have become. Violence itself intimidates expression, but administrative and political overreaction magnifies the threat. Faculty have been disciplined for social media posts. In Texas, a lecturer was dismissed for teaching about gender identity. In California, University of California, Berkeley administrators released to federal authorities the identities of more than a hundred students and faculty whose names appeared (as accused, accuser or affected party) in complaints about antisemitism. Faculty watch colleagues punished unjustly, while students—especially international and marginalized ones—face surveillance and potential charges. Across the country, dissent is mistaken for hate, controversial speech treated as threat and scandal avoidance prioritized over defending expressive rights.

    Academic freedom has long enjoyed special constitutional protection, granting professors wide latitude in teaching and research. But this protection depends on public trust: the sense that higher education fosters critical inquiry rather than partisan indoctrination. When professors behave as ideologues or exercise poor judgment in public, that trust erodes. Yet the greater danger comes not from individual missteps but from capitulating to the demand that every perspective deserves equal standing—or from letting violence and political pressure set the boundaries of what may be said. Higher education should not resemble a bazaar of endless opinion but a community dedicated to the disciplined creation, transmission and critique of knowledge. By training students not to hear every voice equally but to weigh evidence and evaluate claims, universities preserve both their scholarly mission and their democratic role. Institutions that cave to intimidation, or that mistake neutrality for abdication, abandon their responsibility to defend inquiry.

    Equally important, universities serve as legitimating institutions. To place a perspective within their walls signals that it merits serious study, that it has crossed the threshold from private belief to public knowledge. This conferral of legitimacy makes curatorial responsibility critical. Treating perspectives as interchangeable voices distorts the university’s purpose, but so does admitting or excluding them solely under political pressure. Both compromises undermine credibility. External actors understand this and exploit universities’ legitimating authority, pressing institutions to provide platforms that elevate discredited or dangerous views into claims of scholarly validation. The responsibility of the university is not to magnify every claim in equal volume but to steward the line between ideas worth engaging and those demanding correction or refusal. Only in this way can institutions preserve their academic mission and their democratic contribution.

    The way forward is neither unbounded opinion nor fearful silence. It is the principled defense of creating, critiquing and reimagining knowledge through inquiry guided by evidence and protected from violence and censorship. To retreat from this responsibility is to weaken not only higher education but democracy itself.

    Gerardo Martí is the William R. Kenan Jr. Professor of Sociology at Davidson College.

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  • 100 Ways the Trump Administration Has Undermined the Environment, Human Rights, World and Domestic Peace, Labor, and Knowledge

    100 Ways the Trump Administration Has Undermined the Environment, Human Rights, World and Domestic Peace, Labor, and Knowledge

    The Trump administration, since returning to power in 2025, has escalated attacks on the foundations of democracy, the environment, world peace, human rights, and intellectual inquiry. While the administration has marketed itself as “America First,” its policies have more often meant profits for the ultra-wealthy, repression for the working majority, and escalating dangers for the planet.

    Below is a running list of 100 of the most dangerous actions and policies—a record of how quickly a government can dismantle hard-won protections for people, peace, and the planet.

    I. Attacks on the Environment

    1. Withdrawing from the Paris Climate Agreement—again.

    2. Dismantling the EPA’s authority to regulate greenhouse gases.

    3. Opening federal lands and national parks to oil, gas, and mining leases.

    4. Gutting protections for endangered species.

    5. Allowing coal companies to dump mining waste in rivers and streams.

    6. Rolling back vehicle fuel efficiency standards.

    7. Subsidizing fossil fuel companies while defunding renewable energy programs.

    8. Suppressing climate science at federal agencies.

    9. Greenlighting pipelines that threaten Indigenous lands and water supplies.

    10. Promoting offshore drilling in fragile ecosystems.

    11. Weakening Clean Water Act enforcement.

    12. Dismantling environmental justice programs that protect poor communities.

    13. Politicizing NOAA and censoring weather/climate warnings.

    14. Undermining international climate cooperation at the UN.

    15. Allowing pesticides banned in Europe to return to U.S. farms.

    II. Undermining World Peace and Global Stability

    1. Threatening military action against Iran, Venezuela, and North Korea.

    2. Expanding the nuclear arsenal instead of pursuing arms control.

    3. Cutting funding for diplomacy and the State Department.

    4. Withdrawing from the World Health Organization (WHO).

    5. Weakening NATO alliances with inflammatory rhetoric.

    6. Escalating drone strikes and loosening rules of engagement.

    7. Providing cover for authoritarian leaders worldwide.

    8. Walking away from peace negotiations in the Middle East.

    9. Blocking humanitarian aid to Gaza, Yemen, and other war-torn areas.

    10. Expanding weapons sales to Saudi Arabia despite human rights abuses.

    11. Using tariffs and sanctions as blunt instruments against allies.

    12. Politicizing intelligence briefings to justify military adventurism.

    13. Abandoning refugee protections and asylum agreements.

    14. Treating climate refugees as security threats.

    15. Reducing U.S. participation in the United Nations.

    III. Attacks on Human Rights and the Rule of Law

    1. Expanding family separation policies at the border.

    2. Targeting asylum seekers for indefinite detention.

    3. Militarizing immigration enforcement with National Guard troops.

    4. Attacking reproductive rights and defunding women’s health programs.

    5. Rolling back LGBTQ+ protections in schools and workplaces.

    6. Reinstating bans on transgender service members in the military.

    7. Undermining voting rights through purges and voter ID laws.

    8. Packing the courts with extremist judges hostile to civil rights.

    9. Weaponizing the Justice Department against political opponents.

    10. Expanding surveillance powers with little oversight.

    11. Encouraging police crackdowns on protests.

    12. Expanding use of federal troops in U.S. cities.

    13. Weakening consent decrees against abusive police departments.

    14. Refusing to investigate hate crimes tied to far-right violence.

    15. Deporting long-term immigrants with no criminal record.

    IV. Attacks on Domestic Peace and Tranquility

    1. Encouraging militias and extremist groups with dog whistles.

    2. Using inflammatory rhetoric that stokes racial and religious hatred.

    3. Equating journalists with “enemies of the people.”

    4. Cutting funds for community-based violence prevention.

    5. Politicizing natural disaster relief.

    6. Treating peaceful protests as national security threats.

    7. Expanding federal use of facial recognition surveillance.

    8. Undermining local control with federal overreach.

    9. Stigmatizing entire religious and ethnic groups.

    10. Promoting conspiracy theories from the presidential podium.

    11. Encouraging violent crackdowns on labor strikes.

    12. Undermining pandemic preparedness and response.

    13. Allowing corporations to sidestep workplace safety rules.

    14. Shutting down diversity and inclusion training across agencies.

    15. Promoting vigilante violence through online platforms.

    V. Attacks on Labor Rights and the Working Class

    1. Weakening the Department of Labor’s enforcement of wage theft.

    2. Blocking attempts to raise the federal minimum wage.

    3. Undermining collective bargaining rights for federal workers.

    4. Supporting right-to-work laws across states.

    5. Allowing employers to misclassify gig workers as “independent contractors.”

    6. Blocking new OSHA safety standards.

    7. Expanding exemptions for overtime pay.

    8. Weakening rules on child labor in agriculture.

    9. Cutting unemployment benefits during economic downturns.

    10. Favoring union-busting corporations in federal contracts.

    11. Rolling back protections for striking workers.

    12. Encouraging outsourcing of jobs overseas.

    13. Weakening enforcement of anti-discrimination laws in workplaces.

    14. Cutting funding for worker retraining programs.

    15. Promoting unpaid internships as a “pathway” to jobs.

    VI. Attacks on Intellectualism and Knowledge

    1. Defunding the Department of Education in favor of privatization.

    2. Attacking public universities as “woke indoctrination centers.”

    3. Promoting for-profit colleges with predatory practices.

    4. Restricting student loan forgiveness programs.

    5. Undermining Title IX protections for sexual harassment.

    6. Defunding libraries and public broadcasting.

    7. Politicizing scientific research grants.

    8. Firing federal scientists who contradict administration narratives.

    9. Suppressing research on gun violence.

    10. Censoring federal climate and environmental data.

    11. Promoting creationism and Christian nationalism in schools.

    12. Expanding surveillance of student activists.

    13. Encouraging book bans in schools and libraries.

    14. Undermining accreditation standards for higher education.

    15. Attacking historians who challenge nationalist myths.

    16. Cutting humanities funding in favor of military research.

    17. Encouraging political litmus tests for professors.

    18. Treating journalists as combatants in a “culture war.”

    19. Promoting AI-driven “robocolleges” with no faculty oversight.

    20. Gutting federal student aid programs.

    21. Allowing corporate donors to dictate university policy.

    22. Discouraging international students from studying in the U.S.

    23. Criminalizing whistleblowers who reveal government misconduct.

    24. Promoting conspiracy theories over peer-reviewed science.

    25. Normalizing ignorance as a political strategy.        

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  • Employers will increasingly focus on graduates’ skills over technical knowledge

    Employers will increasingly focus on graduates’ skills over technical knowledge

    There are few safe bets about the future, so the impact of technology on labour markets, how transitions through education and into work will change, and the need to reskills and upskill, can only be predicted.

    But we do know that technology – AI in particular – is a disruptive force. We know that declining birth rates and higher employer skills needs have the potential to create a difficult labour market that hinders growth. We also know it’s likely that people who don’t adapt to changes in work could see their careers suffer.

    In response to these shifts, graduate and apprentice employers are considering fresh approaches to their talent strategies. Strategies that will focus less on a person’s age, education and technical experience, and more on their skills, capabilities and aptitudes.

    The Institute of Student Employers (ISE) recent report, From early career to emerging talent, shows that 68 per cent of early career employers have already adopted or partially adopted a skills-based strategy to hiring – and another 29 per cent are considering it.

    A constricted labour market

    Quite rightly, we are all concerned about the tough jobs market facing students, the high volumes of applications they make, and the time it takes many to get a graduate job. Because of the UK’s anaemic growth, the current labour market is tight (ISE predicts graduate vacancies will only grow by one per cent this year). But once growth returns to the economy, it’s likely employers will see significant talent shortages.

    We can see latent labour market problems in the current Labour Market Information (LMI) data. The UK’s unemployment rate at 4.4 per cent is historically low. Only 16.4m people in England and Wales are educated to level 4 or over – yet there are 18.6m jobs currently at that level, rising to 22.7m over the next 10 years. Over the next decade the working age population will increase only by 1.14m people (the over-70s, on the other hand, will increase by 2.1m).

    Mention 2022 and while most remember the heatwave, recruiters remember the post-pandemic growth spurt which left many vacancies unfilled. A CIPD labour market survey from summer of that year reported that 47 per cent of employers had hard-to-fill vacancies and the top response to difficulties reported by employers was to upskill existing staff.

    A problematic word

    What is a skill, an attribute or a capability? What can be taught, learned and developed, and what individual traits are innate? Some skills are technical, some more behavioural. And we’ll all have our own views on the abilities of ourselves and others. So, the word skills is problematic, which makes agreement on what approach we take to skills problematic.

    In their recent Wonkhe articles, Chris Millward and Konstantinos Kollydas and James Coe are right to highlight the challenge of differentiating between knowledge, technical behavioural and cognitive skills. To varying degrees, employers need both. I’d add another challenge, particularly in the UK: the link between what you study and what you do is less pronounced. Over 80 per cent of graduate recruiters do not stipulate a degree discipline. This makes connecting skills development to the labour market problematic.

    Another problem with the use of the word skills is the danger that we take a reductive, overly simplistic view of skills. A student who does a group activity successfully may think they’ve nailed teamworking skills. In reality, working with people involves a multitude of skills that many of us spend our working lives trying to master.

    Employers are already increasing their focus on skills

    In their report The skill-based organisation: a new operating model for work and the workforce, Deloitte describe how organisations are developing “a whole new operating model for work and the workforce that places skills, more than jobs, at the centre.”

    As recruitment for specific expertise becomes more challenging, people are matched to roles based on skills and potential, less on experience in a role. Skills-based hiring strategies encompass career changers, older workers, people who have near-to work experience. Technology maps an organisation’s skills base to create an internal marketplace for roles and employees are encouraged to re-skill and upskill in order to move about the organisation as jobs change.

    Graduates will need the skills and associated mindset to navigate this future world of work. World Economic Forum 2025 Future of Jobs analysis shows that 69 per cent of UK organisations placed resilience, flexibility and agility in the top five skills that will increase in importance by 2030.

    Graduate recruitment strategies could evolve to make less use of education exit points to define the talent pool hired from: career-changers, older-workers, and internal switchers are incorporated into development programmes. More learning content becomes focused on developing behavioural and cognitive skills to promote a more agile cohort.

    Students do develop skills

    Within HE, practitioners have already established a considerable body of knowledge, research and practice on employability skills. Where change is occurring, is in the campus-wide approach to skills that many institutions have developed (or are in the process of developing). Approaches that aim to ensure all students have the opportunity to develop a core set of skills that will enable them to transition through education and into work. Bristol and Kingston, among others, have shown how skills can be embedded right across the curriculum.

    I’m a big fan of Bobby Duffy’s work on delayed adulthood which suggests to me that the average student or graduate in their late teens and early twenties is at quite a different stage of development to previous generations. Which means that it’s wrong-headed to think of deficits in students’ work readiness as the fault of students (or their coddling parents).

    Employers and educators together have a role to play in helping students understand their own skills and how to develop them. Skills require scaffolding. Surfacing skills in the curriculum ensures students understand how their academic work develops core skills.

    And the provision and promotion of extra-curricular activities, including work experience, can be built into the student journey. Programmes where students develop their ability to deal with change and challenging situations, to analyse and solve complex problems, to adopt a positive approach to life-long learning.

    The skills agenda opportunity

    At the ISE we leave the language of skills gaps and employers’ apparent low opinion of graduates to the tabloids. Only 17 per cent of employers in our annual survey say they disagree that graduates are not work-ready. We do ask a more subtle question on the attitudes and behaviours that employers expect early career hires to possess when they start work. The top skills employers thought students weren’t as proficient in as they expected were self-awareness, resilience and personal career management.

    I am not, never have been, and never will be, a policy wonk. Maybe someone who is can design the architecture of incentives and systems that better connect education pathways to labour market needs. This architecture will also have to be able to predict labour market needs four to five years in advance, because that’s the lag between a typical students’ course choice and their job application. But if that can’t be done, surely a good investment is ensuring that students have plenty of opportunities to develop their skills and attributes to deal with an ever more changing workplace.

    Fully embracing a skills approach is a great opportunity to demonstrate how HE adds value to the UK economy through the triangulation of student interests, employer needs and a great education experience.

    Read the ISE’s report, From early talent to emerging talent, for a detailed analysis of the forces impacting how employers will hire and develop students in the future.

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  • With the power of knowledge – for the world

    With the power of knowledge – for the world

    I went along to AHUA conference on Tuesday, and saw a fascinating presentation from Esa Hämäläinen, who’s the Dir­ector of Ad­min­is­tra­tion at the University of Helsinki.

    The university has easily one of my favourite origin stories – it was established by a 13-year-old girl who the world came to know as Queen Christina of Sweden.

    It also has a cracking set of values, some of which appear now to be the sort of thing that’s banned by the Office for Students in England.

    In 2015, under Prime Minister Juha Sipilä’s administration, the government announced a €500 million cut to higher education budgets in Finland.

    That followed a previous €200 million reduction and included freezing the university index, which had adjusted funding based on inflation.

    As a result, universities like the University of Helsinki had to lay off hundreds of staff – about 400 in the case of Helsinki.

    There’s a lot of different ways of calculating staff-student ratios that often make comparisons problematic – but one of the things I was pondering on the train was how they are doing what they’re doing on an academic SSR of 22.2:1 – significantly higher than in the past, and significantly higher than the UK.

    For the avoidance of doubt, I’m not searching for a blueprint on how to shed academic staff. But if cuts are going to rain down anyway, understanding how other systems work beyond “Oh look they have ECTS too” I think (hope) can help.

    I say this partly because a lot of people I talk to are experiencing or implementing plain and simple “reduce the number of optional modules” strategies based on the efficiency of more/large/core – which most research suggests students don’t like, and I suspect is a probable cause of during and post-degree regret.

    What’s fascinating is that rather than just accept the inevitability of a thinner student academic experience as a result of those cuts, the university evolved its Bildung philosophy to make a whole range of scaffolding changes to cope on fewer staff. And I’ve spent a long train journey trying to work out how.

    They call a Twix a Raider

    First some Twix/Raider basics. There’s 180 ECTS for a Bachelor’s degree, designed to be taken over 3 years. No difference to the UK there (unless we count Scotland) other than students can take longer to obtain those 180, supported via the maintenance system to do so – although universities across Europe are variously under government pressure/incentives to speed that up a bit.

    It’s also worth noting that for various reasons, the average entry age for bachelor’s degree programmes in Finland is 24, compared to an OECD average of 22. We have (along with Belgium) the youngest freshers and the fastest completion times in the OECD. That we then beat Belgium on completion rates often causes me to reflect on whether that’s a sign of success or a signal of conveyor-belt trapping, a cause of mental health problems and a driver of lower of academic standards – but I digress.

    What we’d typically call “modules” in the UK are referred to as “courses” in Finland. As for what we’d call a “programme” or “subject pathway”, it varies – but at Helsinki, undergraduate students complete two core “modules”, each comprising a collection of courses, one for “Basic” studies (what we’d think of as a UG first year), and one for “Intermediate” studies (what we’d think of as a second and third year).

    These two modules are each awarded a single grade on a 1–5 scale, and it’s these two grades that appear on the student’s degree transcript.

    So, instead of the UK-style baffling algorithm of final grades weighted in different ways across multiple modules, students in Finland receive just two key grades on their transcript – simple, succinct, and arguably more transparent, along with the pathways taken within them. Additionally, students can receive a separate distinction mark for their dissertation. A nice touch.

    The University of Helsinki is Finland’s flagship institution – huge in size, high in status, and widely seen as the country’s de facto elite public university. And yet, intriguingly, there are only 32 undergraduate degree programmes on offer across its 11 faculties. Within each of these programmes, students have considerable freedom to create their own study path, but the structure is strikingly straightforward – 11 faculties, 32 programmes, no sub-departments, and no sprawling web of hundreds of “course” leaders.

    That also means 32 academic communities, with 32 academic societies that students join to get support from eachother and engage in things – a nice size that avoids having to find 1500 course reps or trying to sustain a meaningful single student community from 40,000 students – all supported by 32 sets of student tutors, of course.

    The mother of all science

    Let’s take Philosophy as an example. To complete the degree, students have to earn 90 ECTS credits in Philosophy-specific study, 75 elective credits, and 15 from general studies. That structure encourages both specialisation and breadth.

    Oh, and a quick technical note – the standard assumption in Finland is that 1 ECTS credit represents 27 hours of student effort. In the UK, by contrast, it’s 20. The reasons are dull and bureaucratic (that didn’t stop me working out why) but worth bearing in mind when comparing intensity.

    First it’s worth digging into the 90 credits earned in Philosophy. These are split into two main “modules” – Basic Studies (30 credits) and Intermediate Studies (60 credits). As I said earlier, the former corresponds to first-year study, and the latter covers second and third year.

    The 15 credits of general studies are interesting. 2 credits are awarded for a reflective planning exercise where students work with an academic to design their personalised study plan – a kind of “choose your own adventure” approach that signals a departure from spoon-feeding from day one. That’s assessed on a pass/fail basis.

    There are also three credits for digital skills training, delivered via self-study – two credits within the Basic Studies and one within Intermediate. Again, this is assessed pass/fail and serves both to build capability and to ensure students are confident in using the university’s largely self-service systems.

    Then there are 10 credits dedicated to communication and language skills. These span both written and oral communication, include components in both Finnish and Swedish, and feature academic writing training – often completed in groups. All of this is, again, pass/fail.

    What I find interesting about these is a recognition that designing a bespoke study programme (that can change over time), along with IT and communication skills, are really about becoming a student – here they are recognised as taking actual time.

    In the Basic Studies module, students take six standard “intro to…” courses worth 5 credits each. These are relatively straightforward in design, delivery, and assessment. Each course is normally assessed via a single exam, although in most cases students can opt to complete coursework instead.

    In each degree programme, 60 subject-based credits – what we’d call second and third year content – then form the Intermediate “module”. Of these, five are allocated to the thesis (dissertation), while the remainder is typically made up of 5-credit courses, offering students considerable choice and customisation.

    To move into intermediate, there’s a 0 credit “maturity” assessment so students aren’t moving there until they’re ready. Then of the 60 Intermediate credits, 30 are structured as follows. 5 credits are awarded for a proseminar, which functions like a structured, small-group academic workshop:

    At the beginning of the course, students are given a review of the basics of academic writing and how to critically review and oppose an academic work. How to formulate a research question is discussed and advice is given on how to obtain source material. The student is then expected to formulate a research question in the form of a short abstract which is then reviewed and discussed by the teacher and other students. Then a period of research and essay writing takes place where the opportunity for supervision is given. At the end of the course, the student must present an essay for review by an opponent and oppose another student’s essay.

    5 credits are for a Candidate intuition seminar, and that looks like this:

    At the beginning of the course, students receive a refresher course in the basics of academic writing and how to critically review and oppose an academic paper. At the beginning of the course, there is also a discussion on how to formulate a research question and participants are given advice on how to obtain source material. The student is then expected to formulate a research question in the form of a short abstract which is then reviewed and discussed by the teacher and other students. This is followed by a period of research and essay writing where opportunities for supervision are provided. At the end of the course, the student must present an essay for review by an opponent and act as an opponent in the processing of another student essay.

    Then as well as the dissertation (thesis) itself there’s 5 credits for a compulsory internship (pass/fail) and 5 credits for preparing to apply what you did on your degree to the world, and that looks like this (also pass/fail):

    This gives the student the opportunity to independently explore the individual, growing competence that the degree provides and the importance of competence in a changing society and working life. The aim is for the student to become familiar with and reflect on the ways in which the unique competence provided by studies in philosophy, in collaboration also with studies in other subjects, which the student has chosen, can be relevant to our lives, to working life, society and the world.

    It can be completed in various different ways, in consultation with the responsible teacher – collaboration, independent studies and observation and reflection tasks related to other modules. An e-portfolio or course diary can also be included.

    And then finally there’s a 5 credit compulsory, and in Philosophy that’s a classic module on History of Philosophy.

    For the other 30 credits of Intermediate there’s then a collection of “classic” academic modules again, often in pathway clusters.

    So via the 60 “subject” ECTS points and the 15 “general studies” ECTS points, that’s 105 ECTS accounted for. And here’s the thing. The 75 left are acquired by picking the sort of stuff I’ve talked about above, but they must be from other degree programmes!

    That means that a Philosophy student that wants to do the basics in statistics or whatever can access what might be regarded as another course’s core modules. That obviously means a large amount of interdisciplinary stuff happening, with quite a lot of interesting student mixing happening too. It also means that the “courses” are highly efficient.

    Oh, and also if you do Erasmus, or learn skills at work, or as a volunteer, or whatever…

    You can receive credit for studies you have completed at higher education institutions either in Finland (universities, the National Defence University, and universities of applied sciences) or abroad. The studies must have been successfully completed.

    You can also get credit for skills you have acquired in working life, positions of trust or hobbies, for example. In this case, we are talking about skills acquired in a way other than formal education.

    A time for reflection

    At this point down the rabbit hole I see small, simple-to-design and simple-to-assess academic modules (without having to cram in 100 agendas), plenty of pass/fail credit (less grading means less pressure for everyone), and lots of focus on choice and independent study. And an actual recognition that skills development matters without it always having to be crammed into optional activity students don’t have time for, or academic modules.

    Just a note on grading. One of the things happening here is that grading itself is less complex (5 is Excellent, 4 is Very good, 3 is Good, 2 is Satisfactory, 1 is Passable and 0 is Fail), there’s less of it to do in general, and the ability to re-take assessments in a funding system that allows for setbacks reduces the need for extenuating circumstances and extensions and so on – so the stakes are less high, less often.

    So broadly what I take from it all is:

    1. The hidden curriculum is less hidden
    2. Academic staff have a simpler life
    3. The credit system overall creates rounded graduates
    4. The design reduces unnecessary pressure on students
    5. Some of the credit prepares students for graded credit instead of it all being graded
    6. There are lots of personalisation options
    7. There’s a much more meaningful degree transcript
    8. There’s more assessment choice
    9. There’s less pressure to get students through at top speed
    10. There’s less high-stakes assessment in general
    11. There are “millions” of potential (what we would call) “programmes” without the coordination overhead, walled gardens and spoonfeeding of (what we would call) programmes
    12. There’s less traditional academic “teaching” going on here, but what there is is more efficient and more straightfoward

    Crucially, lots of the modules I’ve seen are from research-active academics – whose research area probably wouldn’t sustain a whole “programme” in our systems – but whose little chunk of credit sits neatly and sustainably in this system.

    So what could my little GWR trip down that a Finnish rabbit hole all mean?

    First of all, if I was the higher education minister (haha) I’d require there to be no more than the number and titles of QAA’s subjects in its benchmark statements as the degrees on offer as a condition of access to the loan book.

    On the emerging unit of resource, it’s going to end up impossible to innovate if not – getting new programmes approved will always be based on what marketeers think will “sell” – and doing simplifying in this way would force more “choose your own adventure” without the overhead of running and marketing a “programme”. I also take the view that saying to a student on an Open Day that there will be quite a bit of elective choice – when everyone internally knows that a lot of the choice will have gone by the time the VR round is done and that student is in their third year – is pretty immoral (and almost certainly unlawful).

    In addition, I also suspect the “choose your own adventure within some parameters” approach would reduce some of the regret we see in the UK. Even if students enrol with a strong disciplinary orientation (partly because of the ridiculous specialisation we force onto students at Level 1-3), a topline reading of the Bristol “regret” research is that either during or after the degree, students clock how unhelpful the UK’s obsession with narrowing is. (There’s no equivalent “regret” question in the Finnish NSS, but lots of interesting stuff that suggests less regret nonetheless.)

    You’ll have seen that much of the credit is about what we might generically call study skills – via our Belong project, we have unpublished national polling evidence (that will be on the site soon) that suggests that in general, students often regard what is on offer in the UK as too generic, and when it’s optional and non-credit bearing, other demands on their time tend to win out. This appears to be a system that has solved some of that.

    The rattle through above, by the way, was me diving into a Philosophy degree – but even in subjects where we might usually expect to see a more programmatic approach via more compulsory modules, structures and weighting aren’t hugely dissimilar – here’s the generic Bachelor’s in Science, for example.

    Plenty of the “choice” on offer is about both a dissertation and extra credit in the run-up to said dissertation – where there isn’t teaching on the thing the student wants to study per se but students can access academics who might be research-active in that. And some of the other choice options are doubtless constrained by timetable – but that’s eased somewhat by some of the credit being acquired “centrally”, some in self-directed mode, and a maintenance system that allows the average duration to be over 3.5 years. Clash? Take it next semester.

    Ultimately what I’m struck by, though, is the simplicity of the whole thing – which is not obvious on first look. I’m not saying that it’s simple to design the study plan or to even visualise the whole degree (either by diving into the website or reading this account), but I am saying that a lot of the tasks carried out by students or academics are simpler – where the focus is on academic learning and development (with quite sophisticated pedagogical research, innovation and support) rather than endless assessment, complex degree algorithms and multiple agendas.

    To the extent to which you can see a graduate attributes framework here, it’s delivered via multiple types of credit acquisition, rather than every attribute being loaded into every fat module.

    What is, though, absolutely undeniable is that a Chemistry graduate in this system has done less… Chemistry. Maybe the Royal Society of Chemistry (and all of the other PSRBs) would have things to say about that. But they’re nonetheless demonstrably rounded graduates (without a lot of the rounding depending on inaccessible extracurriculars) – and in a mass system, how many Bachelors graduates all need as much Chemistry individually anyway?

    Put another way, if a dwindling number of students want to study just Chemistry, and this system sustains a large number of Chemistry modules that are available both to those who do and those and don’t, isn’t that better for society overall?

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  • Policy change can help manage the demand for graduate knowledge and skills

    Policy change can help manage the demand for graduate knowledge and skills

    “Our universities have a paramount place in an economy driven by knowledge and ideas.”

    These are the opening words of the 2016 white paper Success as a Knowledge Economy, which created the funding and regulatory architecture governing English higher education today. The arrangements are founded on a broad faith in the economic benefits of generating and communicating knowledge.

    This vision assumes that an increasing supply of university graduates and research, coupled with open markets that reward enterprise, leads to endogenous economic growth. That can happen anywhere because ideas are boundless and non-rivalrous, but particularly in England because our universities are among the best in the knowledge business.

    English higher education has grown by integrating the development of specific skills for the workplace alongside universally applicable knowledge. This is clear from the progress of most English universities from institutes established for professional and technical training towards university status, the absorption of training for an increasing range of professions within higher education, and the way in which universities can now articulate the workplace capabilities of all graduates, regardless of their discipline.

    Notwithstanding this, the reforms proposed in 2016 emphasised knowledge more than skills. By that time, most of the cost of teaching in English universities had been transferred to student tuition fees backed by income-contingent loans. So, the reforms mostly focused on providing confidence for the investments made by students and the risks carried by the exchequer. This would be delivered through regulation focused on issues important to students and the government, whilst positioning students as the pivotal influence on provision through competition for their choices.

    Universities would compete to increase and improve the supply of graduates. This would then enhance the capacity of businesses and public services to capitalise on innovation and new technologies, which would yield improved productivity and jobs requiring graduates. That is a crude characterisation, but it provides a starting point for understanding the new imperatives for higher education policy, which are influenced by challenges to this vision of nearly a decade ago.

    From market theory to experience in practice

    Despite an expansion of university graduates, the UK has had slow productivity growth since the recession of 2008–09. Rather than the economy growing alongside and absorbing a more highly educated workforce, there are declining returns for some courses compared with other options and concerns that AI technologies will replace roles previously reliant on graduates. Employers report sustained gaps and mismatches between the attributes they need and those embodied in the domestic workforce. Alongside this, ministers appear to be more concerned about people that do not go to university, who are shaping politics in the USA and Europe as well as the UK.

    These are common challenges for countries experiencing increasing higher education participation. The shift from elite to mass higher education is often associated with a “breakdown of consensus” and “permanent state of tension” because established assumptions are challenged by the scale and range of people encountering universities. This is particularly the case when governments place reliance on market forces, which leads to misalignment between the private choices made by individuals and the public expectations for which ministers are held to account. Universities are expected to embody historically elite modes of higher education reflected in media narratives and rankings, whilst also catering for the more diverse circumstances and practical skills needed by a broader population.

    In England, the government has told universities that it wants them to improve access, quality and efficiency, whilst also becoming more closely aligned with the needs of the economy and civil society in their local areas. These priorities may be associated with tensions that have arisen due to the drivers of university behaviour in a mass market.

    In a system driven by demand from young people, there has been improved but unequal access reflecting attainment gaps in schools. This might not be such a problem if increasing participation had been accompanied by a growing economy that improves opportunities for everyone. But governments have relied on market signals, rather than sustained industrial strategies, to align an increasing supply of graduates with the capabilities necessary to capitalise on them in the workplace. This has yielded anaemic growth since the 2007 banking crash, together with suggestions that higher education expansion diminishes the prospects of people and places without universities.

    In a competitive environment, universities may be perceived to focus on recruiting students, rather than providing them with adequate support, and to invest in non-academic services, rather than the quality of teaching. These conditions may also encourage universities to seek global measures of esteem recognised by league tables, rather than serving local people and communities through the civic mission for which most were established.

    Market forces were expected to increase the diversity of provision as universities compete to serve the needs of an expanding student population. But higher education does not work like other markets, even when the price is not controlled as for undergraduates in England. Competition yields convergence around established courses and modes of learning that are understood by potential students, rather than those that may be more efficient or strategically important for the nation as a whole.

    Navigating the new policy environment

    After more than a decade of reforms encouraging competition and choice, there appears to be less faith in well-regulated market forces positioning knowledgeable graduates to drive growth. Universities are now expected to become embedded within local and national growth plans and industrial strategy sectors, which prioritise skills that can be deployed in specific settings ahead of broadly applicable knowledge. This asks universities to consider the particular needs of industry, public services and communities in their local areas, rather than demand from students alone.

    Despite these different imperatives, English higher education will continue to be financed mostly by students’ tuition fees and governed by regulatory powers designed to provide confidence for their choices. We suggest four ingredients for navigating this, which are concerned with strategy, architecture, regulation and funding.

    The government has promised a single strategy for post-16 education and a new body, Skills England, to oversee it. A more unified approach across the different parts of post-compulsory education should encourage pathways between different types of learning, and a more coherent offer for both learners and employers. But it also needs to align factors that influence the demand for graduates, such as research and innovation, with decisions that influence their supply. That requires a new mindset for education policy, which has tended to prioritise national rules ahead of local responsiveness, or indeed coherence with other sectors and parts of government.

    Delivery of a unified strategy is hampered by the fragmented and complex architecture governing post-16 education. Skills England will provide underpinning evidence, both influencing and drawing on Local Skills Improvement Plans (LSIPs), but it remains uncertain how this will be translated into measures that influence provision, particularly in universities. A unified strategy demands structures for convening universities, colleges, employers and local authorities to deliver it in local areas across the country.

    That could be addressed by extending the remit of LSIPs beyond a shopping list of skills requirements and enhancing the role of universities within them. Universities have the expertise to diagnose needs and broker responses, aligning innovation that shapes products and services with the skills needed to work with them. They will, though, only engage this full capability if local structures are accompanied by national regulatory and funding incentives, so there is a unified local body responsible for skills and innovation within a national framework.

    Regulation remains essential for providing confidence to students and taxpayers, but there could be a re-balancing of regulatory duties, so they have regard to place and promote coherence, rather than competition for individual students alone. This could influence regulatory decisions affecting neighbouring universities and colleges, as well as the ways in which university performance is measured in relation to issues such as quality and access. A clear typology of civic impact, together with indicators for measuring it, could shift the incentives for universities, particularly if there is a joined-up approach across the funding and regulation of teaching, research and knowledge exchange.

    Regulation creates the conditions for activity, but funding shapes it. Higher education tends to be a lower priority than schools within the Department for Education, and research will now be balanced alongside digital technologies within the Department for Science, Innovation and Technology. A new Lifelong Learning Entitlement and reformed Growth and Skills Levy may provide new opportunities for some universities, but any headroom for higher education spending is likely to be tied to specific goals. This will include place and industry-oriented research and innovation programmes and single-pot allocations for some MSAs, alongside the substantial public and private income universities will continue to generate in sectors such as health and defence. In this context, aligning universities with the post-16 education strategy relies on pooling different sources of finance around common goals.

    Closer alignment of this kind should not undermine the importance of knowledge or indeed create divisions with skills that are inconsistent with the character and development of English higher education to date. The shift in emphasis from knowledge towards skills reframes how the contributions of universities are articulated and valued in policy and public debate, but it need not fundamentally change their responsibility for knowledge creation and intellectual development.

    This appears to have been recognised by ministers, given the statements they have made about the positioning of foundational knowledge within strategies for schools, research and the economy. We have, though, entered a new era, which requires greater consideration of the demand for and take-up of graduates and ideas locally and nationally, and a different approach from universities in response to this.

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  • Can knowledge exchange fix a broken economy?

    Can knowledge exchange fix a broken economy?

    There’s always a challenge in trying to describe knowledge exchange, how it’s funded, why it’s worth worrying about, and what it actually does to the economy.

    Mechanisms

    The default is to talk about its underpinning mechanisms. The way that money goes to universities, their partners and then circulates into the real economy, and then hopefully something good happens. The problem with this approach is that outside of experts and hardy enthusiasts like me this approach is, well, rather dull.

    And knowledge exchange is a less than glamorous name for some of the most important work universities do. ESRC, one of UKRI’s funding councils, has a rather elegant way of describing it:

    The Economic and Social Research Council (ESRC) is committed to encouraging collaboration between researchers and businesses, policymakers, the public and third sector organisations (for example charities and voluntary groups). This can create mutual benefits and contribute to positive economic and social impacts outside academia, for example through changes to policy and practice or new products and services created by commercialising research. Two-way interactions of this type are often collectively referred to as knowledge exchange. This is an umbrella term that covers a wide range of activities researchers might engage in, including policy engagement, public engagement, commercialisation and business engagement.

    A less elegant way is to say that universities working together with other organisations can make the economy and society stronger. It is not a dry technocratic thing but the very way in which the wonderful things that are produced in universities become useful. Great ideas without an audience are interesting but fruitless. An expectant audience with no great ideas are bound for disappointment.

    This means that there must be both the conditions for useful ideas to be produced and the conditions for organisations to make use of them. Research England, another funding body of UKRI, funds knowledge exchange through the Higher Education Innovation Fund (HEIF) and the Connecting Capability Fund (CCF). While HEIF is a more general knowledge exchange fund the CCF is focussed on the commercialisation of research with business. These funds are small compared to the overall research funding pots. HEIF is a formula based fund of £260m compared to an overall UKRI budget of over £8bn.

    The key question isn’t whether knowledge exchange is a good thing. It self evidently is. But whether the intervention by funders is producing bigger impacts than would naturally happen through universities working with businesses, policy makers, and other groups. After all, universities would still benefit from equity in spin-outs and bask in the warm glow of civic participation even if they weren’t supported to do so.

    Reports

    UKRI has brought out three new reports that look at knowledge exchange funding.

    The first report is an evaluation of HEIF carried out by Tomas Coates Ulrichsen. The part which UKRI will be most proud of, and should definitely cause them to consider whether their funding is enough, is that every £1 invested in HEIF produces £14.8 return on investment if you crowd in actual and estimated external impacts. Perhaps even more impressively the report also suggests that “38% of knowledge exchange outputs and incomes would not have happened in the absence of HEIF.” This isn’t activity that is being paid for twice but activity that is actually being created.

    However, while this makes the case persuasively for the value of HEIF it’s the summary which gives us a bigger clue into what is going on in the economy. The report notes

    The past two decades has seen KE income secured by English HEPs grow significantly in real terms, with KE income 81% higher in 2022/23 than in 2003/04 for HEPs in receipt of HEIF during the period 2017/18 – 2022/23 (the vast majority of HEPs in England). However, what is clear is that this twenty-year period is characterised by two very different decades. While KE income grew strongly – and faster than the economy as a whole – during the first decade, the past ten years has seen this growth largely stagnate. The limited growth in KE income may well reflect the multiple crises and shocks the UK has faced since then, not least with the Covid-19 pandemic, cost of living crisis, and departure from the European Union and the effects of this on R&D with research grants and contracts income to HEPs from European sources declining almost 30% in real terms since the EU referendum in 2016. KE income now appears to track trends in the economy more widely (as measured by the UK’s GDP).

    To read the inverse of this is that the wider economy is a constraining factor on the ability of universities to deploy their research for social and economic benefit.

    There is perhaps a tacit assumption that if universities produce great and useful research it will lead to great and useful things in the economy and society. This is only true as long as the economy has the absorptive capacity to keep the cycle of knowledge exchange investment which leads to knowledge exchange outputs which supports knowledge exchange income churning.

    Help/HEIF

    The evaluation of HEIF carried out by PA Consulting is particularly illuminating within this frame. The key findings are that in a changing policy environment HEIF has anchored the sector to make some significant social and economic impacts. It is the flexibility of the fund which has allowed specialisms to develop, the autonomy of the fund has found favourability in the sector, its stability has allowed for long-term partnerships, and a more permissive approach to accountability has allowed providers to demonstrate their value without drowning under administration.

    The report is full of examples of how HEIF funding has catalysed wider social and economic activity but the examples have two things in common. The first is that allowing flexibility in the fund means it can be deployed in multiple partners in multiple ways. This means that even where there are wider economic challenges the funding can be tailored to suit the challenges of local economies. The second is that the long-term nature of the fund allows for greater stability within partnerships to withstand adverse economic headwinds.

    Together, the two reports point toward HEIF as being successful as it demonstrably supports economic growth but does so through flexibility and provider autonomy linked, to a lesser or greater extent, to national priorities. It’s only a small fund but it is impactful.

    Same old SMEs

    The final report on CCF by Wellspring again demonstrates a positive return on investment. The programme has led to 200 new spin-outs and supported over 1,500 SMEs. The programme has led to the launch of at least 338 products and services and it is expected more will be launched over time, particularly in high-tech spin-outs.

    The obvious albeit incorrect conclusion to draw would be that if each of these interventions induce such strong economic benefits then making the intervention larger would make the economy stronger. In fact, if the economic returns are so strong then the projects could presumably be 10, 100, or 1,000 times bigger, and continue to provide economic return.

    Instead, what these reports highlight is that knowledge exchange funding is a product of the wider economy. There is a natural limit to how much activity can take place as there comes a point where the economy is not large enough or dynamic enough to absorb the benefits of universities’ work. In fact, these reports indirectly demonstrate how economies get stuck into a death spiral. Productivity stalls which prevents the absorption of innovative products and services. Without innovative products and services the economy cannot become more productive. And so on.

    The benefits these schemes are realising would suggest they are not close to meeting the capacity of the economy and could therefore be much larger. It is also a matter of purpose. The funds are designed on a premise that there is capacity to make use of university work. It is a much harder question to imagine how funding should be designed where it is necessary to restart a broken economy.

    The impact of these funds is striking, the reports written about them are convincing, however they open a door to a wider question of whether knowledge exchange funding is big enough, well directed enough, or tooled properly, to fix the UK’s entrenched economic issues including its collapsed productivity.

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