Category: AI

  • What grade inflation panics miss about the real value of higher education

    What grade inflation panics miss about the real value of higher education

    Cloaks swish. Cameras flash. It’s graduation day, the culmination of years of effort. It celebrates learning journeys whose outcomes have nurtured the realisation of talents as varied as our students themselves.

    It is a triumphant moment. It is also the moment in which the sector reveals the outcome of its own Magic Sorting Hat, whose sorcery is to collapse all this richness into a singular measure. As students move across the stage to grasp the sweaty palm of the VC or a visiting dignitary, they are anointed.

    You are a First. You are a Third. You are a 2:1.

    There is something absurd about this, that such diverse, hard-won successes can be reduced to so little. That absurdity invites a bit of playfulness. So, indulge me in a couple of thought experiments. They are fun, but I hope they reveal something more serious about the way we think about standards, and how often that crowds out a conversation about value.

    Thought experiment one: What if classifications are more noise than signal?

    Let us begin with something obvious. Like any set of grades, classifications exist to signal a hierarchy. They are supposed to say something trustworthy about the distribution of talent – where a First signals the pinnacle of academic mastery. What “mastery” is – and how relevant that signal is beyond the academy – is a point I think we should dwell on as ambiguous.

    “Mastery” isn’t the upper tier of talent. Our quality frameworks do not, by principle, norm reference, and for good reason that are well-worn in assessment debates: shaving off a top slice of talent would exclude cohorts of students who might, in a less competitive year, have made the cut. So, then, we criterion reference; we classify against the extent to which programme outcomes have been met to a high standard. On that logic, we ought to be delighted when more and more students meet those standards. Yet when they do, we shift uneasily and brace for assaultive chorus of “dumbing down.”

    The truth of the First feels even less solid when set against the range of disciplinary and transdisciplinary capabilities we try to pack into that single measure, and the range of contexts that consume it at face value. They use it to rank and sort for their own purposes; to make initial cuts of cohorts of prospective employees to make shortlists manageable, for instance, with troubling assumptive generalisation. That classification is paradoxically a very thin measure, and one that is overloaded with meaning.

    It is worth asking how we ended up trusting so much to a device designed for a quite different era. The honours classification system has nineteenth-century roots, but the four-band structure that still dominates UK higher education really bedded in over the last century. The version we live with now is an artefact of an industrial-era university system; built in a world that imagined talent as a fixed trait and universities as institutions that sorted a small elite into neat categories for professional roles. It made sense for a smaller, more homogeneous system, but sits awkwardly against the complex and interdisciplinary world students now graduate into.

    Today it remains a system that works a bit like a child’s play dough machine. Feed in anything you like, bright colours, different shapes and unique textures, and the mechanism will always force them into the same homogenous brown sausage. In the same way, the classification system takes something rich and individual and compresses it into something narrow and uniform. That compression has consequences.

    The first consequence is that the system compresses in all sorts of social advantages that have little to do with academic mastery. Access to cultural capital, confidence shaped by schooling, freedom from financial precarity, familiarity with the tacit games of assessment. These things make it easier for some students to convert their social position into academic performance. Despite the sector’s valiant reach for equity, the boundary between a 2:1 and a 2:2 can still reflect background as much as brilliance, yet the classification treats this blend of advantage as evidence of individual superiority.

    The second consequence is that the system squeezes out gains that really matter, but that are not formally sanctioned within our quality frameworks. There is value in what students learn in that space a university punctuates, well beyond curriculum learning outcomes. They navigate difficult group dynamics. They lead societies, manage budgets and broker solutions under pressure. They balance study with work or caring responsibilities and develop resilience, judgement, confidence, and perspicacity in ways that marking criteria cannot capture. For many students, these experiences are the heart of their learning gains. Yet once the classification is issued, that can disappear.

    It is easy to be blithe about these kinds of gains, to treat them as nice but incidental and not the serious business of rigorous academic pursuit. Yet we know this extra-curricular experience can have a significant impact on student success and graduate futures, and it is relevant to those who consume the classification. For many employers, the distinctive value that graduates offer over non-graduates is rarely discipline specific, and a substantial proportion of graduates progress into careers only tangentially aligned to their subjects. We still sell the Broader Benefits of Higher Education™, but our endpoint signaling system is blind to all of this.

    The moral panic about grade inflation then catches us in a trap. It draws us into a game of proving the hierarchy is intact and dependable, sapping the energy to attend to whether we are actually evidencing the value of what has been learned.

    Thought experiment two: What if we gave everyone a First?

    Critics love to accuse universities of handing out Firsts to everyone. So, what if we did? Some commentators would probably implode in an apoplectic frenzy, and that would be fun to watch. But the demand for a signal would not disappear. Employers and postgraduate providers would still want some way to differentiate outcomes. They would resent losing a simple shorthand, even though they have spent years complaining about its veracity. Deprived of the simplicity of the hierarchy, we would all be forced into a more mature conversation about what students can do.

    We could meet that conversation with confidence. We could embrace and celebrate the complexity of learning gain. We could shift to focus on surfacing capability rather than distilling it. Doing so would mean thinking carefully about how to make complexity navigable for external audiences, without relying on a single ranking. If learning gains were visible and tied directly to achievement, rather than filtered through an abstract grading function, the signal becomes more varied, more human, and more honest.

    Such an approach would illuminate the nuance and complexity of talent. It would connect achievement to the equally complex needs of a modern world far better than a classification ever could. It would also change how students relate to their studies. It would free them from the gravitational pull of a grade boundary and the reductive brutality that compresses all their value to a normative measure. They could invest their attention in expansive and divergent growth, in developing their own distinctive combinations of talents. It would position us, as educators, more clearly in the enabling-facilitator space and less in the adversarial-arbiter space. That would bring us closer to the kind of relationship with learners most of us thought we were signing up for. And it would just be …nicer.

    Without classifications the proxy is gone, and universities then hold a responsibility to ensure that students can show their learning gains directly, in ways that are clear, meaningful, and relevant.

    A future beyond classifications

    The sector is capable of imagination on this question – and in the mid-2000s it really did. The Burgess Review was our last serious attempt to rethink classifications. It was also the moment in which our courage and imagination faltered in their alignment.

    The Burgess conclusion was blunt. The classification system was not fit for purpose. The proposed alternative was the Higher Education Achievement Report (HEAR), designed to give a much fuller account of a student’s learning. HEAR was meant to capture not only modules and marks, but the gains in skills, knowledge, competence and confidence that arise from a wider range of catalysts: taught courses, voluntary work, caring responsibilities, leadership in clubs and societies, placements, projects and other contributions across university life. It would show the texture of what students had done and the value they could offer, rather than a single number on a certificate.

    Across Europe, colleagues were (and are) pursuing similar ambitions. Across Bologna-aligned countries, universities have been developing transcript systems that are richer, more contextual and more personalised. They have experimented with digital supplements, unified competence frameworks, micro-credentials and detailed records of project work. The mission is less about ranking learners and more about describing learning. At times, their models make our narrow transcript look a little embarrassing.

    HEAR sat in the same family of ideas, but the bridge it offered was never fully crossed. The system stepped back, HEAR survived as an improved transcript, the ambition behind it did not. And fundamentally, the classification remained at the centre as the core value-signal that overshadowed everything else.

    Since then, the sector has spent roughly two decades tightening algorithms, strengthening externality and refining calibration. Important work, but all aimed at stabilising the classification system rather than asking what it is for – or if something else could do the job better.

    In parallel, we have been playing a kind of defensive tennis, batting back an onslaught of accusations of grade inflation from newspapers and commentators that bleed into popular culture and a particular flavour of politics. Those anxieties now echo in the regulatory system, most recently in the Office for Students’ focus on variation in the way institutions calculate degrees. Each time we rush to prove that the machinery is sound – to defend the system rather than question it – we bolster something fundamentally flawed.

    Rather than obsessing over how finely we can calibrate a hierarchy, a more productive question is what kind of signal a mass, diverse system really needs, and what kinds of value we want to evidence. Two growing pressures make that question harder to duck.

    One is the changing conversation about the so-called graduate premium. For years, policymakers and prospectuses have leaned on an article of faith: do a degree, secure a better job.

    Putting aside the problematics of “better,” and the variations across the sector, this has roughly maintained as true. A degree has long been a free pass through the first gates of a wide range of professions. But the earnings gap between graduates and non-graduates has narrowed, and employers are more openly questioning whether lack of a university degree should necessarily preclude certain students from their roles. In this context, we need to get better at demonstrating graduate value, not just presuming it.

    The other pressure is technological. In a near future where AI tools are routine in almost every form of knowledge work, outputs on their own will tell us less about who can do what. The central question will not be whether students have avoided AI, but whether they can use it in the service of their own judgement, originality and values. When almost anyone can generate tidy text or polished slides with the same tools, the difference that graduates make lies in qualities that are harder to see in a single grade.

    If the old proxy is wobbling from both sides, we need a different way of showing value in practice. That work has at least three parts: how we assess, what students leave with, and how we help them make sense of it.

    How we assess

    Authentic assessment offers one answer; assessment that exercises capability against contexts and performances that translate beyond the academy. But the sector rarely unlocks its full potential. Too often, the medium changes while the logic remains the same. An essay becomes a presentation, a report becomes a podcast, but the grade still does the heavy lifting. Underneath, the dominant logic tends to be one of correspondence. Students are rewarded for replicating a sanctioned knowledge system, rather than for evidencing the distinctive value they can create.

    The problem is not that colleagues have failed to read the definitions. Most versions of authentic assessment already talk about real-world tasks, audiences and stakes. The difficulty is that, when we try to put those ideas into practice, we often pull our punches. Tasks may begin with live problems, external partners or community briefs, but as they move through programme boards and benchmarking they get domesticated into safer, tidier versions that are easier to mark against familiar criteria. We worry about consistency, comparability, grade distributions. Anxieties about loosening our grip on standards quietly win out over the opportunity to evidence value.

    When we resist that domestication, authentic tasks can generate artefacts that stand as evidence of what students can actually do. We don’t need the proxy of a grade to evidence value; it stands for itself. Crucially, the value they surface is always contextual. It is less about ticking off a fixed list of behaviours against a normative framework, and more about how students make their knowledge, talents and capacities useful in defined and variable settings. The interesting work happens at the interface between learner and context, not in the delivery of a perfectly standardised product. Grades don’t make sense here. Even rubrics don’t.

    What students leave with

    If we chose to take evidencing learning gains seriously, we could design a system in which students leave with a collection of artefacts that capture their talents in authentic and varied ways, and that show how those talents play out in different contexts. These artefacts can show depth, judgement and collaboration, as well as growth over time. What is lost is the “rigour” and sanction of an expert judgement to confirm those capacities. But perhaps here, too, we could be more creative.

    One way I can imagine this is through an institutional micro-credential architecture that articulates competences, rather than locking them inside individual modules. Students would draw on whatever learning they have done, in the curriculum, around it and beyond the university, to make a claim against a specific micro-credential built around a small number of competency statements. The assessment then focuses on whether the evidence they offer really demonstrates those competencies.

    Used well, that kind of system could pull together disciplinary work, placements and roles beyond the curriculum into a coherent profile. For those of us who have dabbled in the degree apprenticeship space, it’s like the ultimate end-point assessment, with each student forging a completely individualized profile that draws in disciplinary capabilities alongside adjunct and transdisciplinary assets.

    For that to be more than an internal hobby, it needs to rest on a shared language. The development of national skills classification frameworks in the UK might be providing that for us. It is intended to give us a common, granular vocabulary that spans sectors and occupations, and that universities could use as a reference point when they describe what their graduates can do.

    The trouble is, I doubt, that this kind of skills-map-as-transcript can ever really flourish if it must sit in the shadow of a single classification. That was part of HEAR’s problem. It survived as a supplement while the degree class kept doing the signaling. If we are serious about value, we may eventually need to let go of the single upper-case proxy altogether. Every student would leave not with a solitary number, but with a skills profile that is recognisably linked to their discipline and shaped by everything else they have learned and contributed in the years they spent with us.

    How students make sense of it

    Without support to make sense of their evidence, richness risks becoming noise of a different kind. This is one reason classifications remain attractive. They collapse complexity into simplicity. They offer a single judgement, even if that judgement obscures more than it reveals.

    Students need help to unify their evidence into a coherent narrative. It is tempting to see that as the business of careers and employability services alone, but that would be a mistake. This is a whole-institution task, embedded in curriculum, co-curriculum and the wider student experience.

    From conversations within courses to structured opportunities for reflection and synthesis, students need the means to articulate their value in ways that match their aspirations. They need to design imagined future versions of their stories, develop assets to make them real, test them, succeed and fail, and find direction in serendipity. This project of self, and arriving at that story – a grounded account of who they are now, what they can do and where they might go next – is arguably the apex output of a higher education. It is the point at which years of dispersed learning start to cohere into a sense of direction. And it feels like a very modern version of the old ideal of universities as a place to find oneself.

    Perhaps the sector is now better placed, culturally and technologically, to build that kind of recognition model rather than another supplement. Or at the very least, perhaps the combined pressure of AI and a more skeptical conversation about the graduate premium offers enough of a burning platform to make another serious attempt unavoidable.

    A reborn signal

    I am being playful. I do not expect anyone to actually give every student a First. Classifications have long endured, and they will not disappear any time soon. Any institution that chose to step away from them would be taking a genuine act of brinkmanship. But when confronted with accusations of grade inflation, universities defend their practices with care and detail. What they defend far less often is their students, whose talents and achievements are flattened by the very system we insist on maintaining. We treat accusations of inflation as threats to standards, rather than prompts to talk about value.

    The purpose of these thought experiments is to renew curiosity about what a better signal might look like. One that does justice to the richness of learners’ journeys and speaks more honestly about the value higher education adds. One that helps employers, communities and students themselves to see capability in a world where tools like AI are part of the furniture, and where value is found in how learning connects with real contexts.

    At heart, this is about what and whom we choose to value, and how we show it. Perhaps it is time to return to the thread Burgess began and to pick it up properly this time, with the courage that moment represented and the bravery our students deserve.

    Join Mark and Team Wonkhe at The Secret Life of Students on Tuesday 17 March at the Shaw Theatre in London to keep the conversation going about what it means to learn as a human in the age of AI. 

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  • Making human learning visible in a world of invisible AI

    Making human learning visible in a world of invisible AI

    The mainstreaming of disruptive technology is a familiar experience.

    Consider how quickly contactless payment has become largely unavoidable and assumed for most of us.

    In a similar way, we are already seeing how generative artificial intelligence (GenAI) is, even more rapidly, weaving itself into the fabric of education, work, and wider society.

    In higher education’s search for appropriate responses to the rise of GenAI, much of the emphasis has focused on the technology itself. Yet, as machine learning becomes increasingly embedded in everyday tools and student learning practices, we suggest that this brings new urgency to making the ongoing value of human learning visible. Not to do so risks leaving universities struggling to explain, in an era of increasingly invisible GenAI, what is distinctive about higher education at all.

    A revealing weakness

    Our starting point for a meaningful response to this has been a focus on critical thinking. For a long time, institutions have expressed the importance of students developing as capable critical thinkers through high-level signifiers like graduate attributes, employability skills, and course learning outcomes. But these often substitute for shared understanding, signalling value without making it visible. The rise of GenAI does not challenge critical thinking so much as it reveals our existing weakness in articulating its substance and connection to practice.

    If we were to ask you what critical thinking meant to you, what would you say? And would your students think the same? Through a QAA-funded Collaborative Enhancement Project with colleagues from Stellenbosch University, we have been asking teachers these same questions. While each person we spoke to was quick to value it as an essential learning outcome, we were struck by the extent to which staff acknowledged how little time they had spent reflecting on what it meant to them.

    Through extended conversations with colleagues from our two universities we were able to explore what critical thinking meant in a range of disciplines, and to capture the diverse richness of associated practices, from a search for truth, a testing of beliefs, and an openness to critique to systematic analysis and structured argumentation.

    The right answer?

    Colleagues also identified both strengths and barriers in students’ engagement with critical thinking. Some highlighted students’ social awareness and willingness to experiment, while others noted that students often demonstrate criticality in everyday life but struggle to transfer it to academic tasks. Barriers included a tendency to seek “right answers” rather than engage with ambiguity. As one lecturer observed, “students want the correct answer, not the messy process”. Participants also reflected on the influence of GenAI, with some warning that this technology “gives answers too easily” – allowing students to “skip the hard thinking” – while others suggested it could create space for deeper critical engagement if used thoughtfully.

    From the student perspective, surveys at both institutions also revealed broadly positive perceptions of critical thinking as an essential graduate capability, with respondents articulating their belief in its long-term value including in relation to GenAI, but expressing uncertainty as to how such skills were embedded in their programmes.

    The depth of staff responses demonstrates that a collective wellspring of understanding exists. What we need to do more is find ways to bring this to the surface to inform teaching and learning, communicate explicitly to students, and give substance to the claims we make for higher education’s purpose.

    With this practical end in mind, we used our initial findings to develop a Critical Thinking Framework structured around three interrelated dimensions: Critical Clarity, Critical Context, and Critical Capital. This framework supports educators in identifying the forms of critical thinking they wish to prioritise, recognising barriers that may inhibit its development, and situating these within disciplinary and institutional contexts. It serves both as a reflective tool and a practical design resource, guiding staff in creating learning activities and assessments that make human thinking processes visible in a GenAI-rich educational landscape. This framework and a set of supporting resources, along with our full project report, are now available on the QAA website.

    The slowdown and the human factor

    By working with educators in this way, we have seen the adoption of approaches that slow learning down, providing space to support reflection and make the mechanics of critical thinking more visible to learners. Drawing on popular culture through the use of materials that are familiar to students, such as advertising, music and film, has been used as an approach to reduce cognitive load, enabling learners to focus on actually practising thinking critically in ways that are more visible and explicit.

    Having put this approach into practice, the feedback received across both institutions suggests that our framework not only supports staff in designing effective approaches to promote critical thinking but also gives students opportunity to articulate what it means to them to think critically. As students and staff have been given the opportunity to pause and reflect, it has underpinned meaningful awareness of the value of the human component in learning.

    The growth of GenAI has disrupted the higher education sector and challenged leaders and practitioners alike to think differently and creatively about how they prepare graduates for the future. As an international collaboration, this project has reinforced the view that this challenge is not limited to any single institution, and that there is much to be gained from fostering shared understanding. The results have reminded us that effective solutions can include those that are low-cost and low-risk, simple and practical.

    GenAI makes visible what universities have left implicit for too long. Higher education needs to slow down, not to resist GenAI, but to better articulate and advocate for human learning.

    Join us at The Secret Life of Students on Tuesday 17 March at the Shaw Theatre in London to keep the conversation going about what it means to learn as a human in the age of AI. 

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  • Widely used but barely trusted: understanding student perceptions on the use of generative AI in higher education

    Widely used but barely trusted: understanding student perceptions on the use of generative AI in higher education

    by Carmen Cabrera and Ruth Neville

    Generative artificial intelligence (GAI) tools are rapidly transforming how university students learn, create and engage with knowledge. Powered by techniques such as neural network algorithms, these tools generate new content, including text, tables, computer code, images, audio and video, by learning patterns from existing data. The outputs are usually characterised by their close resemblance to human-generated content. While GAI shows great promise to improve the learning experience in various disciplines, its growing uptake also raises concerns about misuse, over-reliance and more generally, its impact on the learning process. In response, multiple UK HE institutions have issued guidance outlining acceptable use and warning against breaches of academic integrity. However, discussions about the role of GAI in the HE learning process have been led mostly by educators and institutions, and less attention has been given to how students perceive and use GAI.

    Our recent study, published in Perspectives: Policy and Practice in Higher Education, helps to address this gap by bringing student perspectives into the discussion. Drawing on a survey conducted in early 2024 with 132 undergraduate students from six UK universities, the study reveals an impactful paradox. Students are using GAI tools widely, and expect their use to increase, yet fewer than 25% regard its outputs as reliable. High levels of use therefore coexist with low levels of trust.

    Using GAI without trusting it

    At first glance, the widespread use of GAI among students might be taken as a sign of growing confidence in these tools. Yet, when students are asked about their perceptions on the reliability of GAI outputs, many express disagreement when asked if GAI could be considered a reliable source of knowledge. This apparent contradiction raises the question of why are students still using tools they do not fully trust? The answer lies in the convenience of GAI. Students are not necessarily using GAI because they believe it is accurate. They are using it because it is fast, accessible and can help them get started or work more efficiently. Our study suggests that perceived usefulness may be outweighing the students’ scepticism towards the reliability of outputs, as this scepticism does not seem to be slowing adoption. Nearly all student groups surveyed reported that they expect to continue using generative AI in the future, indicating that low levels of trust are unlikely to deter ongoing or increased use.

    Not all perceptions are equal

    While the “high use – low trust” paradox is evident across student groups, the study also reveals systematic differences in the adoption and perceptions of GAI by gender and by domicile status (UK v international students). Male and international students tend to report higher levels of both past and anticipated future use of GAI tools, and more permissive attitudes towards AI-assisted learning compared to female and UK-domiciled students. These differences should not necessarily be interpreted as evidence that some students are more ethical, critical or technologically literate than others. What we are likely seeing are responses to different pressures and contexts shaping how students engage with these tools. Particularly for international students, GAI can help navigate language barriers or unfamiliar academic conventions. In those circumstances, GAI may work as a form of academic support rather than a shortcut. Meanwhile, differences in attitudes by gender reflect wider patterns often observed on academic integrity and risk-taking, where female students often report greater concern about following rules and avoiding sanctions. These findings suggest that students’ engagement with GAI is influenced by their positionality within Higher Education, and not just by their individual attitudes.

    Different interpretations of institutional guidance

    Discrepancies by gender and domicile status go beyond patterns of use and trust, extending to how students interpret institutional guidance on generative AI. Most UK universities now publish policies outlining acceptable and unacceptable uses of GAI in relation to assessment and academic integrity, and typically present these rules as applying uniformly to all students. In practice, as evidenced by our study, students interpret these guidelines differently. UK-domiciled students, especially women, tend to adopt more cautious readings, sometimes treating permitted uses, such as using GAI for initial research or topic overviews, as potential misconduct. International students, by contrast, are more likely to express permissive or uncertain views, even in relation to practices that are more clearly prohibited. Shared rules do not guarantee shared understanding, especially if guidance is ambiguous or unevenly communicated. GAI is evolving faster than University policy, so addressing this unevenness in understanding is an urgent challenge for higher education.

    Where does the ‘problem’ lie?

    Students are navigating rapidly evolving technologies within assessment frameworks that were not designed with GAI in mind. At the same time, they are responding to institutional guidance that is frequently high-level, unevenly communicated and difficult to translate into everyday academic practice. Yet there is a tendency to treat GAI misuse as a problem stemming from individual student behaviour. Our findings point instead to structural and systemic issues shaping how students engage with these tools. From this perspective, variation in student behaviour could reflect the uneven inclusivity of current institutional guidelines. Even when policies are identical for all, the evidence indicates that they are not experienced in the same way across student groups, calling for a need to promote fairness and reduce differential risk at the institutional level.

    These findings also have clear implications for assessment and teaching. Since students are already using GAI widely, assessment design needs to avoid reactive attempts to exclude GAI. A more effective and equitable approach may involve acknowledging GAI use where appropriate, supporting students to engage with it critically and designing learning activities that continue to cultivate critical thinking, judgement and communication skills. In some cases, this may also mean emphasising in-person, discussion-based or applied forms of assessment where GAI offers limited advantage. Equally, digital literacy initiatives need to go beyond technical competence. Students require clearer and more concrete examples of what constitutes acceptable and unacceptable use of GAI in specific assessment contexts, as well as opportunities to discuss why these boundaries exist. Without this, institutions risk creating environments in which some students become too cautious in using GAI, while others cross lines they do not fully understand.

    More broadly, policymakers and institutional leaders should avoid assuming a single student response to GAI. As this study shows, engagement with these tools is shaped by gender, educational background, language and structural pressures. Treating the student body as homogeneous risks reinforcing existing inequalities rather than addressing them. Public debate about GAI in HE frequently swings between optimism and alarm. This research points to a more grounded reality where students are not blindly trusting AI, but their use of it is increasing, sometimes pragmatically, sometimes under pressure. As GAI systems continue evolving, understanding how students navigate these tools in practice is essential to developing policies, assessments and teaching approaches that are both effective and fair.

    You can find more information in our full research paper: https://www.tandfonline.com/doi/full/10.1080/13603108.2025.2595453

    Dr Carmen Cabrera is a Lecturer in Geographic Data Science at the Geographic Data Science Lab, within the University of Liverpool’s Department of Geography and Planning. Her areas of expertise are geographic data science, human mobility, network analysis and mathematical modelling. Carmen’s research focuses on developing quantitative frameworks to model and predict human mobility patterns across spatiotemporal scales and population groups, ranging from intraurban commutes to migratory movements. She is particularly interested in establishing methodologies to facilitate the efficient and reliable use of new forms of digital trace data in the study of human movement. Prior to her position as a Lecturer, Carmen completed a BSc and MSc in Physics and Applied Mathematics, specialising in Network Analysis. She then did a PhD at University College London (UCL), focussing on the development of mathematical models of social behaviours in urban areas, against the theoretical backdrop of agglomeration economies. After graduating from her PhD in 2021, she was a Research Fellow in Urban Mobility at the Centre for Advanced Spatial Analysis (CASA), at UCL, where she currently holds a honorary position.

    Dr Ruth Neville is a Research Fellow at the Centre for Advanced Spatial Analysis (CASA), UCL, working at the intersection of Spatial Data Science, Population Geography and Demography. Her PhD research considers the driving forces behind international student mobility into the UK, the susceptibility of student applications to external shocks, and forecasting future trends in applications using machine learning. Ruth has also worked on projects related to human mobility in Latin America during the COVID-19 pandemic, the relationship between internal displacement and climate change in the East and Horn of Africa, and displacement of Ukrainian refugees. She has a background in Political Science, Economics and Philosophy, with a particular interest in electoral behaviour.

    Author: SRHE News Blog

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

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  • AI is challenging us to relocate our sense of educational purpose in the outward-future rather than the inward-past

    AI is challenging us to relocate our sense of educational purpose in the outward-future rather than the inward-past

    As the debates and discussions around use of AI continue to develop, I reflect that, perhaps too often, the questions we ask as educators about the impacts of AI can be too small.

    There seems to me to be a current over-preoccupation with inward-facing considerations of the impact of AI on our own practices and processes: How we can manage the risks of academic misconduct, how we make our assessments a bit more authentic, how we quality assure students’ development of “AI skills”. I don’t deny that these are important and timely questions, but I think they miss the bigger (knottier) purpose-led picture.

    As AI continues to infuse our work in a variety of means and ways we seem sometimes too focused on management and adaptation of processes, rather than working strategically and purposefully to define broader outcomes which face off into the professional and graduate futures of our students and the world they will occupy and shape over the next 50 years.

    Until we start asking the bigger questions about the more fundamental challenges to educational purposes that AI brings in its wake, we will not be in a position to understand the shifts in educator capabilities and competencies and indeed professional identities that such a paradigm shift will necessarily require.

    Recently, with Prof. Nick Jennings, I argued that we can see two “swim lanes” emerging in AI: one focused on process optimisation and efficiency; one on invention and co-creation. Both are useful, but they require very different things from educators.

    AI literacy for optimisation

    AI tools offer compelling possibilities to support students with personalised learning support, rapid retrieval of relevant information and coaching prompts for personal and career development. I don’t see these tools replacing human academic and student services professionals; instead they offer a degree of personalised insight and augmentation to human-centric services.

    Similarly, AI tools can assist with many of the functions of teaching and learning “delivery”, offering ideas for small-group activities, generating reading lists or other learning resources, offering prompts to structure discussion, rapidly processing student feedback, and so on. Again, this is an efficient, step change augmentation to the spectrum of digital tools that can support effective learning and teaching. Educators will adopt these if they find them to be useful, and according to their disciplinary culture, and their personal orientation towards technology in general.

    Just as we have adapted to email or MS Excel (other software is available) as baseline administrative tools used in organisations and businesses, over time I see that academic workflows will no doubt evolve in response to collective learning and accepted wider practices about the usefulness and effectiveness of various AI tools when applied to different elements of academic practice. Some tools might genuinely make academics’ lives easier; others may promise much and deliver very little.

    From an institutional perspective it makes sense to curate a flow of discussion about the adoption of AI tools for learning, teaching and student support. Doing so allows for the dissemination of useful practice, contributes to collective understanding about AI’s capabilities and limitations and, optimally, ensures that where AI tools are adopted they are applied ethically and in ways that do not compromise academic quality.

    AI literacy for reimagining education futures

    With the potential benefits of AI for optimisation duly noted, I don’t think that is the conversation that is going to be the most material for education leaders in the next few years. For me, AI does not represent a specific set of digital capabilities that must be mastered so much as it points to a future that is fundamentally uncertain, and subject to tectonic disruption.

    That loss of predictability speaks to a very different set of purposes and outcomes for education – less the acquisition of a body of knowledge than the development of high end human competencies exercised and mediated through a developed technological literacy, all underpinned by a disciplinary knowledge base.

    Every new technology, from writing to print to the internet to large language models has prompted a reconsideration of the relationship between educational purposes and disciplinary knowledge. Over time, instead of a student “coming to the discipline” as an apprentice and an assumed future practitioner, disciplinary knowledge is increasingly deployed in the service of a broader range of student outcomes – the discipline “comes to the student.” This is also increasingly reflected in portfolio careers in which core knowledge is rehashed, redeployed, recontextualised and directed towards the challenges of the world and of the workplace, none of which are solved by a single discipline. The difference between previous shifts and the paradigm shift being ushered in by AI is the speed, volatility and unpredictability of what it will do. We are in uncharted waters and, if we are honest, we are not really sure where we are headed or how best to help shape those future outcomes and destinations.

    Despite these shifts, or perhaps in part because of them, the idea of the professor still defaults to the guardian and steward of disciplinary knowledge. Recognising that the strength of UK HE in particular comes from a tradition of being organised around somewhat compartmentalised deep disciplinary knowledge, this conceptualisation has remained remarkably consistent even as higher education has become more widely available and serving purposes beyond the passing on of knowledge.

    In this sense AI can never (and should never) “replace” academics as stewards of disciplinary knowledge, but it should prompt a deep examination of what that reconfiguration of the relationship between knowledge and education purpose looks like for the different disciplines – and the moments when students need to cross disciplinary boundaries in service of their potential futures, rather than the futures we imagined when in their shoes.

    The questions and discussion I am interested in curating asks academics about the potential shape of their discipline and its associated professions in 50 years: What does it mean to think, and “do” your discipline with and alongside AI? What does AI do to the professional practices and identities of the professions allied to your disciplines? The answers to such questions are more readily imagined through contemporary cutting edge research agendas than by established approaches to engaging students with existing bodies of knowledge.

    It is only in light of our imagination of the possible futures that await our students that we can start asking what kind of educational environments and approaches we need to build to create the conditions for the development of the skills sets, attitudes and competencies they will need.

    My hunch is that we will collectively need to “unwire” ourselves from “standard” PG Cert and PG Dip teaching development tracks and be prepared to look outside the classics of higher education pedagogy and literature, including to primary education, and innovative workplace CPD to find the approaches that work best. While we might retain a foundational basket of knowledge and skills required for entry to the academic profession, I think these will resonate more strongly with a broader set of high end human competencies than with the traditional skills associated with teaching development.

    It is likely we’ll need to take a more experimental, co-creative approach to the higher education pedagogy, which engages in the outward facing futurology of graduate paths across the next 50 years as a fundamental starting point for considering our own purpose-led practices. In this we might then retain concepts and theories that serve those purposes while discarding those that have outlived their usefulness.

    Sam Grogan will be among the speakers at Kortext LIVE education leaders event on 11 February in London, as part of a panel discussing the Wonkhe/Kortext project Educating the AI Generation. Find out more and book your free spot here.

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  • A Practical Guide – The 74

    A Practical Guide – The 74

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  • 25 of Our Top Stories About Schools, Students and Learning – The 74

    25 of Our Top Stories About Schools, Students and Learning – The 74

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  • The Top 20 Education Next Articles of 2025

    The Top 20 Education Next Articles of 2025

    In a journal devoted to U.S. education reform, some recurring themes in its content are expected: student achievement, curriculum, teacher effectiveness, school choice, testing, accountability. Other topics are more contemporaneous, reflecting the functional reality of American schooling in its present context. The latter group may capture just a moment in time and give future education historians a glimpse at what mattered to early 21st century reformers (and seem quaint in hindsight). It may also reflect prescient insights from leaders, thinkers, and scholars—contributions that document the early stages of a significant transformation in education policy and practice (and later be deemed ahead of their time).

    What we can say confidently is that Education Next published a good mix of the classic and the contemporary in 2025, just as it has each year in its quarter century of existence. You can see for yourself below in our annual Top 20 list of most-read articles, which features an assortment of writings by researchers, journalists, academics, and teachers.

    Among the traditional fare, readers turned to EdNext to keep apprised of developments in classroom instruction, from reading to literacy to history. They wanted to know if the U.S. might be better off evaluating schools using the European model of inspections rather than, or in addition to, student test scores. Amid ongoing debates about the merits of using standardized tests to gauge student preparation, readers were drawn to the findings of researchers in Missouri that 8th graders’ performance on the state’s MAP test are highly predictive of college readiness. In the realm of teachers and teaching, proponents of merit pay received a boost by an analysis of Dallas ISD’s ACE program, which was shown to improve both student performance and teacher retention in the district.

    As for school choice, Education Next followed successes like the expansion of education savings account programs, the proliferation of microschools, and the federal scholarship tax credit passed by Congress as part of the One Big Beautiful Bill Act. But the stumbles of choice had more of a gravitational pull for readers. There were the defeats of private-school voucher measures in three states—continuing a long string of choice failures at the ballot box. There are the enrollment struggles of Catholic schools, which researchers found are impacted by competition from tuition-free charter schools. And just when Catholic and other private religious schools could have gotten a shot in the arm by being allowed to reformulate as religious charters, the Supreme Court deadlocked on the constitutionality of the question, leaving the matter to be relitigated for another day.

    There was no shortage of timely topics that exploded onto the scene and captivated readers. American education is still grappling with the fallout from the Covid-era school shutdowns, now five years in the rearview. Many harbor consternation about the politics of pandemic closures, as demonstrated by the enthusiasm over a new book that autopsied the decisions of that era and the subsequent book review that catapulted onto this year’s list (an unusual feat!). And now there’s research to corroborate the disaster closures were for public education. Two Boston University scholars find evidence of diminishing enrollment in public middle schools, an indication that families whose children were in the early grades in 2020 are parting for the more rigorous shores of private choice. But the post-pandemic problems in schooling have not been uniform. In one of the most-read articles this year, founding EdNext editor Paul Peterson and Michael Hartney show how, based on recent NAEP results, learning loss was greater among students in blue states that had more prolonged school shutdowns than in red states that reopened more quickly.

    Meanwhile, everyone in education circles continues to grapple with what to do about technology in the classroom. Two writers did so in our own pages, presenting opposite perspectives on Sal Khan’s prediction that AI will soon transform education with the equivalent of a personalized tutor for each student. And one of our favorite cognitive scientists gave readers a different way of thinking about how digital devices affect student attention.

    It is perhaps fitting that our most-read article of 2025 was also the cover story of the last print issue of Education Next. (You can read more about our transition to a web-only publication here.) After Donald Trump reassumed the presidency this year and his administration enacted major reductions to the federal bureaucracy, several education-focused programs (and indeed the entire U.S. Department of Education) came under intense scrutiny. One target was Head Start, in part because Project 2025 called to eliminate the program on the grounds it is “fraught with scandal and abuse” and has “little or no long-term academic value for children.” Paul von Hippel, Elise Chor, and Leib Lurie tested those claims against the research and found little basis for them. Yet they also highlight lingering questions about the program’s impact on students’ long-term success—and opportunities to answer them with new research. As of this writing, the nation’s largest early-education program survives, but the sector is still watching and waiting.

    And so are we all for what will happen next in education. Some issues captured by Education Next this year will continue into 2026. Some will flame out. And others that are unforeseen will arise. Readers can depend on Education Next to lean into all the twists and turns that come in the year ahead.

    The full top 20 list is here:

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  • Transparency about AI should be a sector-wide principle

    Transparency about AI should be a sector-wide principle

    Josh Thorpe’s recent Wonkhe article – What should the higher education sector do about AI fatigue? – captured how many are feeling about artificial intelligence. The sector is tired of hype, uncertainty, and trying to keep up with a technology that seems to evolve faster than our capacity to respond. But AI fatigue, as that article suggests, is not failure. It’s a signal that we need to pause, reflect, and respond with human-centred coherence.

    One of the most accessible and powerful responses to AI uncertainty is transparency. By taking a consistent approach to declaring AI use in work or assessment, many educators are taking a first step by simply declaring: “no AI is used in…”

    The statement alone supports development of trust between educators and students, and it can create space for dialogue. It’s not about rushing into AI adoption, it’s about being honest and intentional, whatever the current practice. From there, we can begin to explore what small, discipline-relevant, and appropriate uses of AI might look like.

    The journey starts with transparency, not technology. We need to support staff in engaging with AI in ways that feel ethical, manageable, and empowering. We mustn’t begin with technical training or institutional mandates. We must begin with a simple request to communicate clearly about AI use (or non-use) in our teaching, learning and assessment practices.

    Sheffield Hallam University has implemented an AI Transparency Scale as a communication tool that helps educators consider how they disclose AI use to students, and supports how they can clarify expectations for students in assessment. It’s a conversation starter which prompts educators to reflect on whether AI tools are used in their practice, how this use is communicated with students, and how transparency supports academic integrity and student trust. The scale is helping move from uncertainty to clarity. Not by simplifying AI, but by humanising and clarifying how we engage with it.

    Moving to transparency

    For educators wondering where to start, confident transparency begins with making AI clear and understandable within its specific context. Transparency builds trust and sets clear expectations for staff and students. A simple statement, even a neutral one such as “AI tools were not used in the development of this module,” provides clarity and signals openness. You might adopt a tool like the AI Transparency Scale where prompts can scaffold your communication of AI use or create your own local language. Even short discussions in course or programme team meetings can surface valuable insights and lead to shared practices. The goal is not just to disclose, but to create a shared understanding and practice.

    Engaging students in the conversation about AI and inviting them to share how they are using AI tools helps educators understand emerging practices and co-create ethical boundaries. As Naima Rahman and Gunter Saunders noted in their Wonkhe article, students want AI integrated into their learning – but they want it to be fair, transparent, and ethical.

    Listening and responding transparently reinforces trust. Together, explore questions such as: “what does responsible AI use look like in our subject area?” Consider where automation or analysis might add value, and where human judgment remains essential.

    Transparency here means being explicit about why certain tasks should remain human-led and where AI might play a supportive role. Positioning students as co-leaders in these discussions builds a stronger, more transparent foundation for responsible AI use.

    From individual burden to institutional strategy

    Josh Thorpe’s article rightly calls out the lack of institutional coordination and fragmented AI discourse. The burden of response has fallen largely on individuals, with limited support from policy, leadership, or infrastructure.

    To move forward, we need coherent institutional leadership that frames AI not just as a technical challenge, but as a support, pedagogical, and ethical challenge. By sharing our experiences, resources, and approaches openly, we can develop shared principles that can guide diverse practices across the sector. Finally, we need alignment with the changing nature of authorship, assessment, and professional competence in an AI-enabled world. Simon Sneddon goes into the need to prepare students for the world of (artificial intelligence-enabled) work in another recent Wonkhe article.

    Transparency offers a bridge between policy and practice. It’s a principle that can be embedded in institutional guidance, supported through professional development, and aligned with sector-wide values.

    As the Office for Students, Jisc, and other bodies continue to shape the AI landscape and how we navigate it, institutions must find ways to empower their staff, not just inform them. That means creating space for reflection, dialogue, and ethical experimentation.

    Transparency alone will not solve the challenges of AI in education, but it is a good place to start. The sector can begin to move from fatigue to fluency, one transparent step at a time.

    AI transparency statement: In developing this article, I used Microsoft Copilot to support the writing process. I provided original textual inputs, guided the reference of relevant existing materials, added additional sources, and critically reviewed and refined generated outputs to produce the final piece. This corresponds to level 3 of the AI Transparency Scale, indicating active human oversight, original content, and editorial control.

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  • Why the AI Bubble Is Actually a $60T Black Hole (GEN)

    Why the AI Bubble Is Actually a $60T Black Hole (GEN)

     

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  • What Does AI Readiness Mean for Schools? – The 74

    What Does AI Readiness Mean for Schools? – The 74


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    Class Disrupted is an education podcast featuring author Michael Horn and Futre’s Diane Tavenner in conversation with educators, school leaders, students and other members of school communities as they investigate the challenges facing the education system in the aftermath of the pandemic — and where we should go from here. Find every episode by bookmarking our Class Disrupted page or subscribing on Apple Podcasts, Google Play or Spotify.

    Michael and Diane sit down with Alex Kotran, founder and CEO of the AI Education Project (AIEDU), to dive into what true “AI readiness” means for today’s students, educators and schools. They explore the difference between basic AI literacy and the broader, more dynamic goal of preparing young people to thrive in a world fundamentally changed by technology. The conversation ranged from the challenges schools face in adapting assessments and teaching practices for the age of AI, to the uncertainties surrounding the future of work. The episode asks key questions about the role of education, the need for adaptable skills, and how we can collectively steer the education system toward a future where all students can benefit from the rise of AI.

    Listen to the episode below. A full transcript follows.

    *Correction: At 17:40, Michael attributes an idea to Andy Rotherham, The idea should have been attributed to Andy Smarick.

    Diane Tavenner: Hey, Michael.

    Michael Horn: Hey, Diane. It is good to see you as always. Looking forward to this conversation today.

    AI Education and Literacy Insights

    Diane Tavenner: Me, too. You know what I’m noticing, first of all, I’m loving that we’re doing a whole season on AI because I felt like the short one was really crowded. And now we get to be very expansive in our exploration, which is fun. And that means we’ve opened ourselves up. And so there’s so much going on behind the scenes of us constantly pinging each other and reading things and sending things and trying to make sense of all the noise. And just this morning, you opened it up super big. And so it works out perfectly with our guest today. So I’m very excited to be here.

    Michael Horn: No, I think that’s right. And we’re having similar feelings as we go through the series. And I’m, I’m really excited for today’s guest and because I think, you know, there are a lot of headlines right now around executive actions with regards to AI or, you know, different countries making quote, unquote, bold moves, whether it’s South Korea or Singapore or China and how much they’re using AI in education or not. We’re going to learn a lot more today, I suspect, from our guest, and he’s going to help put it all in the context, hopefully, because we’ve got Alex Kotran, excuse me, joining us. He’s the founder and CEO of the AI Education Project, or AIEDU. And AIEDU is a nonprofit that is designed to make sure that every single student, not just a select few, understands and can benefit from the rise of artificial intelligence. Alex is working to build a national movement to bring AI literacy and readiness into K12 classrooms, help educators and students explore what AI means for their lives, their work, and their futures.

    And so with all that, I’m really excited because, as I said, I think he’s going to shed a little bit of light on these topics for us today. I’m sure we’re only going to get to scratch the surface with him because he knows so much, but he’s really got his pulse on the currents at play with AI and education, and perhaps he can help us separate some of the hype from reality, or at least the very real questions that we ought to be asking. So, Alex, with all that said, no pressure, but welcome. We’re excited to have you.

    Alex Kotran: I’ll do my best.

    Michael Horn: Sounds good. Well, let’s start maybe just your personal story right into this work and what motivates you around this topic in particular, to spend your time on it.

    Alex Kotran: I’ve been in the AI space for about 10 years. But you know, besides being sort of proximate to all these conversations about AI, you know, I don’t have a background in software, computer science. I don’t think I have ever written a line of code. I mean, my dad was a software engineer. He teaches CS now. No background in technology or CS, no background in education. And so I actually, I had funders ask me this when I first launched AIEDU like, well like, why are you here? Like, what’s, what’s your role in all of this? You know, my background is in really political organizing. I started my career working on a presidential campaign, went and worked for the White House for the Obama administration, doing outreach for the Affordable Care act and other stuff like Ebola and Medicare and, and then found myself in D.C.

    and after I just kind of got burned out of politics for reasons people probably don’t need to hear and can completely understand. And so it wasn’t that I was so smart to like, oh, I knew AI was the next thing. I just was like, I really want to move to San Francisco. I visited there, visited the city like twice and just fell in love and sort of fell into tech and an AI company that was working in cleantech. And so I was sort of doing AI work before it was really cool. It was like back in 2015, 2016. And then I ended up getting like what at the time was a kind of a really random job that I had a lot of mentors who were like, I don’t know, Alex, like AI, like this is just like a fringe, you know, emerging technology kind of like, you know, 3D printing and VR and XR and the Metaverse, you know, is that really like what you should do? And I just had like, nah, I just want to learn.

    It seems really interesting. And that’s why I joined this AI company essentially working for the family office for the CEO. It was like, sort of a hybrid family office, corporate job, doing CSR, corporate social responsibility in the legal sector. This is the first company to build AI tools for use in the law. And so I was sort of charged with how do we advance the governance of AI and sort of like the safe and ethical use of AI and the rule of law. And so I basically had a blank canvas and ended up building the world’s first AI literacy program for judges. I worked with the National Judicial College in Stanford and NYU Law, trained thousands of judges around the world in partnership, by the way, with non profits like the Future Society and organizations like UNESCO. And because my parents are educators, I, you know, and my parents are foreign immigrants as well.

    And so they always ask me about my job and really trying to convince me to go back, to go to law school or get a PhD or something. And I was like, well, no, but, you know, I actually, I’m, I don’t need to go to law school. I’m actually training judges. Like, they’re, they’re coming to learn from me about this thing called AI. And my mom was like, oh, like, well, that sounds so interesting. You know, have you thought about coming, you should come to my school and teach my kids about AI. And she teaches high school math in Akron, Ohio. And I was just like, surely your kids are learning about AI.

    That’s, you know, my assumption is that we’re at a minimum talking to the future workers about the future of work. I just assume that, you know, like, you know, judges who tend to be older, like, they kind of need to be caught up. And after I started looking around to see, like, is there other curriculum that I could share with my mom’s school, I found that there really wasn’t anything. And that was back in 2019. 2018/2019. So way before ChatGPT and thus AIEDU was born when I realized, OK, this doesn’t exist. This actually seems like a really big problem because even as, even as early as 2018, frankly, as early as 2013, people in the know, technologists, people in Silicon Valley, labor economists, were sounding the alarms, like, AI is, you know, automation is going to replace like tens of millions of jobs.

    This is going to be one of the huge disruptors. You had the World Economic Forum talking about the fourth Industrial Revolution. Really, this wasn’t much of a secret. It was just, you know, like, esoteric and like, you know, in the realm of like certain nerdy wonky circles. And it just, there wasn’t a bridge between those, the people that were meeting at the AI conferences and the people in education. And I would really say, like, our work now is still anchored in this question of, like, how do you make sure that there is a bridge between the cutting edge of technology and the leadership and decision makers who are trying to chart a course not over the next two years, which is sort of like how a lot of, I think Silicon Valley is thinking in the sort of like, very immediate reward system where they’re just, you know, like, they’re, they’re looking at the next fundraise. But in education, you’re thinking about the next 10 years. These are huge tanker ships that we’re trying to navigate now and we’re entering.

    I think this is such a trope, but, like, we are really entering uncharted waters. And so, like, steering that. That supertanker is hard and I suppose to really belabor it as maybe AIEDU is sort of like the nimble tugboat, you know, that’s trying to just sort of like, nudge everybody along and sort of like guide folks into the future. And that demands answering some of this core question of the future of work, which hopefully we’ll get some more time to talk about.

    Michael Horn: Yeah, I want to, I want to move there in a moment, but I, but first, like, I maybe I don’t know that all of our audience will be caught up with all the, you know, sort of this macro environment right where. Where we sit right now in terms of the national policy, executive actions as it pertains to AI and education. They’ve probably heard about it, but don’t know what it actually means, if anything. And so maybe sort of set the scene around where we are today nationally on these actions? What if it is actually meaningful or impactful? What if it is maybe more lip service around the necessity of having the conversation rather than moving the ball, just sort of set the stage for us where we are right now.

    Alex Kotran: It’s really hard to say. I mean, there’s been a lot of action at the federal level and at state levels and schools have implemented AI strategies. The education space is inundated with, like, discussion and initiatives at working groups and bills and, you know, like, pushes for, like, AI and education. I think the challenge now is, like, we really haven’t agreed on, like, to what end? Like, is this, you know, are we talking about using AI to advance education as a tool? So, like, can AI allow us to personalize learning and address learning gaps and help teachers save time, or are we talking about the future of work and how do we make sure kids are ready to thrive? And there are some that say, well, they. We just need to get them really good at using tools. Which is a conversation I literally had earlier today where there was like a college to career nonprofit and they were like, well, we’re trying to figure out what tools that help kids learn because we want them to be able to get jobs.

    I think like AIEDU, like, our work is actually, we don’t build tools. We don’t even have a software engineer on our team, which we’re trying to fix, like, if there’s a funder out there that would like to help fund an engineer, we’d love to have one. But our work is really systems change. Because if you like, zoom out and like, this is, I think, where I do have this skill set. And it’s kind of like, again, it’s a bit niche.

    The education system is not. It’s not one thing. It’s like, it’s sort of like an organism. The same way that like redwood trees are organisms. Like, they’re kind of all connected, the root structure. But it’s actually like you’re looking at a forest that looks very different, you know, that’s not centralized. You know, every state kind of has their own strategy. And frankly, every district, in many cases, you’re talking about, you know, in some cases, like government scale, procurement, discussion, bureaucracy involved.

    Advancing AI Readiness in Education

    Alex Kotran: So if you’re trying to do systems change, this is really a project of like, how do you move a really heterogeneous group of humans and different audiences and stakeholders with different motivations and different priorities? And so our work is all about, OK, like, setting a North Star for everybody, which is like defining where we’re actually trying to go, what. And we use the word AI readiness, not AI literacy. Because what we’re, what we care about is kind of irrespective of whether kids are really good at using AI. Like, are they thriving in the world? And then like, how do you get there? Like, like most of our budget goes to delivering that work, you know, doing actual services, where we’re building the human, basically building the human capital and like, the content. So like training teachers, building curriculum, adapting existing curriculum, more so than building new curriculum, but like integrating learning experiences into core subjects that build the skills that students are going to need. And those skills, by the way, are not just AI literacy, but durable skills like problem solving, communication, and core content knowledge frankly, like being able to read and write and do math, we think is actually really important still, if not more important. And then sort of the third pillar to our work is really catalyzing the ecosystem.

    And because the only way to do this is by building a movement, right? Like, sure, there. There’s an opportunity for someone to build a successful nonprofit that’s delivering services today. But if you actually want to change the world and really solve this problem on the timescale required, you have to somehow rally the entire, there’s like a million K12 nonprofits. We need all of them. This is like an all hands on deck moment. And so our organization is really obsessed with, like, how do we stay small and almost like operate as the intel inside to empower, like, the existing nonprofits so that they don’t have to all pivot and, like, become AI because, like, there’s just not enough AI experts to go around. If every school and every nonprofit wanted to hire an AI transformation officer.

    Like, there just wouldn’t be enough people for them to hire.

    Diane Tavenner: Yeah, they’re still trying to hire a good tech lead in schools. We’re definitely not getting an AI expert in every school soon. So you’re, you’re speaking my language, you know, sort of change management, vision, leadership 101, etc. I’m wondering, you know, sort of not necessarily the place we were thinking we’d go in this conversation, but I think it’d be fun to go, like, really deep for a moment that I think is related to your North Star comment. What does school look like in the age of AI? When kids are flourishing, when young people are flourishing, and when they’re successfully launching? I think that’s what the North Star has to describe.

    And you just started naming a whole bunch of things that are still important in school, which feel very familiar to me. They’re all parts of the schools that I’ve built and designed and whatnot. And so I think one of the interesting things is maybe we’ll then build back up to policy and whatnot. But, like, what does it look like if we succeed, if there is this national movement, we’re successful. We have schools or whatever they are that are enabling young people to flourish. What do you think that that looks like?

    Alex Kotran: Yeah, this is the question of our day. Right. I mean, I think this is where, I mean, just to go back to this, like, state of play. I think, like, we’re kind of. It’s very clear that we are in the age of AI, right? This is no longer some future state. And frankly, like, ignore all the talk about AI bubbles because it kind of doesn’t matter. I mean, there was, there was like, there’s always a bubble. There was a bubble when we had railroads.

    There was a bubble when we had, like, in the oil boom. There was a bubble with the Internet. You know, there probably will be some kind of a bubble with AI, but that’s kind of like part and parcel with transformational technologies. Nobody who’s really spent time digging these technologies believes that there’s not going to be AI sort of totally proliferated throughout our work in society in like, 10 years, which is, again, the timeframe that we’re thinking about. The key question is, though, like, what is it? Like, what does it mean to thrive? And so there’s more than just getting a job. But I think most people would admit that, like, having a job is really important. So maybe we start there and we can also talk about, you know, the, the social, emotional components of just sort of like, being able, being resilient to some of like, the onslaught of synthetic media and like, AI companions as other stuff. One of, if not the most important thing is, like, how do you get a job and like, have like, you know, be able to support yourself and, and that question is really unanswered right now.

    Uncertainty in AI and Future Jobs

    Alex Kotran: And so everybody in the education system is trying to figure out, like, well, what is our strategy? But we don’t know where we’re going? Like, we really do not know what the jobs of the future are. And like, I’ve, like, you hear platitudes like, well, it’s not that AI is going to take your job, it’s that somebody using AI is going to take your job. Which is a kind of a dumb thing to say because it’s, it’s correct. I mean, it’s like, it’s like, basically like, okay, either AI is going to do all the jobs, which I don’t like, like, that actually may happen, some people say, sooner than later. I just assume it’s going to be a long, long time if it ever, if we ever get there. And so until we get there, that means that there are humans doing jobs and AI and technology doing other aspects of work. So, like, what are the humans doing is really the important question. Not just like, are they using AI? But like, how are they using AI? How aren’t they using AI? Until we get more fidelity about what the future of work looks like, what are the skills you should be teaching? Because, like, you know, like, I think a lot about, like, cell phones.

    And you go back to 2005 and you can imagine a conversation where it’s like, and all this is completely true, right? In 2005, it would be correct to say that, you know, you will not be able to get a job if you don’t know how to use a cell phone. You will be using a cell phone every single day, whether you’re a plumber or a mathematician or an engineer or an astrophysicist. And yet I think most of us would agree that, like, we shouldn’t have, like, totally pivoted education to focus on, like, cell phone literacy because, like, nobody’s going to hire you because you know how to use a phone and AI like, probably is going to some degree get there. I mean, it’s already sort of there, right? Like, sure, there are people who will charge you money to teach you prompt engineering, but you could also just open up Gemini and say, help me write a prompt. Here’s what I want to do. And it will basically tell you how to do it.

    Diane Tavenner: I mean, we. You’ve seen this. You might not be old enough to remember this, but I was a teacher when everyone thought it was a really good idea to teach keyboarding in school. It’s like a class. What we discovered is actually if you just have people using technology, they learn how to use the keyboard. Right? Like, it happens in the natural course of things and you don’t have a class for it. So what I hear you saying is like, your approach is not about this sort of, you know, there’s some finite set of information or skill, you know, not even skills in many ways that we’re going to teach kids. But it’s like, what does it look like to have them ready for the world that honestly is here to today and then keeps evolving and changing over the next 10 years? And so where to even go with that, Michael because.

    Michael Horn: I mean, part of me wonders, Alex, like, if I start to name the things that remain relevant, what, like, maybe the conversation to have is like, what’s less relevant in your view, based on what the world of work and society is going to look like?

    What’s the stuff that we do today that you know, will feel quaint? Right, that we should be pruning from?

    Diane Tavenner: Yeah, cursive handwriting. That is still hotly debated by, by the way.

    Alex Kotran: But, you know, although you get like Deerfield Prep and they’re going back to pen and paper.

    Michael Horn: Right. So that, I mean, that’s kind of where I’m curious. Like, what practices would you lean into? What would you pull away from? Because, I mean, that’s part of the debate as well. Like our friend Andy Rotherham, I believe at the time we’re recording it, just had a post around how it’s time for a, you know, a pause on AI in all schools. Right. Not sure that’s possible for a variety of reasons. But, like, what would you pull back on? What would you lean into? What would you stop doing that’s in schools today, as you think about that readiness for the world that will be here in your, we’re all guessing, but 10 years from now.

    Alex Kotran: Now, what to pull back on? I mean, look, take home essays are dead. Don’t assign take-home essays like the detectors are imperfect. It’s like, and as a teacher, do you really want to be like an, you know, a cyber forensics specialist? Like that’s not the right use of your time. And also you’re using AI. So it’s a bit weird to the dissonance of like, oh, like empowering teachers with AI, but then like, we need to prevent kids from using it. But I think they’re like low hanging fruit. Like, OK, don’t assign take-home essays.

    The way to abstract, that is students are. You can call it cheating, let’s just call it shortcuts. What we do need to do is figure out, OK, how can AI, how is AI being used as a shortcut? And whether you ban it in schools, kids are going to use it out of school. And so teachers need to figure out how to create assessments and homework and projects that design such that you can’t just use AI as a shortcut. And there’s like, this is a whole separate conversation. But just like to give one example, having students demonstrate learning by coming into the class and presenting and importantly having to answer questions in real time about a topic. You can use all the AI you want, but if you’re going to be on the spot and you don’t understand whatever the thing is that you’re presenting about and you’re being asked questions like, you know, that’s the kind of thing where sure, use all the AI. If it’s helpful, you might just.

    But ultimately you just need to learn the thing. But like the more important question is like, I don’t know if school changes as much as people might think. I think it does change. I think there’s a lot that we know needs to change that is kind of irrespective of AI. Like we need learning to be more engaging. We need more project based learning. We need to shift away from just sort of like pure content knowledge, memorization. But that’s not necessarily new or novel because of AI.

    I think it is more urgent than ever before.

    Michael Horn: I’m curious, like what’s. Because I do think this is also hotly debated, right? Like in terms of the role of knowledge and being able to develop skills and things of that nature. And so I’m just sort of curious, like what’s the thin layer of knowledge you think we need to have? Or, or like Steven Pinker’s phrase, common knowledge Right

    And what’s the stuff we don’t have? Like we don’t have to memorize state capitals, right? Maybe.

    Diane Tavenner: No. Yeah, I don’t think we need to memorize the state capital, because, yeah, but keep going.

    Michael Horn: Yeah, yeah, I’m curious now. It’s like, right, like as we think about, because we do have this powerful assistant serving us now and we think about what that means for work. And I, but I guess I’m just curious, like, what does that really mean in terms of that balance, right? Like, what is all knowledge learned through the project or this, you know, how do we think about, you know, and it’s a lot of just in time learning perhaps, which is more motivating. I’m curious, like, how you think about that.

    Alex Kotran: I think this needs to be like, backed by, like research, right? Like, sure, it probably is, right, that you don’t need to memorize all the state capitals. But then I think you, you start to get to a place where like, OK, well, but do you even need to learn math? Because AI is really good at math and I think math is actually a good analog because I don’t really use math very much or I use relatively simplistic math day to day. I, I think it was really valuable for me to like, have spent the time building computational thinking skills and logic. And also just math was really hard for me and it was challenging. And like the process of learning a new abstract, hard thing. I do use that skill, even some of the rote memorization stuff. You know, my brother went to med school and like they spent a lot of time just memorizing like completely just like every tiny aspect of the human body.

    They like have to learn it. It’s actually like, I think doctors are really interesting, a great way to kind of double click on this because if doctors don’t go through all of that and don’t understand the body and go through all of the rote process of literally taking like thousand question tests where they have to know like random things about blood vessels. And even if they’re never going to deal with that specific aspect of the human body, doctors kind of like build this sort of like generalized set of knowledge and then also they spend all this time like interacting with real world cases. And you, you start to build instincts based on that and, and you talk to hospitals about like, oh, what about, you know, AI to help with diagnosis? And one of the things I hear a lot of is, well, we’re worried about doctors losing the capacity to be a check on the AI because ultimately we hear a lot about the human in the loop. The human in the loop is only relevant if they understand the thing that they’re looped into. So, yeah, so like, I don’t know, I mean, maybe we.

    Diane Tavenner: Yeah, you’re onto something. You’re spurring something for me that I, I actually think is the new thing to do and haven’t been doing and aren’t talking about. And that is this, let me see if I can describe it as I’m understanding it, unfold the way you’re talking about it. So I had a reaction to the idea of memorizing the state capitals because memorizing them is pretty old school, right? It calls back to a time where you aren’t going to be able to go get your encyclopedia off the shelf and look up the capitals. Like you have to have that working knowledge in your mind, if you will, to have any sense of geography and, you know, whatever you might be doing. And it was pretty binary.

    Like it really wasn’t easy to access knowledge like that. So you really did have to like memorize these things. Math, multiplication tables get cited often and whatnot for fluency in thinking and whatnot. So I don’t think that goes away. But it’s different because we have such easy access to AI and so there isn’t this like dependency on, you’re the only source of that knowledge, otherwise you’re not going to be able to go get it. But it doesn’t take away the need to have that working understanding of the world and so many things in order to do the heavier lifting thinking that we’re talking about and the big skills. And I think that, I don’t think there’s a lot of research on that in between pieces, like, how do you teach for that level of knowledge acquisition and internalization and whatnot? And how do you then have a, you know, a more seamless integration with the use of that knowledge in the age of AI when it’s so easily accessible? So that feels like a really interesting frontier to me. That doesn’t look exactly the same as what we’ve been doing, but isn’t totally in a different world either.

    It is restricted, responsive and reflective of the technology we have and how it will get used now.

    Rethinking Assessments and Learning Strategies

    Alex Kotran: Yeah, it’s, it’s a helpful push because like, what I’m not saying is that I know everything in school is fine. I don’t think I’ve ever talked to a superintendent who would say, oh, I’m feeling good about our assessment strategy. Like, we’ve known that and because really what you’re describing is assessments like what, like what are we assessing in terms of knowledge, which becomes the driver and incentive structure for teachers to like, you know, because to your point. Are you spending five weeks just memorizing capitals or are you spending two weeks and then also then saying, OK, now that you’ve learned that, I want you to actually apply that knowledge and like come up with a political campaign for governor of, you know, a state that you learned about and like, tell us about like why you’re going to be picking those. You know, tell us about your campaign platform. Right. And you know, like, how is it connected to what you learned about the geography of that state? So it’s like adapting, integrating project based learning and more engaging and relevant learning experiences. And then like the mix and the balance of what, what’s happening in the classroom is sort of, and this is the, the challenging thing because it’s like the assessments will inform that, but it’s also there the assessments are downstream of sort of like it’s not just about getting the assessments right, but it’s like, why are we assessing these things? And so that you very quickly get to like, well like, what is the future of work? And because like, yeah, I mean like, you probably don’t need to learn the Dewey Decimal system anymore.

    Even though being able to navigate knowledge is maybe one of the most important things, certainly something I use every day.

    Diane Tavenner: One of the things we tend to do in US Education, Alex, is be so US centric and we forget that other people on the planet might be grappling with some of these things. I know you track a lot of what happens around the globe. What can we look at as models or interesting, you know, experiments or explorations. Everything from like big system change work, which I know we have different systems across the world, so that’s different. It’s a little bit, it’s not groundswell, it’s a top down but like anything from policy, big system all the way down to like who, who might be doing interesting things in the classroom. Where are you looking for inspiration or models across the globe?

    Alex Kotran: I mean, South Korea is a really interesting case study. You mentioned South Korea. I think at the beginning of this, during the intro they were just in headlines because they had done this big push. They would like roll out personalized learning nationwide. And then they announced that they were rolling back or sort of slowing down or pausing on the strategy. I forget if it was a rollback or a pause, but they’re basically like, wait, this isn’t working. And what they found is that they hadn’t made a requisite investment in the teacher capacity. And that was clear.

    And so part of the reason I’m tracking that is because I don’t know that there’s very much for us to learn from what any school is doing right now, beyond, like, there’s a lot for us to learn in the sense of like, how can we empower teacher, like, how do we empower teachers to run with this stuff? Because they are doing that. You know, like, I think there’s a lot to learn from a, like a mechanical standpoint of like, implementation strategies. But I don’t know that anybody has figured this out because like, nobody can yet describe what the future of work looks like. And I know this because the AI companies can’t even describe what the future of work looks like. You know, you had like Dario Amodei at Anthropic seven months ago, saying in six months, 90% of code is going to be written by AI, which is not the case. Not even close.

    Diane Tavenner: And Amazon’s going to lay off 30,000 white collar workers this week,

    Alex Kotran: Which they did.. Yes. And so you have. But is that really because of AI or is that because of overhiring from interest rates? I mean there’s like, so, so until we answer this question of like, what is like. And really the way to say what is the future of work is like, to put it in educational terms, how are you going to add value to the labor market? Like, David Otter has this like, example which I think is really important. It’s like, you know, the crosswalk coordinator versus the air traffic controller. And then, like, we pay the air traffic controller four times as much because any one of us could go, be a crosswalk coordinator like today, just give us a vest and a stop sign. I don’t, I assume you’re not moonlighting as an air traffic controller. I’m certainly not.

    It would take us, I think, I don’t know what the process is, but I think years to acquire the expertise. And so there is this barrier of expertise to do certain things. And what AI will do is lower the barriers to entry for certain types of expertise, things like writing, things like math. And so in those environments where AI is increasingly going to be automating certain types of expertise, then, well, for people to still get wages that are good or to be employed, they have to be adding something additional. And so the question of like, what are the humans adding? Again, we get to stuff like durable skills. We get to stuff like a human in the loop. But I think it’s much more nuanced than that. And the reason I know that is because there’s the MIT study.

    I think it was a survey, but let’s call it a study. I think they called it a study. So there’s a study from MIT that found that 95% of businesses, AI implementations failed, have not been successful. So really what we’re seeing is, yes, AI is blowing up, but for the most part, most organizations have not actually cracked the code on like, how to like, unlock productivity and like. And so I think that there’s actually quite a lot of business change management and organizational change that’s coming. And so actually kind of trying to hone in on what does that look like, I think is maybe the key, because that will take 10 years if you look at computers. Computers, like, could have revolutionized businesses long before, but they ended up getting adopted. I mean, it took like decades actually for, you know, spreadsheets and things like that to become ubiquitous.

    And like Excel is a great example of something. I was just talking to this, this expert from the mobile industry who was talking about, like, the interesting thing about spreadsheets was it didn’t just automate because there were people who literally would hand write, you know, ledgers before Excel. And so obviously that work got automated. But the other thing that spreadsheets did, where they created a new category of work, which is like the business analysts, because. Because before spreadsheets there was really the only way to get that information was to like, call somebody and sort of like compile it manually. And now you had a new way to look at information which actually unlocked a new sort of function that didn’t exist. And that meant, like, businesses now have teams of people that are like, doing layers of analysis that they didn’t realize that they could do before. And so

    Diane Tavenner: I wonder, what you’re saying is sparking two things for me. And again, we could talk probably all day, but we don’t have all day. So sadly, I think this might be bringing us to a close here for the moment. But I’m curious what both of you think on this because you brought up air traffic controllers. And in my new life and work, I’m very obsessed with careers and how people get into them and whatnot. I’ve done deep dives on air traffic controllers. And it’s, my macro point here is going to be.

    I do wonder if this moment of AI is also just extreme, exposing existing challenges and problems and bringing them to the forefront. Because let me be clear, training air traffic controllers in the US was a massive problem before AI came around, before any of this happened. It’s a really messed up system. It is so constrained. It’s not set up for success. Like, it’s just such a disaster and a mess and it’s such a critical role that we have. And it’s probably going to change with AI. Like, so you’ve just got all these things going on.

    And I’m wondering, Michael, from your perspective, is that what happens in these, you know, moments of disruption and is that all predictable and how do we get out of it? And then, Alex, you’re talking about. I was having a conversation this morning about this idea that all these companies no longer are hiring sort of those entry level analysts, or they’re hiring far fewer of them. And my wondering is no one can seem to answer this question yet. Great. Where’s your manager coming from? Because if you don’t employ any people at that level and they haven’t sort of learned the business and learned things, what do you think they’re just sitting on the sidelines for seven, eight years and then they’re ready to slide in there into, you know, the roles that you are keeping? And so are these just problems that already existed that are now just being exposed, you know, what’s going on? What do you all think?

    Job Market Trends and AI

    Alex Kotran: So, first of all, we really don’t know if the, like, I’m not convinced that the reason that there’s high unemployment among college grads is because of AI. I mean, I think there was overhiring because of interest, low interest rates. I think that companies are trying to free up cash flow to pay for the inference costs of these tools. And, and I think in general, like, you know, we’re, there’s going to be like, sort of like boom, bust cycles in terms of hiring in general. And we’ve been in a really good period of high employment for a long time. I think what, what is clear is if you talk to like earlier stage companies, you know, I was talking to a friend of mine at Cursor, which is like one of the big vibe coding companies, like blowing up, worth lots and lots of money. And I asked them about, like, oh, like I keep hearing about like, you know, companies aren’t hiring entry level engineers anymore because like, you’re better off having someone with experience.

    And he’s like, all of our engineers are in like their early 20s. Huh. OK, that’s interesting. Well, yeah, because actually it’s a lot faster and easier to train somebody who’s an AI native who learned software engineering while vibe coding. But he’s like, but we’re a small organization that’s like basically building out our structure as we go so we don’t have to like operate within sort of like the confines. I think there’s going to be this idea of like incumbent organizations. They have the existing hierarchy because ultimately you’re looking for people who are like really fast learners who can like learn new technology, who are adaptable and who are good at like doing hard stuff. If you’re a small organization, you’re probably better off just like hiring young people that like, you know, have those instincts.

    If you’re a large organization, what you might do is just maybe you’re laying off some of the really slow movers and then retaining and promoting the people that are already in place and have those characteristics. And then your point about like training the next generation, like law firms are thinking about this a lot because like you could, maybe you could automate all the entry level associates, but you do need a pipeline. But then you get to do you need middle managers? I mean like if the business models are less hierarchical because you just don’t need all those layers, then maybe you don’t worry so much about whether you need middle management and it’s more about do you need more. I think what companies are going to realize is they actually need more systems thinkers and technology native employees that are integrated into other verticals of knowledge work that outside of tech. So like, if you think about marketing and like business and customer success and you know, like non profit world fundraising and policy analysts, like all of these teams that generally have like people from the humanities. You know, I think companies are going to say, OK, how do we actually get people that like can do some vibe coding and have a little bit of like CS chops to build out some, you know, much more efficient and productive ways for these teams to operate. But like nobody knows. Nobody knows.

    I don’t know. Michael?

    Michael Horn: I love this point, Alex, where you’re ending and that like, and I like the humility frankly in a lot of the guests that we’ve had around. This is like the honesty that we’re all guessing a little bit at this future and we’re looking at different signals right. As we do. I think my quick take off this and I’ll try to give my version of it, I guess is you mentioned David Otter earlier at mit, Alex. Right. And part of his contention is that actually, right, it levels expertise between jobs that we’ve paid a lot for and jobs that we haven’t and more people like, as opposed to technology that is increasing inequality. This may be a technology that actually decreases inequality. And I guess it goes to my second thing, Diane, around what the question you asked and air traffic control training is a great example.

    But like, fundamentally, the organizations and processes we have in place have a very scarcity mindset. And I suspect they’re going to fight change and we’re going to need new disruptive organizations, similar to what Alex was just saying, that look very differently to come in. And it gets to a little bit of, I think what everyone says with technology, like the short term predictions are huge. They tend to disappoint on that. The long term change is bigger than we can imagine. And I guess I kind of wonder is the long term change what we. Alex, earlier on this season we had Reed Hastings and you know, he has a very abundant sort of society mindset where the robots plus AI plus probably quantum computing, like, are doing a lot of the things, or is it frankly sort of what you or I think Paul LeBlanc would argue, which is that a lot of these things that require trust and we want people like, yes, you can build an AI that does fundraising for you. But like, do I really trust both sides of that equation? I’d rather interact with someone.

    Right. There’s a lot of social capital that sort of greases these wheels ultimately in society. And I guess that’s a bit of the question. And Diane, I guess part of me thinks, you know, Carlota Perez, who’s written about technology revolutions, right. She says that there will be some very uncomfortable parts of this, right. And a bit of upheaval. Part of me keeps wondering if we can grease the wheels for new orgs to come in organically, can we avoid some of that upheaval because they’ll actually more naturally move to paying people for these jobs in a more organic way.

    And I, right now we have a, I’m not sure we have that mindset in place. That’s a bit of my question.

    Diane Tavenner: More questions than answers. More questions than answers. Really. This has been, wow, really provocative.

    Michael Horn: Yeah. So let’s, let’s, let’s leave. We could go on for a while. Let’s leave the conversation here for the moment. Alex, A segment we have on the show as we wrap up always is things we’re reading, watching, listening to either inside work or we try to be outside of work. You know, podcasts, TV shows, movies, books, whatever it might be. What’s on your night table or in your ear or in front of your eyes right now that you might share with us.

    Alex Kotran: I’m reading a book about salt. It’s called Salt.

    Michael Horn: This came out a few years ago. Yeah. Yeah. My wife read it.

    Alex Kotran: Yeah, I’m actually reading it for the second time. But it is, you know, it’s interesting because we. It’s something that’s, like, now you take for granted. But, you know, there’s a time when, you know, wars were fought. You know, it sort of spurred entire new sorts of technologies around. Like, the Erie Canal was basically, you know, like, salt was a big component of, you know, why we even built the Erie Canal. It’s. It’s actually nicknamed a ditch that salt built, you know, spurring new mining techniques.

    Technology’s Interconnected Conversation

    Alex Kotran: And, you know, I just find it fascinating that, like, you know, there are these, like, technology is so interconnected not to bring it back. I know this is supposed to be outside, but all I read, I only read nonfiction, so it’s going to be connected in some way. I just, like, fascinated by, like, you know, there are these sort of, like, layers behind the scenes that we sometimes take for granted that, you know, can actually be, like, you know, quietly, you know, monumental. I think what’s cool about this moment with technology is it’s like everybody’s a part of this conversation. Like, before, it was, like, much more cloistered. And so I think that’s just, like, good. Even though, yes, there’s a lot of noise and hype and, you know, snake oil and all that stuff, but I think in general, like, we are better off by, like, having folks like you, like, asking folk, asking people for, like, you know, like, driving conversation about this and not just leaving it to a small group of experts to dictate.

    Diane Tavenner: So I think this is cheating, but I’ve done this one before. But I’m gonna cheat anyway because, as you know, Michael, because you hear me talk about it a lot, the. The one news source I religiously read is called Tangle News. It’s a newsletter now and a podcast. It’s grown like crazy since I first started listening. I love it. It’s like a startup.

    It started, I think when I started reading, it was like, under 50,000 subscribers or something. Now up half a million. Executive editor, Isaac Saul, who I’m going to say this about a news person I trust, which I think is just a miracle. And I’m bringing it up this week because he wrote a piece last Friday that, honestly, I had to break over a couple days because it was really brutal to read. That’s just a very honest accounting of where we are in this moment. The best piece I’ve heard, I’ve read or, or heard about it. And then on Monday, he did another piece where, you know, they do what’s the left saying? What’s the right saying? What’s his take? You know, what are dissenting opinions? I just love the format. I love what they’re doing.

    I was getting ready to write them a thank you note slash love letter, which I do periodically. And I thought I’d just say it on here.

    Michael Horn: I was gonna say now you can just excerpt this and send them a video clip.

    Diane Tavenner: So I hope, I hope people will check it out. I love, love, love the work they’re doing, and I think you will too.

    Michael Horn: I’m gonna go historical fiction. Diane, I’m like, surprising you multiple weeks in a row here, I think. Right? Yeah. Because, Alex, I’m like you. I’m normally just nonfiction all the time, but I don’t know. Tracy said you have to read this book, Brother’s Keeper by Julie Lee.

    It’s based on. It’s historical fiction based on a. About a family’s migration from North Korea to South Korea during the Korean War. It is a tear jerker. I was crying like, literally sobbing as I was reading last night. And Tracy was like, you OK? And I was like, I think I won’t get through the book. But I did, and it’s fantastic.

    So we’ll leave it there. But, Alex, huge thanks. You spurred a great conversation. Looking forward to picking up a bunch of these strands as we continue. And for all you listening again, keep the comments, questions coming. It’s spurring us to think through different aspects of this and invite other guests who have good answers or at least the right questions and signals we ought to be paying attention to. So we’ll see you next time on Class Disrupted.


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