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  • If we are serious about improving student outcomes, we can’t treat teacher retention as an afterthought

    If we are serious about improving student outcomes, we can’t treat teacher retention as an afterthought

    In the race to help students recover from pandemic-related learning loss, education leaders have overlooked one of the most powerful tools already at their disposal: experienced teachers.

    For decades, a myth has persisted in education policy circles that after their first few years on the job, teachers stop improving. This belief has undercut efforts to retain seasoned educators, with many policymakers and administrators treating veteran teachers as replaceable cogs rather than irreplaceable assets.

    But that myth doesn’t hold up. The evidence tells a different story: Teachers don’t hit a plateau after year five. While their growth may slow, it doesn’t stop. In the right environments — with collaborative colleagues, supportive administrators and stable classroom assignments — teachers can keep getting better well into their second decade in the classroom.

    This insight couldn’t come at a more critical time. As schools work to accelerate post-pandemic learning recovery, especially for the most vulnerable students, they need all the instructional expertise they can muster.

    That means not just recruiting new teachers but keeping their best educators in the classroom and giving them the support they need to thrive.

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

    In a new review of 23 longitudinal studies conducted by the Learning Policy Institute and published by the Thomas B. Fordham Institute, all but one of the studies showed that teachers generally improve significantly during their first five years. The research review also found continued, albeit slower, improvement well into years 6 through 15; several of the studies found improvement into later years of teaching, though at a diminished pace.

    These gains translate into measurable benefits for students: higher test scores, fewer disciplinary issues, reduced absenteeism and increased postsecondary attainment. In North Carolina, for example, students with highly experienced English teachers learned more and were substantially less likely to skip school and more likely to enjoy reading. These effects were strongest for students who were most at risk of falling behind.

    While experience helps all teachers improve, we’re currently failing to build that experience where it’s needed most. Schools serving large populations of low-income Black and Hispanic students are far more likely to be staffed primarily by early career teachers.

    And unfortunately, they’re also more likely to see those teachers leave after just a few years. This churn makes it nearly impossible to build a stable, experienced workforce in high-need schools.

    It also robs novice teachers of the veteran mentors who could help them get better faster and robs students of the opportunity to learn from seasoned educators who have refined their craft over time.

    To fix this, we need to address both sides of the equation: helping teachers improve and keeping them in the classrooms that need them most.

    Research points to several conditions that support continued teacher growth. Beginning teachers are more likely to stay and improve if they have had high-quality preparation and mentoring. Teaching is not a solo sport. Educators who work alongside more experienced peers improve faster, especially in the early years.

    Teachers also improve more when they’re able to teach the same grade level or subject year after year. Unfortunately, those in under-resourced schools are more likely to be shuffled around, undermining their ability to build expertise.

    Perhaps most importantly, schools that have strong leadership and which foster time for collaboration and a culture of professional trust see greater gains in teacher retention over time.

    Teachers who feel supported by their administrators, who collaborate with a team that shares their mission and who aren’t constantly switching subjects or grade levels are far more likely to stay in the profession.

    Pay matters too, especially in high-need schools where working conditions are toughest. But incentives alone aren’t enough. Short-term bonuses can attract teachers, but they won’t keep them if the work environment drives them away.

    Related: One state radically boosted new teacher pay – and upset a lot of teachers

    If we’re serious about improving student outcomes, especially in the wake of the pandemic, we have to stop treating teacher retention as an afterthought. That means retooling our policies to reflect what the research now clearly shows: experience matters, and it can be cultivated.

    Policymakers should invest in high-quality teacher preparation and mentoring programs, particularly in high-need schools. They should create conditions that promote teacher stability and collaboration, such as protected planning time and consistent teaching assignments.

    Principals must be trained not just as managers, but as instructional leaders capable of building strong school cultures. And state and district leaders must consider meaningful financial incentives and other supports to retain experienced teachers in the classrooms that need them most.

    With the right support, teachers can keep getting better. In this moment of learning recovery, a key to success is keeping teachers in schools and consciously supporting their growing effectiveness.

    Linda Darling-Hammond is founding president and chief knowledge officer at the Learning Policy Institute. Michael J. Petrilli is president of the Thomas B. Fordham Institute, a visiting fellow at the Hoover Institution and an executive editor of Education Next.

    Contact the opinion editor at [email protected].

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

    The Hechinger Report provides in-depth, fact-based, unbiased reporting on education that is free to all readers. But that doesn’t mean it’s free to produce. Our work keeps educators and the public informed about pressing issues at schools and on campuses throughout the country. We tell the whole story, even when the details are inconvenient. Help us keep doing that.

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  • Building a Thriving Classroom Community – “Bond & Beyond” – Faculty Focus

    Building a Thriving Classroom Community – “Bond & Beyond” – Faculty Focus

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  • Building a Thriving Classroom Community – “Bond & Beyond” – Faculty Focus

    Building a Thriving Classroom Community – “Bond & Beyond” – Faculty Focus

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  • QUT ‘anti-racism’ debate speakers cleared – Campus Review

    QUT ‘anti-racism’ debate speakers cleared – Campus Review

    A Queensland university and controversial speakers involved in an ‘anti-racism’ symposium earlier this year have been cleared of any misconduct, an independent review has found.

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  • UQ vaccine bought in billion-dollar deal – Campus Review

    UQ vaccine bought in billion-dollar deal – Campus Review

    Breakthrough vaccine technology invented by the University of Queensland is at the centre of a landmark deal worth up to $1.6bn struck with global pharmaceutical giant Sanofi.

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  • Living in an expertise economy – Campus Review

    Living in an expertise economy – Campus Review

    Corporate learning expert and former chief strategy officer at Southern New Hampshire University Kelly Palmer shares the ideas from her new book, The Expertise Economy: How the Smartest Companies Use Learning to Engage, Compete, and Succeed in this episode.

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  • AUD $50bn net gain from students with minimal rent price impact

    AUD $50bn net gain from students with minimal rent price impact

    International students are not responsible for sky-high rental price hikes, according to the latest analysis produced by Australia’s central bank, the Reserve Bank of Australia.

    In its latest bulletin assessing the role international students play in Australia’s economy, it estimated a AUS$50bn net gain from students and underlined their value as employees too.

    Spending by international students was also an important contributor to growth in consumer demand in Australia following the pandemic, it declared.

    “In periods of strong inflows of students, such as just after borders reopened after the pandemic, this likely had an important effect on aggregate demand in the economy.”

    And the report pointed out that international students constitute the second largest group of temporary visa holders with work rights in Australia after New Zealand citizens.

    “A greater share of international students work in accommodation and food, as well as retail, compared with the share of the total labour force,” detailed report authors.

    “Further, an increasing share of students are now working in health care, consistent with strong labour demand in this sector.”

    The report noted this contribution was important in helping businesses in these sectors facing labour shortages in the tight labour market that emerged post-pandemic.

    The timing of the report is useful, as new ESOS legislation is considered and the government is facing calls from the sector to stop stifling international student demand – with the latest calls relating to the new visa application fee which is killing demand from short-term students.

    When it comes to the political hot potato of international student populations squeezing out domestic renters or contributing to accommodation price surges, RBA was dismissive of that thesis.

    The rise in international student numbers is likely to have accounted for only a small share of the rise in rents since the onset of the pandemic
    Reserve Bank of Australia

    Models of the housing market used by the RBA suggest that a 50,000 increase in population would raise private rents by around 0.5 per cent compared with a baseline projection. The marginal effect of an additional renter may be greater in periods where the rental market is tight and vacancy rates are low, such as occurred post-pandemic.

    “Nonetheless, the rise in international student numbers is likely to have accounted for only a small share of the rise in rents since the onset of the pandemic, with much of the rise in advertised rents occurring before borders were reopened.”

    One area where higher international student numbers have generated a supply response has been in purpose-built student accommodation, noted the report, with rapid growth in building approvals for such projects in recent years.

    Note the gov plan to expand cap for insttutions investing in PBSA.

    Another interesting fact shared was that International students make up around one-third of Australia’s permanent resident intake –  around 30 per cent of international students went on to apply for temporary graduate visas in the five years to 2022, said the report citing 2022 data.

    There is less expected flow into temporaray labour market now – “this is because the recent tightening in visa policy has targeted groups of students who were more likely to be seeking to work” explained RBA.

    “That is, those international students who do receive visas going forward are less likely to be focused on employment opportunities in Australia on average,” said the report, citing Andrew Norton.

    In sum, “rapid growth in the international student stock post-pandemic likely contributed to some of the upward pressure on inflation from 2022 to early 2023, especially as arriving students frontloaded their spending as they set up in Australia and took time to join the labour market. However, the increase in international students was just one of many other forces at play in this time that drove demand above supply in the economy, and hence higher inflation. For instance, supply-side factors were the biggest driver of the increase in inflation in 2022 and 2023 (RBA 2023; Beckers, Hambur and Williams 2023) while strong domestic demand arising from supportive fiscal and monetary policy also played an important role.”

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  • Universities need to reckon with how AI is being used in professional practice

    Universities need to reckon with how AI is being used in professional practice

    One of the significant themes in higher education over the last couple of decades has been employability – preparing students for the world of work into which they will be released on graduation.

    And one of the key contemporary issues for the sector is the attempt to come to grips with the changes to education in an AI-(dis)empowered world.

    The next focus, I would argue, will involve a combination of the two – are universities (and regulators) ready to prepare students for the AI-equipped work where they will be working?

    The robotics of law

    Large, international law firms have been using AI alongside humans for some time, and there are examples of its use for the drafting of non-disclosure agreements and contracts, for example.

    In April 2025, the Solicitors Regulation Authority authorised Garfield Law, a small firm specialising in small-claims debt recovery. This was remarkable only in that Garfield Law is the first law firm in the world to deliver services entirely through artificial intelligence.

    Though small and specialised, the approval of Garfield Law was a significant milestone – and a moment of reckoning – for both the legal professional and legal education. If a law firm can be a law firm without humans, what is the future for legal education?

    Indeed, I would argue that the HE sector as a whole is largely unprepared for a near-future in which the efficient application of professional knowledge is no longer the sole purview of humans.

    Professional subjects such as law, medicine, engineering and accountancy have tended to think of themselves as relatively “technology-proof” – where technology was broadly regarded as useful, rather than a usurper. Master of the Rolls Richard Vos said in March that AI tools

    may be scary for lawyers, but they will not actually replace them, in my view at least… Persuading people to accept legal advice is a peculiarly human activity.

    The success or otherwise of Garfield Law will show how the public react, and whether Vos is correct. This vision of these subjects as high-skill, human-centric domains needing empathy, judgement, ethics and reasoning is not the bastion it once was.

    In the same speech, Vos also said that, in terms of using AI in dispute resolution, “I remember, even a year ago, I was frightened even to suggest such things, but now they are commonplace ideas”. Such is the pace at which AI is developing.

    Generative AI tools can, and are, being used in contract drafting, judgement summaries, case law identification, medical scanning, operations, market analysis, and a raft of other activities. Garfield Law represents a world view where routine, and once billable, tasks performed by trainees and paralegals will most likely be automated. AI is challenging the traditional boundaries of what it means to be a professional and, in concert with this, challenging conceptions of what it is to teach, assess and accredit future professionals.

    Feeling absorbed

    Across the HE sector, the first reaction to the emergence of generative AI was largely (and predictably) defensive. Dire warnings to students (and colleagues) about “cheating” and using generative AI inappropriately were followed by hastily-constructed policies and guidelines, and the unironic and ineffective deployment of AI-powered AI detectors.

    The hole in the dyke duly plugged, the sector then set about wondering what to do next about this new threat. “Assessments” came the cry, “we must make them AI-proof. Back to the exam hall!”

    Notwithstanding my personal pedagogic aversion to closed-book, memory-recall examinations, such a move was only ever going to be a stopgap. There is a deeper pedagogic issue in learning and teaching: we focus on students’ absorption, recall and application of information – which, to be frank, is instantly available via AI. Admittedly, it has been instantly available since the arrival of the Internet, but we’ve largely been pretending it hasn’t for three decades.

    A significant amount of traditional legal education focuses on black-letter law, case law, analysis and doctrinal reasoning. There are AI tools which can already do this and provide “reasonably accurate legal advice” (Vos again), so the question arises as to what is our end goal in preparing students? The answer, surely, is skills – critical judgement, contextual understanding, creative problem solving and ethical reasoning – areas where (for the moment, at least) AI still struggles.

    Fit for purpose

    And yet, and yet. In professional courses like law, we still very often design courses around subject knowledge, and often try to “embed” the skills elements afterwards. We too often resort to tried and tested assessments which reward memory (closed-book exams), formulaic answers (problem questions) and performance under time pressure (time constrained assessments). These are the very areas in which AI performs well, and increasingly is able to match, or out-perform humans.

    At the heart of educating students to enter professional jobs there is an inherent conflict. On the one hand, we are preparing students for careers which either do not yet exist, or may be fundamentally changed – or displaced – by AI. On the other, the regulatory bodies are often still locked into twentieth century assumptions about demonstrating competence.

    Take the Solicitors Qualifying Examination (SQE), for example. Relatively recently introduced, the SQE was intended to bring consistency and accessibility into the legal profession. The assessment is nonetheless still based on multiple choice questions and unseen problem questions – areas where AI can outperform many students. There are already tools out there to help SQE student practice (Chat SQE, Kinnu Law), though no AI tool has yet completed the SQE itself. But in the USA, the American Uniform Bar Exam was passed by GPT4 in 2023, outperforming some human candidates.

    If a chatbot can ace your professional qualifying exam, is that exam fit for purpose? In other disciplines, the same question arises. Should medical students be assessed on their recall of rare diseases? Should business students be tested on their SWOT analyses? Should accounting students analyse corporate accounts? Should engineers calculate stress tolerances manually? All of these things can be completed by AI.

    Moonshots

    Regulatory bodies, universities and employers need to come together more than ever to seriously engage with what AI competency might look like – both in the workplace and the lecture theatre. Taking the approach of some regulators and insisting on in-person exams to prepare students for an industry entirely lacking in exams probably is not it. What does it mean to be an ethical, educated and adaptable professional in the age of AI?

    The HE sector urgently needs to move beyond discussions about whether or not students should be allowed to use AI. It is here, it is getting more powerful, and it is never leaving. Instead, we need to focus on how we assess in a world where AI is always on tap. If we cannot tell the difference between AI-generated work and student-generated work (and increasingly we cannot) then we need to shift our focus towards the process of learning rather than the outputs. Many institutions have made strides in this direction, using reflective journals, project-based learning and assessments which reward students for their ability to question, think, explain and justify their answers.

    This is likely to mean increased emphasis on live assessments – advocacy, negotiations, client interviews or real-world clinical experience. In other disciplines too, simulations, inter- and multi-disciplinary challenges, or industry-related authentic assessments. These are nothing revolutionary, they are pedagogically sound and all have been successfully implemented. They do, however, demand more of us as academics. More time, more support, more creativity. Scaling up from smaller modules to large cohorts is not an easy feat. It is much easier to keep doubling-down on what we already do, and hiding behind regulatory frameworks. However, we need to do these things (to quote JFK)

    not because they are easy, but because they are hard, because that goal will serve to organize and measure the best of our energies and skills, because that challenge is one that we are willing to accept, one we are unwilling to postpone.

    In law schools, how many of us teach students how to use legal technology, how to understand algorithmic biases, or how to critically assess AI-generated legal advice? How many business schools teach students how to work alongside AI? How many medical schools give students the opportunity to learn how to critically interpret AI-generated diagnostics? The concept of “digital professionalism” – the ability to effectively and ethically use AI in a professional setting – is becoming a core graduate-level skill.

    If universities fail to take the lead on this, then private providers will be eager, and quick, to fill the void. We already have short courses, boot camps, and employer-led schemes which offer industry-tailored AI literacy programmes – and if universities start to look outdated and slow to adapt, students will vote with their feet.

    Invention and reinvention

    However, AI is not necessarily the enemy. Like all technological advances it is essentially nothing more than a tool. As with all tools – the stone axe, the printing press, the internet – it brings with it threats to some and opportunities for others. We have identified some of the threats but also the opportunities that (with proper use), AI can bring – enhanced learning, deeper engagement, and democratisation of access to knowledge. Like the printing press, the real threat faced by HE is not the tool, but a failure to adapt to it. Nonetheless, a surprising number of academics are dusting off their metaphorical sabots to try and stop the development of AI.

    We should be working with the relevant sector and regulator and asking ourselves how we can adapt our courses and use AI to support, rather than substitute, genuine learning. We have an opportunity to teach students how to move away from being consumers of AI outputs, and how to become critical users, questioners and collaborators. We need to stop being reactive to AI – after all, it is developing faster than we can ever do.

    Instead, we need to move towards reinvention. This could mean: embedding AI literacy in all disciplines; refocusing assessments to require more creative, empathetic, adaptable and ethical skills; preparing students and staff to work alongside AI, not to fear it; and closer collaboration with professional regulators.

    AI is being used in many professions, and the use will inevitably grow significantly over the next few years. Educators, regulators and employers need to work even more closely together to prepare students for this new world. Garfield Law is (currently) a one-off, and while it might be tempting to dismiss the development as tokenistic gimmickry, it is more than that.

    Professional courses are standing on the top of a diving board. We can choose obsolescence and climb back down, clinging to outdated practices and condemn ourselves to irrelevance. Or, we can choose opportunity and dive in to a more dynamic, responsive and human vision of professional learning.

    We just have to be brave enough to take the plunge.

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  • What should the higher education sector do about AI fatigue?

    What should the higher education sector do about AI fatigue?

    Raise the topic of AI in education for discussion these days and you can feel the collective groan in the room.

    Sometimes I even hear it. We’re tired, I get it. Many students are too. But if we don’t keep working creatively to address the disruption to education posed by AI – if we just wait and see how it plays out – it will be too late.

    AI fatigue is many things

    There are a few factors at play, from an AI literacy divide, to simply talking past each other.

    AI literacy is nearly unmanageable. The complexity of AI in education, exacerbated by the pace of technological change, makes AI “literacy” very difficult to define, let alone attain. Educators represent a wide range of experience levels and conceptual frames, as well as differing opinions on the power, quality, opportunity, and risk of generative AI.

    One person will see AI as a radical first step in an intelligence revolution; the next will dismiss it as “mostly rubbish” and minimise the value discussing it at all. And, as far as I have found, there is no leading definition of AI literacy to date. Some people don’t even like the term literacy.

    Our different conceptual frames compete with each other. Many disciplines and conceptual orientations are trying to talk together, each with their own assumptions and incentives. In any given space, we have the collision of expert with novice, entrepreneur with critic, sceptic with optimist, reductionist with holist… and the list goes on.

    We tend to silo and specialise. Because it is difficult to become comprehensively literate in generative AI (and its related issues), many adopt a narrow focus and stick with that: assessment design, academic integrity, authorship, cognitive offloading, energy consumption, bias, labour ethics, and others. Meetings take on the character of debates. At the very least, discussions of AI are time-consuming, as each focus seems to need airing every day.

    We feel grief for what we may be losing: human authorship, agency, status, and a whole range of normative relational behaviours. A colleague recently told me how sad she feels marking student work. Authorship, for example, is losing coherence as a category or shared value, which can be surreal and dispiriting for both writers and readers. AI’s disruption brings a deeply challenging emotional experience that’s rarely discussed.

    We are under-resourced. Institutions have been slow to roll out policy, form working groups, provide training, or fund staff time to research, prepare, plan, and design responses. It’s a daunting task to just keep up with, let alone get ahead of, Silicon Valley. Unfortunately, the burden is largely borne by individuals.

    The AI elephant in the room

    Much of the sector suffers from the wishful thinking that AI is “mostly rubbish”, not likely to change things much, or simply an annoyance. Many educators haven’t thought through how AI technologies may lead our societies and our education systems to change radically and quickly, and that these changes may impact the psychology of learning and teaching, not to mention the entire infrastructure of education. We talk past each other.

    Silicon Valley is openly pursuing artificial general intelligence (AGI), or something like that. Imagine a ChatGPT that can do your job, my job, and a big piece of the knowledge-work jobs recent graduates may hope to enter. Some insiders think this could arrive by 2027.

    A few weeks ago, Dario Amodai, CEO of AI company Anthropic, wrote his prediction that 50 per cent of entry-level office jobs could vanish within the next couple of years, and that unemployment overall could hit 20 per cent. This could be mostly hype or confirmation bias among the tech elite. But IBM, Klarna, and Duolingo have already cited AI-linked efficiencies in recent layoffs.

    Whether these changes take two years, or five, or even ten, it’s on the radar. So, let’s pause and imagine it. What happens to a generation of young people who perceive increasing job scarcity, and options and social purpose?

    Set aside, for now, what this means for cities, mental health, or the social fabric. What does it mean for higher education – especially if a university degree no longer holds the value it once promised? How should HE respond?

    Responding humanely

    I propose we respond with compassion, humanity… and something like a plan. What does this look like? Let me suggest a few possibilities.

    The sector works together. Imagine this: a consortium of institutions gathers together a resource base and discussion space (not social media) for AI in education. It respects diversity of positions and conceptual frames but also aims for a coherent and pragmatic working ethos that helps institutions and individuals make decisions. It drafts a change management plan for the sector, embracing adaptive management to create frameworks to support institutions to respond quickly, intelligently, flexibly, and humanely to the instability. It won’t resolve all the mess into a coherent solution, but it could provide a more stable framework for change. And lift the burden on thousands of us who feel we are reinventing the wheel every day.

    Institutions take action. Leading institutions embrace big discussions around the future of society, work, and education. They show a staunch willingness to face the risks and opportunities ahead, they devote resources to the project, and they take actions that support both staff and students to navigate change thoughtfully.

    Individuals and small groups are empowered to respond creatively. Supported by the sector and their HEIs, they collaborate to keep each other motivated, check each other on the hype, and find creative new avenues for teaching and learning. We solve problems for today while holding space for the messy discussions, speculate on future developments, and experiment with education in a changing world.

    So sector leaders, please help us find some degree of convergence or coherence; institutions, please take action to resource and support your staff and students; and individuals, let’s work together to do something good.

    With leadership, action, and creative collaboration, we may just find the time and energy to build new language and vision for the strange landscape we have entered, to experiment safely with new models of knowledge creation and authorship, and to discover new capacities for self-knowledge and human value.

    So groan, yes – I groan with you. And breathe – I’ll go along with that too. And then, let’s see what we can build.

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  • Careers services can help students avoid making decisions based on AI fears

    Careers services can help students avoid making decisions based on AI fears

    How students use AI tools to improve their chances of landing a job has been central to the debate around AI and career advice and guidance. But there has been little discussion about AI’s impact on students’ decision making about which jobs and sectors they might enter.

    Jisc has recently published two studies that shine light on this area. Prospects at Jisc’s Early Careers Survey is an annual report that charts the career aspirations and experiences of more than 4,000 students and graduates over the previous 12 months. For the first time, the survey’s dominant theme was the normalisation of the use of AI tools and the influence that discourse around AI is having on career decision making. And the impact of AI on employability was also a major concern of Jisc’s Student Perceptions of AI Report 2025, based on in-depth discussions with over 170 students across FE and HE.

    Nerves jangling

    The rapid advancements in AI raise concerns about its long-term impact, the jobs it might affect, and the skills needed to compete in a jobs market shaped by AI. These uncertainties can leave students and graduates feeling anxious and unsure about their future career prospects.

    Important career decisions are already being made based on perceptions of how AI may change work. The Early Careers Survey found that one in ten students had already changed their career path because of AI.

    Plans were mainly altered because students feared that their chosen career was at risk of automation, anticipating fewer roles in certain areas and some jobs becoming phased out entirely. Areas such as coding, graphic design, legal, data science, film and art were frequently mentioned, with creative jobs seen as more likely to become obsolete.

    However, it is important not to carried away on a wave of pessimism. Respondents were also pivoting to future-proof their careers. Many students see huge potential in AI, opting for careers that make use of the new technology or those that AI has helped create.

    But whether students see AI as an opportunity or a threat, the role of university careers and employability teams is the same in both cases. How do we support students in making informed decisions that are right for them?

    From static to electricity

    In today’s AI-driven landscape, careers services must evolve to meet a new kind of uncertainty. Unlike previous transitions, students now face automation anxiety, career paralysis, and fears of job displacement. This demands a shift away from static, one-size-fits-all advice toward more personalised, future-focused guidance.

    What’s different is the speed and complexity of change. Students are not only reacting to perceived risks but also actively exploring AI-enhanced roles. Careers practitioners should respond by embedding AI literacy, encouraging critical evaluation of AI-generated advice, and collaborating with employers to help students understand the evolving world of work.

    Equity must remain central. Not all students have equal access to digital tools or confidence in using them. Guidance must be inclusive, accessible, and responsive to diverse needs and aspirations.

    Calls to action should involve supporting students in developing adaptability, digital fluency, and human-centred skills like creativity and communication. Promote exploration over avoidance, and values-based decision-making over fear, helping students align career choices with what matters most to them.

    Ultimately, careers professionals are not here to predict the future, but to empower all students and early career professionals to shape it with confidence, curiosity, and resilience.

    On the balance beam

    This isn’t the first time that university employability teams have had to support students through change, anxiety, uncertainty or even decision paralysis when it comes to career planning, but the driver is certainly new. Through this uncertainty and transition, students and graduates need guidance from everyone who supports them, in education and the workplace.

    Collaborating with industry leaders and employers is key to ensuring students understand the AI-enhanced labour market, the way work is changing and that relevant skills are developed. Embedding AI literacy in the curriculum helps students develop familiarity and understand the opportunities as well as limitations. Jisc has launched an AI Literacy Curriculum for Teaching and Learning Staff to support this process.

    And promoting a balanced approach to career research and planning is important. The Early Careers Survey found almost a fifth of respondents are using generative AI tools like ChatGPT and Microsoft Copilot as a source of careers advice, and the majority (84 per cent) found them helpful.

    While careers and employability staff welcome the greater reach and impact AI enables, particularly in challenging times for the HE sector, colleagues at an AGCAS event were clear to emphasise the continued necessity for human connection, describing AI as “augmenting our service, not replacing it.”

    We need to ensure that students understand how to use AI tools effectively, spot when the information provided is outdated or incorrect, and combine them with other resources to ensure they get a balanced and fully rounded picture.

    Face-to-face interaction – with educators, employers and careers professionals – provides context and personalised feedback and discussion. A focus on developing essential human skills such as creativity, critical thinking and communication remains central to learning. After all, AI doesn’t just stand for artificial intelligence. It also means authentic interaction, the foundation upon which the employability experience is built.

    Guiding students through AI-driven change requires balanced, informed career planning. Careers services should embed AI literacy, collaborate with employers, and increase face-to-face support that builds human skills like creativity and communication. Less emphasis should be placed on one-size-fits-all advice and static labour market forecasting. Instead, the focus should be on active, student-centred approaches. Authentic interaction remains key to helping students navigate uncertainty with confidence and clarity.

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