Tag: Revolution

  • From curriculum to career: why universities must lead the education–skills revolution

    From curriculum to career: why universities must lead the education–skills revolution

    This blog was kindly authored by Dr. Ismini Vasileiou, Associate Professor at De Montfort University. You can find HEPI’s other blogs on the Curriculum and Assessment Review here and here.

    When the Department for Education published its Curriculum and Assessment Review, billed as a Curriculum for Life and Work on 4 November 2025, it signalled more than a curriculum reform – it marked a national conversation about what education is for. For the first time, the school curriculum will explicitly combine knowledge, digital capability, employability, and citizenship – preparing young people not just for exams, but for participation in a complex, data-driven, and interconnected world. Crucially, this is not about replacing education with skills. It’s about redefining education as the process through which skills for life and work are formed. The message is clear: education and skills are inseparable, and the system must now be designed as one continuous journey.

    A moment of alignment

    This announcement completes the trajectory begun by the Post-16 Education and Skills White Paper (October 2025). Together, these two policy pillars – one focused on schools, the other on tertiary education – outline a vision of coherence across the learning lifecycle. The Post-16 paper’s introduction of V-Levels, simplification of Level 3 qualifications, and expansion of Higher Technical Qualifications now align with the Curriculum for Life and Work, which embeds the early foundations of employability and digital literacy in every pupil’s experience. For the first time in decades, England’s education policy points in a single direction: towards a joined-up system of education that builds character, competence, and confidence. But the success of this vision depends on one missing piece – universities, which sit at the intersection of learning, innovation, and the workforce.

    Education, not training

    Much of the public debate risks falling into false dichotomies: academic versus vocational, education versus skills. The government’s language – “life and work” – recognises that these are not opposites but continuums. Education remains the intellectual and moral foundation of a healthy democracy. But when delivered holistically, it also nurtures adaptability, creativity, and applied understanding – the very capacities employers now seek. Universities have a critical role in championing this integrated view. Their purpose is not to become training providers but to model what it means for education to produce confident, employable citizens who can learn, unlearn, and relearn throughout their lives.

    Lessons from cyber: integration in action

    This holistic approach already exists in one part of the education system: the cyber sector.

    The Cyber Workforce of the Future white paper (2025) called for a unified skills taxonomy, a shared definition of competence across education and industry, and seamless progression from schools through FE and HE into work. That model aligns almost exactly with what the new curriculum and the post-16 reforms now propose nationally: an ecosystem where education, employability, and innovation are interdependent rather than sequential. In cyber, this has already meant cross-sector curriculum design, embedded work experience, and a culture that treats technical and academic learning as equally rigorous. The next step is to scale that success across all disciplines – from green technologies to healthcare, design, and AI.

    Universities at the centre of reform

    Universities can make or break this national vision. Their position in the education–skills continuum gives them both responsibility and leverage. To succeed, they must:

    1. Anticipate the learners of 2028: The first cohort to study under the new curriculum will arrive at university at the start of the next decade. Institutions must adapt admissions, pedagogy, and assessment to students whose schooling will emphasise applied learning, digital literacy, and teamwork.
    2. Build local and regional partnerships: Collaborating with FE colleges, Skills England, and employers will be essential to map seamless pathways from school to post-16 and higher education.
    3. Integrate employability into education: Employability should not be treated as a bolt-on service but as an educational principle – part of how critical thinking, problem-solving, and collaboration are taught across disciplines.
    4. Champion digital confidence: With data, AI, and cyber understanding now fundamental to the new curriculum, universities must ensure every graduate – not only those in STEM – leaves equipped to operate in a digital society.
    5. Measure outcomes holistically: Success should not be judged solely by employment rates but by how graduates contribute to innovation, community resilience, and lifelong learning.

    Risks and responsibilities

    Reform at this scale brings challenges. Without alignment across sectors, the new curriculum could risk being a policy of aspiration rather than transformation. Schools may teach for adaptability, only for universities to assess for recall. Equally, the pressure to define “skills for work” must not narrow education’s scope. The aim is not to produce workers but well-educated citizens who can shape the future of work. Universities can protect that balance – ensuring that the education–skills revolution deepens, rather than dilutes, the purpose of learning.

    From reform to renewal

    The Curriculum for Life and Work represents a rebalancing of the national education story: knowledge still matters, but so do capability, confidence, and contribution. This aligns perfectly with the model already tested through the Cyber Workforce of the Future initiative – where education, employability, and innovation are treated as parts of one system. That approach, proven in a fast-moving digital sector, now provides a template for reform across the entire economy. For higher education, the challenge – and the opportunity – is to lead. By embedding employability as a dimension of education, not its substitute, universities can turn these policy reforms into a sustainable framework for growth, equity, and lifelong learning. The UK has a rare moment of alignment: curriculum reform, post-16 reform, and national skills strategy all pointing in the same direction. If higher education steps forward now, this could become not just another skills agenda, but a true education revolution for life and work.

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  • The post-16 pivot: why higher education needs to lean into the skills revolution

    The post-16 pivot: why higher education needs to lean into the skills revolution

    This blog was kindly authored by Dr. Ismini Vasileiou, Associate Professor at De Montfort University.

    The government’s new Post-16 Education and Skills White Paper reframes how the UK prepares people for work, learning, and life. It promises a simpler, more coherent system built around quality, parity of esteem, and progression – introducing new V-Levels, reforming Level 3 and below qualifications, and setting out clearer routes into higher education and skilled employment.

    Within it there is an unmistakable message for universities: higher education is no longer a separate tier but a partner in a joined-up skills ecosystem.

    This direction of travel strongly echoes the recommendations of the Cyber Workforce of the Future white paper, which called for a unified national skills taxonomy, stronger coordination between education and employers, and consistent frameworks for developing technical talent. The government’s post-16 reforms, though broader in scope, now seeks to achieve at system level what the cyber sector has already begun to pilot.

    Reimagining pathways: from fragmentation to flow

    At the heart of the White Paper lies the ambition to create “a seamless system where every learner can progress, without duplication or dead ends.” The proposed V-Levels for 16-19-year-olds aim to sit alongside A-Levels, replacing hundreds of overlapping technical qualifications and creating a nationally recognised route into both higher technical and academic study.

    Reforms to Level 2 and entry-level qualifications will introduce new “Foundation Programmes” that build essential skills and prepare learners for work or further study. Alongside these, stepping-stone qualifications in English and Mathematics will replace automatic GCSE resits, acknowledging that linear repetition has failed to deliver progress for many young people.

    The emphasis on simplified, stackable routes reflects the very principles behind the Cyber Workforce of the Future model, which proposed interoperable learning pathways connecting schools, further education, higher education, and industry within a single skills continuum. What began as a sector-specific call for alignment in cyber is now being written into national policy.

    Higher education’s new context

    The White Paper links post-16 reform directly to the Industrial Strategy and to Skills England’s mission to align learning with labour-market demand. For universities, several themes stand out:

    • Progression and parity: Higher education is expected to work together with further education and employers to ensure that learners completing V-Levels and higher technical qualifications can progress seamlessly into Level 4, 5, and 6 provision.
    • Higher Technical Qualifications (HTQs): The expansion of HTQs in growth areas such as AI, cyber security, and green technology positions universities as key co-developers and deliverers of technical education.
    • Quality and accountability: The Office for Students will have powers to limit recruitment to poor-quality courses and tie tuition-fee flexibility to demonstrable outcomes, reinforcing the need for robust progression and employability data.
    • Lifelong learning and modularity: The commitment to the Lifelong Learning Entitlement demands interoperability of credits across further education and higher education – another concept long championed in the cyber-skills ecosystem.

    Taken together, these reforms require universities to move beyond disciplinary silos and become brokers of opportunity – enabling flexible, lifelong learning rather than simply delivering three-year degrees.

    From strategy to delivery: lessons from cyber that can scale

    The Cyber Workforce of the Future paper provides a live example of how the government’s post-16 vision can be delivered in practice. Its framework rests on three transferable pillars:

    1. Unified skills taxonomy – mapping qualifications and competencies against occupational standards to create a common language for education and industry.
    2. Education – industry bridge – aligning curriculum design and placements to real-world demand through structured partnerships between universities, FE colleges, and employers.
    3. Inclusive pipeline development – embedding equity and access by designing pathways that work for diverse learners and career changers, not just traditional entrants.

    These principles are not unique to cyber; they represent a template for how any technical or digital field can align with the White Paper’s objectives. The challenge now is scaling this joined-up approach nationally across disciplines – from advanced manufacturing to health tech and green energy.

    Six priorities for universities

    1. Redefine admissions and progression routes
      Recognise new qualifications such as V-Levels and HTQs as rigorous, valued entry points to higher education.
    2. Co-design regional skills ecosystems
      Partner with futher education colleges, local authorities, and industry to map regional growth sectors and align provision accordingly.
    3. Develop flexible, modular curricula
      Build stackable learning blocks that learners can access and re-enter throughout their careers under the Lifelong Learning Entitlement.
    4. Co-create with employers
      Move from consultation to collaboration, embedding placements, apprenticeships, and micro-credentials that reflect labour-market demand.
    5. Support learner transition
      Provide structured academic and digital-skills support for students from vocational or stepping-stone routes.
    6. Measure outcomes transparently
      Track progression, attainment, and employability by qualification route to evidence value and inform continuous improvement.

    Opportunities and risks

    The White Paper’s success will depend on genuine partnership between universities, further education providers, and employers. Without coordination, the new structure could replicate old hierarchies – leaving V-Levels or technical routes seen as second-tier options. Similarly, tighter regulation must not deter universities from widening participation or admitting learners who require additional support.

    The cyber-skills sector demonstrates what can work when these risks are managed: clear frameworks, shared standards, and collaborative delivery that bridges academic and technical domains. Replicating this across disciplines will require sustained investment and policy stability, not short-term pilots.

    A new social contract for tertiary education

    The Post-16 Education and Skills White Paper represents a genuine reset for tertiary education – one that values technical excellence, lifelong learning, and regional growth alongside academic achievement.

    Its goals mirror those already embedded within the Cyber Workforce of the Future initiative: building a national system where education and employment are continuous, mutually reinforcing stages of one journey. The cyber model shows that when universities act as integrators –  connecting further education, employers, and government – policy ambitions translate into measurable workforce outcomes.

    What began as a sector-specific experiment can now serve as a blueprint for system-wide reform. If universities across all disciplines embrace this pivot, they can help turn the White Paper’s vision into reality – a cohesive, agile, and inclusive skills ecosystem ready for the future economy.

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  • Transforming higher education learning, assessment and engagement in the AI revolution: the how

    Transforming higher education learning, assessment and engagement in the AI revolution: the how

    • By Derfel Owen, London School of Hygiene & Tropical Medicine and Janice Kay, Higher Futures.

    Generative AI and other new technologies create unprecedented challenges to some of the deepest and longest-held assumptions about how we educate and support students. We start from a position that rejects a defensive stance, attempting to protect current practice from the perceived threat of AI. Bans, restrictions and policies to limit AI use have emerged in an effort to uphold existing norms. Such approaches risk isolating and alienating students who are using AI anyway and will fail to address its broader implications. The point is that AI forces us to reconsider and recapitulate current ways of how we teach, how we help students to learn, how we assess and how we engage and support.  Four areas of how we educate require a greater focus:

    • Critical Thinking and Problem-Solving: Teaching students to evaluate, analyse, and synthesise information while questioning AI-generated outputs.
    • Creativity and Innovation: Focusing on nurturing original ideas, divergent thinking, and the ability to combine concepts in novel ways.
    • Emotional Intelligence: Prioritising skills like empathy, communication, and collaboration,  essential for leadership, teamwork, and human connection.
    • Ethical Reasoning: Training students to navigate ethical dilemmas and critically evaluate the ethical implications of AI use in society.

    Here we set out some practical steps that can be taken to shift us in that direction.

    1. Emphasise Lifelong Learning and Entrepreneurialism

    Education should equip students with the ability to adapt throughout their lives to rapidly evolving technologies, professions and industries. Fostering the ability to learn, unlearn, and relearn quickly in response to changing demands is essential. A well-rounded education will combine new and established knowledge across subjects and disciplines, building in an assumption that progress is made through interdisciplinary connections and creating space to explore the unknown, what we might not know yet and how we go about finding it.

    The transformation of traditional work through AI and automation necessitates that students are fully equipped to thrive in flexible and diverse job markets. Entrepreneurial thinking should be nurtured by teaching students to identify problems, design innovative solutions, and create value in ways that AI can support but not replicate. Leadership development should focus on fostering decision-making, adaptability, and team-building skills, emphasising the inherently human aspects of leadership.

    We should be aware that jobs and job skills in an AI world are evolving faster than our curricula. As McKinsey estimates, AI will transform or replace up to 800 million jobs globally, and the stakes are too high for incremental change.

    2. Promote Originality and Rigour though Collaboration

    AI’s strength lies in the processing speed and the sheer breadth of existing data and knowledge that it can access. It can tell you at exceptional pace what might have taken hours, days or weeks to discover. This should be viewed as a way to augment human capabilities and not as a crutch. Incorporating project-based, collaborative learning with AI will empower students to collaborate to create, solve problems, and innovate while reinforcing their roles as innovators and decision-makers. Working together should be a means of fostering communication skills, but can also be strengthened to encourage, promote and reward creativity and divergent thinking that goes further than conventional knowledge. Students should be encouraged to pursue discovery through critical thinking and verification, exploring unique, self-designed research questions or projects that demand deep thought and personal engagement. These steps will build digital confidence, ensuring students can use AI with confidence and assuredness, are able to test and understand its limitations and can leverage it as a tool to accelerate and underpin their innovation. Examples include generating content for campaigns or portfolio outputs, using AI to synthesise original data, demonstrating Socratic dialogue with AI and its outputs, challenging and critiquing prompts.

    3. Redesign Assessments

    Traditional assessments, such as essays and multiple-choice tests, are increasingly vulnerable to AI interference, and the value they add is increasingly questionable. To counter this, education should focus on performance-based assessments, such as presentations, debates, and real-time problem-solving, which showcase students’ ability to think critically and adapt quickly. Educators have moved away from such assessment methods in recent years because evidence suggests that biases creep into oral examinations. This needs reevaluating to judge the balance of risk in light of AI advancements. Stereotyping and halo biases can be mitigated and can increase student engagement with the assessment and subject matter. What is the greater risk? Biases in oral assessment? Or generating cohorts of graduates with skills to complete unseen, closed-book exams that are likely to be of limited value in a world in which deep and complex information and instruction can be accessed in a fraction of the time through AI? We must revisit these norms and assumptions.

    Collaborative assessments should also be prioritised, using group projects that emphasise teamwork, negotiation, and interpersonal skills. Furthermore, process-oriented evaluation methods should be implemented to assess the learning process itself, including drafts, reflections, and iterative improvements, rather than solely the final outputs. Authenticity in learning outputs can be assured through reflective practices such as journals, portfolios, and presentations that require self-expression and cannot be easily replicated by AI, especially when accompanied by opportunities for students to explain their journey and how their knowledge and approach to a topic have evolved as they learn.

    Achieving such radical change will require a dramatic scaling back of the arms race in assessment, dramatic reductions in multiple, modularised snapshot assessments. Shifting the assessment workload for staff and students is required, toward formative and more authentic assessments with in-built points of reflection. Mitigating more labour-intensive assessments, programme-wide assessment should be considered.

    4. Encourage understanding of the impact of AI on society, resilience and adaptability

    AI will accentuate the societal impact of and concerns about issues such as bias, privacy, and accountability. Utilising AI in teaching and assessment must build an expectation that students and graduates have an enquiring and sceptical mindsets, ready to seek further validation and assurance about facts as they are presented and how they were reached, what data was accessed and how; students need to be prepared and ready to unlearn and rebuild. This will require resilience and the ability to cope with failure, uncertainty, and ambiguity. A growth mindset, valuing continuous learning over static achievement, will help by enhancing their ability to adapt to evolving circumstances. Simulated scenario planning for real-world application of learning will help equip students with the skills to navigate AI-disrupted workplaces and industries successfully.

    The new kid on the block, DeepSeek, has the important feature that it is an open-source reasoning model, low cost (appearing to beat OpenAI o1 that is neither open-source nor free) with the benefit that it sets out its ‘thinking’ step-by-step, helpful for learning and demonstrating learning. It is not, however, able to access external reports critical of the Chinese state, de facto showing that Gen AI models are wholly dependent on the large language data on which they are trained. Students need fully to understand this and its implications.

    Navigating these wide-ranging challenges demands robust support for those shaping the student experience—educators, mentors, and assessors. They remain the heart of higher learning, guiding students through an era of unprecedented change. Yet, bridging the gap between established and emerging practices requires more than just adaptation; it calls for a transformation in how we approach learning itself. To thrive in an AI-integrated future, educators must not only enhance their own AI literacy but also foster open, critical dialogues about its ethical and practical dimensions. In this evolving landscape, everyone—students and educators alike—must embrace a shared journey of learning. The traditional role of the academic as the sole expert must give way to a more collaborative, inquiry-driven model. Only by reimagining the way we teach and learn can we ensure that AI serves as a tool for empowerment rather than a force for division.

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  • The Revolution Will Not Be Televised (Gil Scott-Heron)

    The Revolution Will Not Be Televised (Gil Scott-Heron)

    Gil Scott Heron’s “The Revolution Will Not Be Televised” (1971) urges us to critically engage with the world, empower communities, and move beyond passive learning to active, transformative action.  His work was born in the context of the Civil Rights Movement and the Black Power movement, and it serves as a reminder of how history and sociology can inform contemporary struggles. Will we heed the message? 

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