Reports and White Papers

Count on It: Numeracy, AI, and Social Mobility

Connect, PolicyWorking paper

2026.

Links | BibTeX

Higher Education for Sustainable Transformation: Policy Messages from the HESI Community in 2026

Community, HESIWorking paper

2026.

Links | BibTeX

Written evidence: The use of Artificial Intelligence and EdTech in Education

Specht, Doug; Saunders, GunterWorking paper

2026.

Links | BibTeX

Being indispensable: Capabilities for a human-AI world, the ‘FUTURES’ framework

Specht, Doug; Saunders, GunterWorking paperOpen Access

2026.

Abstract | Links | BibTeX

@workingpaper{nokey,

title = {Being indispensable: Capabilities for a human-AI world, the ‘FUTURES’ framework},

author = {Doug Specht and Gunter Saunders},

url = {https://www.hepi.ac.uk/reports/being-indispensable-capabilities-for-a-human-ai-world-the-futures-framework/},

year = {2026},

date = {2026-03-05},

urldate = {2026-03-05},

publisher = {Higher Education Policy Institute},

abstract = {Artificial intelligence has inƮuenced higher education for many years, but the rapid growth of generative AI (GenAI) is accelerating change across teaching, learning and professional services. Early uses of AI focused on automation, such as intelligent tutoring systems or early warning analytics. GenAI, however, is now embedded directly in students’ and staƪ members’ everyday work.

Seventy-five per cent of young people aged 13-to-18 have already used GenAI, and similar patterns are emerging across universities. These tools can support idea generation, writing, feedback and administrative eƯciency, reshaping expectations of how learning happens and work gets done.

The opportunities are significant. GenAI can personalise learning, reduce staff workload and widen access to support. It can enable faster feedback, drive change in assessment approaches and provide greater Ʈexibility in how our students and staff complete tasks. At the same time, there are tangible, concerning risks. The Large Language Models (LLMs) that make tools like ChatGPT work can reƮect bias within their training data. Access to the most capable GenAI tools by students who can aƪord them may deepen inequalities, and the use of GenAI without due critical evaluation of any output can weaken independence, originality and authentic learning. Concerns around environmental impact add an additional layer of complexity.

This report argues that higher education requires a dual response: eƪective integration of GenAI, and systematic development of the human capabilities that remain essential in an AI-rich world. The FUTURES framework provides a practical model, oƪering seven domains – ranging from digital Ʈuency and ethical judgement to resilience, wellbeing and social intelligence – that can be embedded across curricula, assessment and institutional strategy.

The report focuses specifically on the role of GenAI in teaching and learning within higher education institutions. While GenAI clearly has significant implications for the research community, from literature synthesis and data analysis to hypothesis generation and collaborative knowledge production, the scope of this report is intentionally circumscribed to pedagogical practice and the wider student experience.

By maintaining this focus on teaching and learning, rather than on research excellence or institutional knowledge production, we are better able to examine in depth the challenges of higher education pedagogy and the student experience. Educational institutions have a responsibility to ensure that GenAI adoption aligns with the well-established principles of authentic learning, critical 4 Being indispensable: Capabilities for a human-AI world, the ‘FUTURES’ framework thinking development and equitable access. The risks and opportunities that emerge in the teaching and learning context diƪer in nature and severity from those facing the research community, and warrant dedicated, sustained attention.

Retaining our teaching and learning focus allows us to provide institutions with actionable guidance rooted in pedagogical values and student outcomes, rather than spreading analysis too thinly across the multiple and sometimes competing demands placed on universities.

For policymakers and university leaders, there are significant implications to consider. Institutions need clear approaches to GenAI-enabled learning, supported by practical governance, AI literacy training and equal access to GenAI tools. Curricula should purposefully integrate human-AI collaboration while ensuring ethical reasoning, critical thinking and wellbeing remain central to any overarching policy or strategy. Sector bodies have a role to provide guidance on assessment design in an AI-enabled environment, and government must invest in capability building to ensure students and staff can prosper in a world where GenAI is seen as a partner or co-worker / assistant. By adopting the FUTURES framework, we believe universities can find a ‘middle’ path with GenAI, enhancing human potential while navigating the opportunities and risks of AI more broadly.},

keywords = {},

pubstate = {published},

tppubtype = {workingpaper}

}

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Artificial intelligence has inƮuenced higher education for many years, but the rapid growth of generative AI (GenAI) is accelerating change across teaching, learning and professional services. Early uses of AI focused on automation, such as intelligent tutoring systems or early warning analytics. GenAI, however, is now embedded directly in students’ and staƪ members’ everyday work.

Seventy-five per cent of young people aged 13-to-18 have already used GenAI, and similar patterns are emerging across universities. These tools can support idea generation, writing, feedback and administrative eƯciency, reshaping expectations of how learning happens and work gets done.

The opportunities are significant. GenAI can personalise learning, reduce staff workload and widen access to support. It can enable faster feedback, drive change in assessment approaches and provide greater Ʈexibility in how our students and staff complete tasks. At the same time, there are tangible, concerning risks. The Large Language Models (LLMs) that make tools like ChatGPT work can reƮect bias within their training data. Access to the most capable GenAI tools by students who can aƪord them may deepen inequalities, and the use of GenAI without due critical evaluation of any output can weaken independence, originality and authentic learning. Concerns around environmental impact add an additional layer of complexity.

This report argues that higher education requires a dual response: eƪective integration of GenAI, and systematic development of the human capabilities that remain essential in an AI-rich world. The FUTURES framework provides a practical model, oƪering seven domains – ranging from digital Ʈuency and ethical judgement to resilience, wellbeing and social intelligence – that can be embedded across curricula, assessment and institutional strategy.

The report focuses specifically on the role of GenAI in teaching and learning within higher education institutions. While GenAI clearly has significant implications for the research community, from literature synthesis and data analysis to hypothesis generation and collaborative knowledge production, the scope of this report is intentionally circumscribed to pedagogical practice and the wider student experience.

By maintaining this focus on teaching and learning, rather than on research excellence or institutional knowledge production, we are better able to examine in depth the challenges of higher education pedagogy and the student experience. Educational institutions have a responsibility to ensure that GenAI adoption aligns with the well-established principles of authentic learning, critical 4 Being indispensable: Capabilities for a human-AI world, the ‘FUTURES’ framework thinking development and equitable access. The risks and opportunities that emerge in the teaching and learning context diƪer in nature and severity from those facing the research community, and warrant dedicated, sustained attention.

Retaining our teaching and learning focus allows us to provide institutions with actionable guidance rooted in pedagogical values and student outcomes, rather than spreading analysis too thinly across the multiple and sometimes competing demands placed on universities.

For policymakers and university leaders, there are significant implications to consider. Institutions need clear approaches to GenAI-enabled learning, supported by practical governance, AI literacy training and equal access to GenAI tools. Curricula should purposefully integrate human-AI collaboration while ensuring ethical reasoning, critical thinking and wellbeing remain central to any overarching policy or strategy. Sector bodies have a role to provide guidance on assessment design in an AI-enabled environment, and government must invest in capability building to ensure students and staff can prosper in a world where GenAI is seen as a partner or co-worker / assistant. By adopting the FUTURES framework, we believe universities can find a ‘middle’ path with GenAI, enhancing human potential while navigating the opportunities and risks of AI more broadly.

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The Environment in Focus

Specht, DougTechnical Report

2025.

Links | BibTeX