Morning, Friday, 11th December 2026
Online
This conference will examine next steps for artificial intelligence in higher education in the UK. Planned areas for discussion include implications for teaching and learning, assessment and academic standards, institutional governance and capability, staff and graduate skills, research and innovation, and equality and the student experience.
Emerging evidence & policy direction
It will bring together stakeholders and policymakers to assess priorities for the responsible adoption and oversight of AI across higher education in the context of the forthcoming publication of findings from the Office for Students and Advance HE’s joint sector-wide research expected later this year, alongside evidence from the Quality Assurance Agency’s recently published report The Perfect Storm: AI, assessment and a sector under pressure. Delegates will consider implications of emerging evidence alongside the AI Opportunities Action Plan: One Year On and implementation of the Government’s Post-16 Education and Skills White Paper, including expectations for institutional AI strategies, the integrity of qualifications and the development of AI capability across the student population.
Institution-wide adoption, governance & assessment
Sessions will examine practical requirements for moving from individual use and isolated pilots towards responsible institution-wide adoption. This will include how governance and accountability should operate, how institutions can assure the systems they procure, and what support staff and students might need as practice develops. Further discussion will consider approaches to evaluating the impact of AI on learning, accessibility and workload, alongside the redesign of assessment to provide reliable evidence of students’ knowledge, skills and independent judgement. Delegates will also examine how providers can give clear guidance on acceptable use while maintaining consistency, fairness and appropriate flexibility between disciplines.
Skills, research & access
Planned sessions will assess the longer-term contribution of higher education to an AI-enabled economy and research system. Delegates will discuss how AI literacy can be embedded across disciplines in ways that respond to changing professional and employer requirements without weakening foundational learning. Consideration will be given to the technical, critical and ethical capabilities graduates will require, as well as the role of universities in AI-enabled research and innovation. Wider discussion will examine inequalities in access to tools, infrastructure and expertise, and how collaboration between institutions, employers, government and sector bodies can support proportionate oversight, shared learning and public confidence.
Overview of areas for discussion
- policy direction, regulation & institutional governance:
- OfS and Advance HE research - adoption, regulation, standards and student outcomes
- roles of government, regulators, sector bodies and institutional leaders - common principles and institutional autonomy
- institution-wide AI strategies - governance, accountability and human oversight
- adoption decisions - evidence of need, benefit, risk and cost
- assessment, teaching & the student experience:
- assessment redesign - legitimate assistance, assessed AI capability and independent work
- moving beyond AI detection - addressing software limitations, proportionate assurance, fairness and consistency
- pedagogical uses of AI - feedback, tutoring, personalised learning and student support
- clear expectations for use - student involvement and effects on learning, attainment and retention
- AI literacy, staff capability & employment:
- AI literacy across subjects and levels - technical, critical, ethical and subject capability for all students - foundational knowledge, independent judgement and the value of qualifications
- graduate outcomes in an AI-affected labour market - entry-level recruitment, employability and provider accountability for student outcomes
- staff development - confidence, workload, professional autonomy and changing roles
- alignment with employer and professional needs - industry placements, tech traineeships and applied learning
- data, infrastructure, procurement & assurance:
- data protection and cyber security - safeguarding student and research data
- procurement and contractual safeguards - auditability, provider oversight and technological dependency
- model performance and accountability - challenging or correcting AI-supported decisions
- access to secure tools, compute and expertise - national infrastructure funding alongside financial and environmental implications
- equality, accessibility & student protection:
- accessible learning and support - disabled and neurodivergent students, human oversight and student choice
- unequal access to AI - tools, devices, connectivity, training and institutional support
- bias and differential performance - implications for particular groups and international students
- transparency, privacy and redress - maintaining trust between students, staff and institutions
- research, innovation & national AI capability:
- AI-enabled research - priorities in the UKRI AI Research and Innovation Strategic Framework
- research integrity - authorship, reproducibility, peer review and generated material
- data, compute and interdisciplinary expertise - intellectual property, security and industry collaboration
- skills, commercialisation and regional innovation - AI Growth Zones, public-interest AI and national capability
