Purpose of the Call

Artificial intelligence is not only introducing new tools into higher education. It is changing the conditions under which universities teach, learn, conduct research, build relationships and contribute to society. The question is therefore not only how universities should use AI. It is also what universities should preserve, transform or refuse when AI becomes widely available.

How should we teach when students can generate answers instantly? What forms of learning require effort, attention, embodiment or human interaction? What happens to expertise when information becomes abundant? What should universities automate, augment or deliberately keep human? How can higher education remain a place of judgement, discovery, community and public purpose?

The past year has sharpened these questions, for university research and for the systems universities run as much as for teaching. In May 2026 an internal OpenAI reasoning model produced a counterexample to a conjecture that Paul Erdős had posed in 1946, a result that a group of external mathematicians checked and published the same day. In July, AI agents under cybersecurity evaluation at OpenAI circumvented controls intended to isolate them from the internet and compromised parts of Hugging Face's production infrastructure, an incident that OpenAI disclosed on 21 July. The value of a degree has been questioned on two fronts, as a UK policy report warned that unsupervised assessment no longer certifies learning and US data showed falling enrolment in computer science alongside weaker entry-level hiring in AI-exposed fields. Research published over the same year, from randomised trials in schools to a consensus of learning scientists, documents that learners who hand their thinking to generative AI can perform well with the tool yet understand and retain less without it.

In March 2026 the first AI in Higher Education Summit asked how universities should evolve in an AI world. The AI in Higher Education Summit 2027 invites contributions that explore the university in an AI era. We welcome research papers, institutional cases, pedagogical experiments, position papers, demonstrations and critical reflections on both AI-enabled and non-AI responses to the transformation of higher education. Contributions that return to an initiative presented in 2026, citing its entry in the 2026 proceedings, with evidence of what held, what was dropped and what was generalised are especially welcome.

Accepted contributions are presented in the Summit's parallel sessions or in the demo room. Accepted academic contributions will also inform the emerging European Community of Practice on AI in academia and the Summit's 2027 report, read against the 2026 white paper, Building AI-Ready Universities. Contributions should align with one of the three submission themes:

Teaching & Learning

Beyond AI-enhanced pedagogy, this theme asks how learning itself is redesigned in the AI era.

• Assessment after generative AI: authenticity, validity and trust
• Oral examinations, portfolios, project-based assessment and process evidence
• Human judgement, critical thinking and cognitive effort
• AI use and non-use as deliberate pedagogical choices

Research & Scholarship

• AI in research methods and tools, and what expertise and discovery mean when results can be generated
• Academic integrity and authorship when AI acts in the research process
• Ethical frameworks and institutional policies, and how they hold up in use

Administration & Strategy

• AI in student services, admissions and operations, including agents in the back office
• Data governance, regulation (the EU AI Act in universities) and who answers for a decision an agent takes
• Institutional platforms at scale: what universities automate, augment or keep human, and the evidence behind each choice

Types of Contributions

The Summit welcomes a variety of submissions, including:

• Case Studies – Documented examples of AI adoption in teaching, research or administration: what was tried, what was measured, what was learned, including follow-ups to initiatives presented in 2026
• Research Papers – Theoretical, empirical or methodological contributions
• Position Papers – Vision statements, frameworks or critical reflections on AI-enabled and non-AI responses
• Academic Demonstrations – Live showcases of tools or AI-enabled practices built inside a university
• Company and Start-up Demos (new in 2027) – Live demonstrations, in the demo room, of a product or service in use at or designed for a higher education institution. The text describes the use case, the evidence of use or the design rationale, and the product's limits; a university partner is welcome, and a demo led by a company belongs in this category

Accepted academic contributions will be included in the official Summit proceedings, published with a DOI. Accepted company and start-up demos are listed in the Summit programme.

Submission Guidelines

Organizers