Date of the Workshop: Monday 1 June 2026
Location: University College London
Number of participants: Between 55-60
Number of distinct organisations represented: 23 from registration form
Introduction
The workshop was motivated by the rapid improvement of AI systems and their growing ability to perform many of the tasks traditionally used in mathematical sciences education, including problem solving, coding, statistical analysis, and mathematical exposition. This raises questions not only about assessment, but also about the broader purpose of a mathematical sciences degree and the balance between technical skills, conceptual understanding, judgement, communication, and professional practice. The workshop provided an opportunity for participants to share experiences, emerging challenges, and discuss possible responses at curriculum, module, and assessment level.
Workshop Overview
Attendees represented a range of mathematical sciences disciplines and educational roles, creating opportunities for discussion across institutional and disciplinary boundaries.
The day began with a keynote address from Professor Michael Grove, setting the scene for the challenges and opportunities that generative AI presents for higher education.
The remainder of the morning was structured around three themed presentations:
- Proof, critical reasoning, and logic (Dr Chris Birkbeck)
- Coding and assessment in the age of AI (Dr Jenn Gaskell)
- Ethical considerations in the use of AI (Dr Liz Munday)
The afternoon consisted of two rounds of breakout discussions. Participants selected from sessions aligned with one of the three themes in each round. Each breakout discussion was facilitated by the relevant speaker of the morning session.
Main Themes and Ideas
Proof, critical reasoning, and logic
Dr Chris Birkbeck’s presentation explored the implications of generative AI for mathematical reasoning and proof. Discussion focused on the distinction between producing mathematical arguments and genuinely understanding them.
Coding and assessment in the age of AI
Dr Jenn Gaskell examined the growing impact of AI-assisted coding on both learning and assessment. Participants discussed the opportunities offered by AI tools to support learning, reduce barriers to programming, and increase productivity. At the same time, concerns were raised about how educators can ensure that students develop genuine computational understanding when AI can generate substantial amounts of code.
Ethical considerations in the use of AI
Dr Liz Munday’s presentation focused on the ethical dimensions of AI use within higher education and professional practice. Discussions explored issues of transparency, fairness, bias, accountability, environmental impact, and the responsible use of AI tools.
Emerging Implications and Recommendations
A clear message from the workshop (and from the post-workshop feedback) was that there are no straightforward answers to the challenges posed by generative AI. The implications for curriculum design, assessment, and student learning are complex, and different institutions and disciplines are experimenting with a range of approaches.
Participants noted that institutions are currently operating at different stages of their response to AI and under different institutional policies and guidance. As a result, there is considerable variation in what is considered acceptable practice in teaching, learning, and assessment. While this can create uncertainty, it also provides opportunities for institutions to learn from one another’s experiences.
Resources and Links
Slides and a link to recorded presentations will be added shortly to the workshop webpage: A Mathematical Sciences Curriculum in the Age of AI
Acknowledgments
We are grateful for the funding received from the IMA/ LMS/ RSS. In addition to this, we were supported financially by the Department of Mathematics, the Department of Statistical Science, and the Faculty of Mathematics and Physical Sciences at UCL.
Conclusion
The workshop provided an opportunity for the mathematical sciences community to engage with the challenges and opportunities presented by generative AI. Our focus was on proof and reasoning, coding, and ethical considerations, rather than solely on assessment. While there was recognition that AI will continue to transform aspects of teaching, learning, and professional practice, participants consistently emphasised the continuing importance of deep understanding, critical judgement, and responsible use of technology.
