Overview
David’s AI Fellowship explores how AI tools can support enquiry-led research-rich learning in large first‑year mathematics courses (around 200 students).
Approach
Following an early review, David moved from inquiry‑based learning (IBL) to problem-based learning (PBL), which he felt is better suited to first‑year students and aligns with LSE’s applied, real‑world approach to mathematics. PBL also mirrors professional mathematical research, where work is driven by clearly defined problems.
AI is embedded within new lab‑based sessions, replacing traditional classes and seminars. Students experiment, identify patterns, form conjectures, and then prove them – replicating authentic research practice.
David is adapting MIT’s Technology‑Enabled Active Learning (TEAL) model, using collaborative, simulation‑based sessions supported by an AI‑assisted coding environment. This allows students to generate large numbers of examples while bypassing syntactic issues associated with writing Python scripts, effectively acting as a vibe-coding copilot grounded in course content.
Impact and next steps
The outcome of this development will be an active-learning / problem-based learning (PBL) framework designed for LSE’s particular focus(es). The modelling and coding environment will be designed so that colleagues will be able to adopt the framework and use the tools with minimal technical expertise.
Other work underway
Work on the fellowship project has resulted in two spin-off projects:
- David is collaborating with Craig Walker, an external expert appointed as Project Lead for LSE’s Teaching and Learning Spaces Strategy, to inform the development of active learning teaching spaces.
- A partnership with Maastricht University to update established PBL frameworks for an AI‑rich educational context, drawing on Maastricht’s international leadership in PBL.