Open Learner Models in the Age of Generative AI

Research in Open Learner Models (OLMs) has long explored how enabling learners to review and interact with a learner model can support learner metacognition and agency by making learner representations transparent and inspectable. Nowadays, Generative AI, and in particular, Large Language Models (LLMs) create both an opportunity and a challenge for OLMs: while LLMs can generate rich, naturalistic explanations of learner states and lower the cost of OLM development, they lack the structured, inspectable representations that give OLMs their epistemic integrity. This half-day interactive workshop aims to bring together researchers and practitioners to critically examine this tension and explore how OLMs can leverage the communicative power of LLMs without sacrificing transparency, validity, and learner agency. Through opening provocations, a state-of-the-art presentation, and a hands-on design challenge, participants will surface open research questions, identify non-negotiable principles for LLM-enhanced OLMs, and collectively develop a research agenda. The workshop targets the AIED, learning analytics, and user modeling communities, and aims to produce a shared position paper and design guidelines as concrete outputs.