Structuring versus Problematizing: How LLM-based Agents Scaffold Learning in Diagnostic Reasoning
Supporting students in developing diagnostic reasoning is a key challenge across educational domains. Novices often face cognitive biases such as premature closure and over-reliance on heuristics, and they struggle to transfer diagnostic strategies to new cases. Scenario-based learning (SBL) enhanced by Learning Analytics (LA) and large language models (LLM) offers a promising approach by combining realistic case experiences with personalized scaffolding. Yet, how different scaffolding approaches shape reasoning processes remains insufficiently explored. This study introduces PharmaSim Switch, an SBL environment for pharmacy technician training, extended with an LA- and LLM-powered pharma...
| Authors | Fatma Betül Güreş, Tanya Nazaretsky, Seyed Parsa Neshaei et al. |
|---|---|
| Primary category | cs.HC |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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