AI RESEARCH
Structuring versus Problematizing: How LLM-based Agents Scaffold Learning in Diagnostic Reasoning
arXiv CS.AI
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ArXi:2604.09158v1 Announce Type: cross ing 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.