[2604.09158] Structuring versus Problematizing: How LLM-based Agents Scaffold Learning in Diagnostic Reasoning

Computer Science > Human-Computer Interaction arXiv:2604.09158 (cs) [Submitted on 10 Apr 2026] Title:Structuring versus Problematizing: How LLM-based Agents Scaffold Learning in Diagnostic Reasoning Authors:Fatma Betül Güreş, Tanya Nazaretsky, Seyed Parsa Neshaei, Tanja Käser View a PDF of the paper titled Structuring versus Problematizing: How LLM-based Agents Scaffold Learning in Diagnostic Reasoning, by Fatma Bet\"ul G\"ure\c{s} and 3 other authors View PDF HTML (experimental) Abstract: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 pharmacist agent that implements pedagogical conversations rooted in two theory-driven scaffolding approaches: \emph{structuring} and \emph{problematizing}, as well as a student learning trajectory. In a between-groups experiment, 63 vocational students completed a learning scenario, a near-transfer scenario, a...