AI summaryⓘ
The authors studied how to help pharmacy technician students improve their diagnostic reasoning using a learning system called PharmaSim Switch. This system uses realistic case scenarios and an AI pharmacist agent that gives personalized help in two ways: organizing students' thinking (structuring) and encouraging deeper questioning (problematizing). They found both methods helped students use diagnostic strategies well, but the difficulty of the cases mattered more for performance than the help style or prior knowledge. Structuring led to more active participation, while problematizing encouraged deeper engagement. The authors suggest combining these methods could better support learning in AI-based training.
diagnostic reasoningscenario-based learninglearning analyticslarge language modelsscaffoldingpremature closureheuristicsstructuringproblematizingtransfer of learning
Authors
Fatma Betül Güreş, Tanya Nazaretsky, Seyed Parsa Neshaei, Tanja Käser
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, and a far-transfer scenario under one of the two scaffolding conditions. Results indicate that both scaffolding approaches were effective in supporting the use of diagnostic strategies. Performance outcomes were primarily influenced by scenario complexity rather than students' prior knowledge or the scaffolding approach used. The structuring approach was associated with more accurate Active and Interactive participation, whereas problematizing elicited more Constructive engagement. These findings underscore the value of combining scaffolding approaches when designing LA- and LLM-based systems to effectively foster diagnostic reasoning.