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

TL;DR

This study evaluates how structuring versus problematizing scaffolding approaches, enabled by LLMs and Learning Analytics, influence diagnostic reasoning in pharmacy training scenarios, highlighting their distinct impacts on student engagement and accuracy.

Contribution

It introduces PharmaSim Switch, an innovative SBL environment with LLM-powered scaffolding, and compares two theory-driven approaches to enhance diagnostic reasoning in vocational education.

Findings

Both scaffolding approaches supported diagnostic strategy use.

Scenario complexity influenced performance more than scaffolding or prior knowledge.

Structuring led to more accurate participation; problematizing fostered more constructive engagement.

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.