Uncovering Students' Inquiry Patterns in GenAI-Supported Clinical Practice: An Integration of Epistemic Network Analysis and Sequential Pattern Mining

TL;DR

This study analyzes how pharmacy students develop medication history taking in GenAI assisted practice by applying Epistemic Network Analysis and Sequential Pattern Mining to 50,871 utterances from 1,487 student VP dialogues. It reveals that high performers center on recognizing relevant information and integrate rapport building with structured inquiry, while low performers are more confined to routine questions. Language background, prior pharmacy experience, and institutional context shape distinct inquiry patterns, with EAL learners showing verification bridging and L1 speakers exhibiting broader social connections. The work demonstrates how combined learning-analytics approaches can inform adaptive GenAI training and formative assessment in health professions education. Overall, it provides methodological and practical insights for leveraging GenAI traces to support diverse learning pathways in clinical communication.

Abstract

Assessment of medication history-taking has traditionally relied on human observation, limiting scalability and detailed performance data. While Generative AI (GenAI) platforms enable extensive data collection and learning analytics provide powerful methods for analyzing educational traces, these approaches remain largely underexplored in pharmacy clinical training. This study addresses this gap by applying learning analytics to understand how students develop clinical communication competencies with GenAI-powered virtual patients -- a crucial endeavor given the diversity of student cohorts, varying language backgrounds, and the limited opportunities for individualized feedback in traditional training settings. We analyzed 323 students' interaction logs across Australian and Malaysian institutions, comprising 50,871 coded utterances from 1,487 student-GenAI dialogues. Combining Epistemic Network Analysis to model inquiry co-occurrences with Sequential Pattern Mining to capture temporal sequences, we found that high performers demonstrated strategic deployment of information recognition behaviors. Specifically, high performers centered inquiry on recognizing clinically relevant information, integrating rapport-building and structural organization, while low performers remained in routine question-verification loops. Demographic factors including first-language background, prior pharmacy work experience, and institutional context, also shaped distinct inquiry patterns. These findings reveal inquiry patterns that may indicate clinical reasoning development in GenAI-assisted contexts, providing methodological insights for health professions education assessment and informing adaptive GenAI system design that supports diverse learning pathways.

Figures (2)

• Figure 1: Clinical simulation platform architecture illustrating the student-VP interaction cycle, with GenAI engine processing and data logging components (solid arrows: user interactions; dashed arrows: system processing).
• Figure 2: Epistemic Network Analysis subtraction plots.