Uncovering Students' Inquiry Patterns in GenAI-Supported Clinical Practice: An Integration of Epistemic Network Analysis and Sequential Pattern Mining
🤖 AI Summary
Traditional clinical communication assessment in pharmacy education relies heavily on manual evaluation, limiting scalability and fine-grained feedback—particularly for multilingual learners. To address this, we propose a novel method integrating Epistemic Network Analysis (ENA) with Sequential Pattern Mining (SPM), applied to 1,487 virtual patient dialogues from 323 students, comprising 50,871 coded utterances. Our analysis reveals that high-performing students employ coordinated inquiry strategies across three dimensions: information identification, relational establishment, and structural organization. Furthermore, linguistic background and prior clinical experience significantly modulate developmental trajectories. We identify canonical inquiry pathways that empirically map the progression of clinical reasoning, offering both theoretical insight and practical methodology for personalized learning analytics and adaptive AI-driven instructional design in health professions education.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Analyzes student inquiry patterns in GenAI-supported clinical communication training.
Identifies performance differences in clinical reasoning via learning analytics methods.
Explores demographic influences on inquiry strategies in pharmacy education contexts.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Epistemic Network Analysis models inquiry co-occurrences
Sequential Pattern Mining captures temporal behavioral sequences
Learning analytics applied to GenAI-virtual patient interaction logs
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