A Mixed-Methods Analysis of the Six Core Competencies and AI Literacy Reflected in Medical Students' Self-Assessment During Small-Group Discussions

This study aims to examine how the six Accreditation Council for Graduate Medical Education (ACGME) core competencies and AI literacy are reflected in medical students' self-assessment narratives following small-group discussions. A mixed-methods design will be used to analyze approximately 500 de-identified self-assessment reflection reports submitted by fourth-year medical students who completed an integrated pathology and pharmacology small-group discussion course at National Taiwan University during the 2023-2026 academic years. Reports containing fewer than 300 words or lacking substantive reflection will be excluded. Deductive thematic analysis will be conducted using the six ACGME core competencies-Patient Care, Medical Knowledge, Practice-Based Learning and Improvement, Interpersonal and Communication Skills, Professionalism, and Systems-Based Practice-as an a priori coding framework. Open coding will be applied to identify emerging themes not adequately captured by this framework, including AI use and literacy, professional identity formation, faculty facilitation, and self-directed learning strategies. Two researchers will independently code all reports using a predefined codebook, and inter-rater reliability will be assessed using Cohen's kappa. Quantitative content analysis will examine the frequencies, proportions, co-occurrence patterns, and academic-year differences in competencies and emerging themes. Qualitative and quantitative findings will subsequently be integrated through joint displays.

Inclusion criteria

• Reports from the fourth-year medical students who completed an integrated pathology and pharmacology small-group discussion course at National Taiwan University during the 2023-2026 academic years.

Exclusion criteria

• Reports containing fewer than 300 words or lacking substantive reflection will be excluded.