This study applies Kolb’s Experiential Learning Theory (ELT) and Davis’s Technology Acceptance Model (TAM) to characterize generative AI use among students in a College of Korean Medicine (KM) and to analyze perception differences by clinical practicum experience. A cross-sectional, mixed-methods survey was conducted with 126 students: 72 pre-clerkship Year 1–3 students (before university hospital practicum) and 54 clerkship Year 4 students (during practicum). The questionnaire covered five domains: (1) experience, level, and purposes of generative AI use; (2) expectations, concerns, and solutions for AI in KM clinical and educational contexts; (3) trust and validation strategies; (4) legal and ethical issues; and (5) overall concerns, mitigation strategies, and anticipated benefits. Quantitative items were analyzed with frequency and descriptive statistics, and free-text responses with thematic analysis. Most students regarded generative AI as an information and learning support tool and articulated the need for structured AI education. Technology-acceptance attitudes differed by practicum status: pre-clerkship students expected support for clinical reasoning and evidence-based decision-making, whereas clerkship students emphasized patient education and clinical communication. Major concerns across cohorts included reliability and accuracy, algorithmic bias, legal/ethical accountability, and the risk that overreliance could weaken self-directed learning and critical thinking. Overall, the findings demonstrate that practicum experience shapes acceptance and envisioned uses of generative AI and support a staged, ELT-based integration model: critical appraisal and creative reconstruction in pre-clerkship years, and safe, patient-facing applications with explicit validation and governance in clerkship. These implications can guide curriculum design and faculty development for responsible AI adoption in KM education.
Han et al. (Wed,) studied this question.