A Curriculum-Embedded Two-Session AI Chatbot-Based History-Taking Practicum in Korean Medicine Diagnostics

Background: History-taking is a core clinical competency in Korean medicine diagnostics, but conventional training methods such as peer role-play and standardized patient-based education have limitations in providing repeated, individualized, and scalable practice opportunities. This study aimed to evaluate the feasibility and educational value of a two-session AI chatbot-based history-taking practicum with automated feedback in Korean medicine diagnostics. Methods: This prospective single-arm repeated-measures educational study was conducted with fourth-year students at the College of Korean Medicine, Dongguk University, in May and June 2026. A total of 76 students participated in two chatbot-assisted history-taking sessions using dizziness and shoulder pain scenarios. Students completed surveys on baseline AI familiarity, chatbot experience, usability, and self-efficacy. Self-efficacy was assessed at three time points: before the first session, after the first session, and after the second session. Chatbot-generated feedback scores were compared between session 1 and 2 for each scenario using paired complete-case analyses. Open-ended responses were descriptively categorized. Results: Students rated the chatbot-based practicum positively in terms of active participation, perceived usefulness, accessibility, and convenience. Item-level self-efficacy analysis showed significant time effects in two domains: planning the conversation, and closing the conversation appropriately. The overall mean self-efficacy score gradually increased from 3.739 ± 0.546 before the first session to 3.887 ± 0.591 after the second session; however, the overall time effect did not reach statistical significance. Chatbot-generated feedback scores showed scenario-dependent patterns. Scores for the shoulder pain scenario increased from session 1 to session 2 before adjustment, but this change did not remain significant after Holm correction; scores for the dizziness scenario showed a non-significant decreasing trend. Open-ended responses indicated that students valued repeated practice and immediate feedback, while also noting limitations related to feedback accuracy, realism of patient responses, and the lack of physical examination or multimodal diagnostic information. Conclusions: The chatbot-assisted practicum was feasible and favorably perceived by students, with selected item-level changes and a modest non-significant upward trend in overall self-efficacy in this exploratory educational study. These findings support the potential role of AI chatbot-based simulation as a supplementary, scalable tool for repeated history-taking practice and formative feedback, rather than as a replacement for performance-based clinical skills training. Given the single-arm design and reliance on learner-reported outcomes, these findings should be interpreted as exploratory. Future controlled studies should incorporate objective performance outcomes, expert-validated automated scoring, non-AI comparison groups, and more realistic multimodal clinical scenarios to determine the educational effectiveness of chatbot-assisted history-taking training.