Knowledge-Based Chatbots in Clinical Teaching: AI-Powered Virtual Patients to Improve History-Taking During Internal Medicine Rotations

Proficiency in history-taking is foundational to safe clinical practice, yet traditional teaching methods offer infrequent opportunities for deliberate practice. This study evaluated whether an AI-powered virtual patient chatbot with a custom knowledge base could improve history-taking competence among fifth-year medical students during general medicine rotations. A mixed-method, quasi-experimental, non-equivalent control group design was used. The experimental group (N ≈ 157) engaged with four custom knowledge-based chatbot scenarios, while a control cohort (N ≈ 156) followed traditional training within the same curriculum. Assessment was performed using a validated Objective Structured Clinical Examination across two standardized cases, and user perceptions were gathered using an adapted Technology Acceptance Model survey from 105 students, with qualitative feedback from 32 trained students in a focus group. Compared to the control group, the experimental group demonstrated significant improvements in direct assessments (79.2% vs. 74.8%, p = 0.002) and indirect assessments (81.2% vs. 79%, p = 0.026). Participants reported a high user-perception score (80%) across usefulness, ease, attitude, and behavior. Focus group findings highlighted the chatbot’s value as a safe, flexible, feedback-rich tool. The chatbot-based training improved students’ performance and satisfaction and should serve as a supplementary aid rather than a substitute for real patient interactions.