AI Ed Wiki logoAI Ed Wiki
Use with AI

📄 Research Article

Using Generative AI to Simulate Patient History-Taking in a Problem-Based Learning Tutorial: A Mixed-Methods Study

● high · 2026-08-20 · problem-based-learning, generative-ai, medical-education, virtual-patient, simulation, mixed-methods

Synthesis: In March 2024, 37% of second-year students at a small community-based Midwestern U.S. medical school piloted a genAI-enabled virtual patient in a required PBL tutorial, comparing it to the school's legacy Electronic PBL Module (ePBLM) searchable database. Across two genAI groups (N=13) and two comparison groups (N=13) sharing the same faculty facilitator, the genAI avatar presented essential case content accurately but students treated it as a sophisticated "question base" rather than a real patient. The study demonstrates that genAI can represent essential PBL case content, stimulate problem-driven collaboration, and boost perceived engagement and teamwork — while exposing the need to frame the experience so students experiment with the AI's humanlike capabilities and adapt to its limitations.

Key Findings

  • GenAI enabled accurate essential content, with occasional deviations. Pertinent positive findings matched the ePBLM, but the AI deviated on non-essential or non-pertinent-negative content (e.g., one group reported headaches "on and off for a few months"; the ePBLM returned "Only on occasion"), and reported occasional recreational marijuana use not in the ePBLM.
  • GenAI tutorials took longer, especially on history-taking. GenAI groups spent 55–65 min vs. 36–39 min for ePBLM, with ~10 min more on history-taking (24–30 vs. 16–19 min); only genAI groups closed the case by communicating the diagnosis and initial management plan to the patient.
  • Students rated the genAI experience significantly higher overall. Perceptions survey total rose only for the genAI condition (beta = 5.77, 95% CI [1.27, 10.27], p = .013); genAI students rated clinical accuracy 1.6 points higher post-tutorial and reported higher enjoyability, teamwork, interest, and confidence. Model R² = 0.25, F(3, 48) = 5.39, p = .003.
  • History information recall was near ceiling in both conditions. Immediate recall was 9.40 (ePBLM) vs. 10.10 (genAI) of 11; delayed recall 7.94 vs. 8.63. Only time (immediate→delayed forgetting) was significant (p < .001); rate of forgetting did not differ by condition.
  • Students did not interview the avatar like a human. Their questioning was predominantly closed-ended and efficiency-focused; Group 1's student asked the tutor, "Can we give him advice or is it [the AI] just, like, a question base?" The genAI's rapport-building embellishments were sometimes inconsistent (forthcoming responses for one group, irritable tone for another).

Study Design & Method

Between-groups, mixed-methods pilot at a small community-based U.S. medical school using hybrid PBL. Twenty-six second-year students (37% of Class of 2026; convenience sample; of 14 reporting demographics, 79% women, 20% minoritized racial/ethnic group) opened the first required case of the Endocrinology, Reproduction, and Gastroenterology (ERG) unit (~3 months before USMLE Step 1). Two groups (N=13) conducted voice-to-voice history-taking with a genAI-enabled avatar (Randy Rhodes, a 54-year-old man with Type 2 Diabetes); two comparison groups (N=13) gathered history by keyword search of the ePBLM. All four groups had the same expert faculty facilitator (a 16-year "master" PBL tutor). The avatar combined a local speech-to-text model, ElevenLabs voice synthesis, D-ID facial animation, and OpenAI GPT-4o driven by the faculty ePBLM case materials with "guardrails" (e.g., preventing the AI from revealing the diagnosis). Data included: descriptive observation (Spradley) of audio recordings by 5 co-investigators using a reflexivity-bracketed template with inductive consensus coding; an 8-item locally developed Learner Perceptions Survey (summated 8–40, item 5 reverse-coded); and an 11-item short-answer Patient History Information Quiz graded masked to condition, administered immediately and 2 weeks post-tutorial. Analysis used OLS linear regression (R 4.5.0; lme4, emmeans, gtsummary, ggplot2), two-tailed α = 0.05.

Implications for AI in Education

  • Representing patients realistically in PBL is feasible but needs framing. GenAI accurately delivered essential case content and stimulated the problem-driven collaboration defining PBL, but students defaulted to a "question base" frame. Tutors should encourage students to experiment with the AI's humanlike capabilities and help them recognize and adapt to its limitations.
  • Humanistic interaction requires early and explicit Scaffolding. Because rising third-year students brought an ePBLM-querying framework into the encounter, introducing genAI-enabled patients early (even first year) — before an alternative interactional framework takes hold — may promote more patient-centered communication than the predominantly closed-ended questioning observed.
  • Content deviation creates a new tutor role. The inevitability of genAI deviation (especially on pertinent negative findings) suggests tutors must help students stay on track when embellished or inconsistent responses tempt distraction; the impact should be studied systematically across case types.
  • Reality-accuracy trade-off is central. Tightening guardrails to prevent deviation risks making the LLM robotic and unrealistic; loosening them risks bias, inaccuracy, unpredictability, and inappropriate emotional dependence — a philosophical question about how much deviation from faculty control PBL can absorb.
  • Methodological strengths and limits. This is among the first studies to directly observe (rather than infer from activity/self-report) the effect of manipulating patient modality in PBL. It is a "best case" single-case, single-school pilot using locally developed instruments and small group numbers (no group-level statistical control), so it aims at groundwork rather than generalizability.

Connected Concepts

Connected Articles

Citation

Mool, A., Schmid, J., Johnston, T., Smith McCoy, K.J., et al. (2026). Using generative AI to simulate patient history-taking in a problem-based learning tutorial. Technology, Knowledge and Learning.