Educational Equivalence of AI-Driven Virtual Patients and Standardized Patients in Undergraduate Medical Education:A Mixed-Methods Randomized Crossover Study

Background Artificial intelligence (AI) driven virtual patients are increasingly proposed as scalable alternatives to standardized patient (SP) simulation in medical education; however, robust comparative evidence using objective performance outcomes remains limited, particularly in resource-constrained contexts. Methods A mixed-methods randomized crossover study was conducted among undergraduate medical students. Participants completed both AI-driven virtual patient and SP simulation encounters across four clinical scenarios. Clinical reasoning was assessed using Key Feature Problems (KFPs) administered pre- and post-encounter, and clinical performance was evaluated using Objective Structured Clinical Examinations (OSCEs). Learner satisfaction was measured using a 5 point Likert-scale survey, and qualitative data were collected through semi-structured interviews with students and faculty. Quantitative outcomes were analyzed using paired comparisons, and qualitative data underwent thematic analysis. Results Eighty students were enrolled, and 64 completed all study components (80% response rate). Both simulation modalities were associated with significant improvements in clinical reasoning. Mean KFP scores increased following AI-driven virtual patient encounters from 58.4% (SD 9.6) to 68.9% (SD 10.2; t (63) = 7.12, p  < 0.001), and following SP encounters from 59.1% (SD 10.1) to 73.6% (SD 9.8; t (63) = 9.34, p  < 0.001). Overall OSCE performance was comparable across modalities, with mean scores of 25.24 (SD 4.26) for AI-driven virtual patients and 28.51 (SD 5.37) for SPs (out of 40), showing overlapping performance ranges. Learner satisfaction ratings were high for both modalities (overall satisfaction: AI 4.21 ± 0.58; SP 4.34 ± 0.54). Qualitative findings highlighted AI-based simulation as psychologically safe and effective for preparation and repeated practice, while SPs were valued for interpersonal realism. Conclusions AI-driven virtual patient simulation supports clinical reasoning, performance, and learner satisfaction outcomes comparable to standardized patient simulation in undergraduate medical education. These findings support a blended simulation model in which AI-based virtual patients complement SPs, offering a scalable and equitable approach to strengthening clinical skills training, particularly in resource-constrained settings.