AI-Generated Multiple Mini Interview (MMI) Stations for Medical School Admissions: Psychometric Evaluation.

BACKGROUND: Multiple mini interviews (MMIs) are widely used in medical school admissions to assess applicants' nonacademic attributes in a structured and reliable manner. However, the development of high-quality MMI stations is resource intensive and dependent on expert input. OBJECTIVE: This study explored the utility of artificial intelligence (AI) in the generation of MMI stations for the Direct and Graduate Entry Medicine Program admissions process for domestic applicants at Monash Medical School. To our knowledge, this study represents the first empirical evaluation of AI-generated MMI stations deployed in a real-world medical school admissions context. METHODS: A total of 56 MMI stations from the 2025 admissions cycle were evaluated, including 17 (30.4%) AI-generated and 39 (69.6%) traditionally developed stations, administered across 824 domestic applicants for a total of 4897 applicant-station interactions. We assessed station quality through both reliability (using Cronbach α to examine internal consistency) and discrimination capability (using SD and range of scores) at the station level. RESULTS: AI-generated stations exhibited slightly higher reliability (α=0.82) compared with traditional stations (α=0.81), though this difference was not statistically significant (P=.91). Both AI-generated and traditionally developed stations demonstrated variable discrimination capability, with some stations from each development method showing excellent combinations of high reliability and strong discriminatory power, while others exhibited ceiling effects that limited their discriminatory power. Of note, a greater proportion of AI-generated stations were classified as optimal (α>0.85), and a smaller proportion were classified in the review category (α<0.75), compared with traditional stations. These results suggest that AI-generated stations can achieve psychometric performance comparable to traditionally developed stations. CONCLUSIONS: Our findings highlight the utility of AI as a useful tool for MMI station generation, offering a scalable approach that may reduce the resource burden on faculty while maintaining or enhancing psychometric quality for applicants. Ongoing quality assurance and evaluation remain essential to ensure fairness and validity across the admissions process.