The Forgotten Student—Generative AI Use in Medical Education and the Technology-Integrated Educational Design (TIED) Road Map: Scoping Review

Background: The growing impact of generative AI (GenAI) on medical education has given rise to many reviews discussing AI literacy, trustworthiness, potential uses, and institutional or faculty benefits. However, there has been surprisingly little review-level attention to its impact on students and learning. Objective: This scoping review examines the impact of GenAI-enabled educational interventions on undergraduate medical students. Methods: We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. An electronic search was conducted between September 22 and September 24, 2025, across Ovid (MEDLINE and Embase), PubMed, Web of Science, and Scopus, with no language or date restrictions, and excluding reviews, commentaries, letters, editorials, preprints, conference papers, and study protocols. Each record was independently screened by two reviewers, who thoroughly discussed and agreed upon its final inclusion in the analysis. We used a modified version of the Kirkpatrick model as an analytical framework for the final dataset. Results: or its sister journals (n=9). The educational interventions described included GenAI used for simulated patient experiences, knowledge or decision support, and content generation. The majority of studies fell under Kirkpatrick level 1 (n=23), focusing on initial user reactions. The studies that fell under Kirkpatrick level 2 (n=16) examined learning outcomes in terms of knowledge gain, skill acquisition, and learning attitudes. No studies were classified as Kirkpatrick level 3. Three studies demonstrated explicit theoretical grounding and therefore were classified as Kirkpatrick level 4 in our modified model. Only 13 studies spanned across levels. Numerous studies fell outside the model, including studies without student-facing interventions (119 studies) and studies focused on GenAI performance (110 studies). Although these studies were not included in the final analysis, they revealed the extent to which other topics dominate the academic conversation on GenAI in medical education. Conclusions: Our review highlights the volume of studies that prioritize tool performance over learning processes, as well as a trend toward the fragmentation of educational design elements in research evaluating GenAI-enabled educational interventions. Therefore, based on our findings, we developed the Technology-Integrated Educational Design (TIED) road map that reimagines the Kirkpatrick model as a tool to guide student-centered, theory-driven, and iterative design of technology-enabled educational interventions. By integrating preinstructional exploration, mechanism-oriented thinking, educational design, implementation, and evaluation within a single student-centered framework, the road map encourages educators and researchers to consider not only whether technology-enabled interventions work and are acceptable, but also how, for whom, and under what conditions they support meaningful learning.