Mapping the landscape of AI-assisted formative feedback in medical education: A bibliometric analysis

Artificial intelligence (AI) is transforming medical education, particularly in formative feedback. This study conducted a comprehensive bibliometric analysis to map the intellectual landscape, research trends, and future directions of AI-assisted formative feedback in medical education.

A systematic search was performed in the Web of Science Core Collection database for English-language articles published between January 1, 2021, and October 12, 2025. Bibliometric analysis was conducted using VOSviewer, CiteSpace, and the R-bibliometrix package to analyze publication trends, geographic distribution, institutional collaboration, journal impact, author contributions, co-citation patterns, and keyword occurrences.

The analysis included 116 publications, revealing exponential growth in AI-assisted formative feedback research from 2021 to 2025. The United States dominated the research landscape, followed by China and other European nations. Institutional collaboration centered on the University of Michigan, connecting North American and Asian research clusters. BMC Medical Education emerged as the leading journal, while interdisciplinary knowledge flow originated from clinical medicine and drew on health sciences and educational psychology. Keyword bursts identified "feedback," "large language model," and "medical education" as the most prominent research hotspots in 2024 to 2025.

AI-assisted formative feedback in medical education is a rapidly evolving field driven by advancements in large language models, immersive technologies, and personalized assessments. Future research should prioritize theory-informed, ethically grounded, and patient-oriented AI integration to augment human instruction and demonstrably improve learner competence and patient care. Increased international collaboration and interdisciplinary knowledge exchange are crucial for the responsible adoption of AI in medical education.