Integrating Generative AI-Based Assistance Tool in Programming Education for Medical Students: A Cross-Sectional Study

Backgroud With the increasing importance of computational skills in healthcare, there is a growing need to equip medical students with programming knowledge to address complex healthcare challenges effectively. Traditional programming methods, however, can be challenging for medical students due to heavy academic loads and limited exposure to coding. Generative artificial intelligence (GenAI) presents a promising solution to these issues. Methods This research evaluated the feasibility of Chat2R, an R programming assistance tool powered by GenAI, within the postgraduate course “Practical Techniques of Medical Data Mining” at Peking Union Medical College (PUMC). A mixed-methods approach was used, combining quantitative surveys and qualitative insights to assess the tool’s effectiveness and students’ reception. Quantitative data was gathered through post-implementation surveys measuring students' perceptions of their coding proficiency and the tool’s utility. Qualitative analysis explored student interactions with Chat2R, identifying key challenges and concerns to enhance the educational experience. Results A total of 31 postgraduate students from 14 different disciplines participated in the survey. The positive feedback supported the integration of Chat2R as a valuable educational tool, highlighting GenAI’s role in enhancing computational thinking skills. Between March 13 and April 2, 2024, 28 students actively engaged with Chat2R, generating 1,603 questions. Of these, 311 were specifically related to defined data analysis processes: 138 questions on data collection, 74 on data preprocessing, 4 on data exploration, 87 on data visualization, and 8 to data modeling. Conclusion This study demonstrates that integrating Chat2R, a GenAI-based programming tool, can significantly enhance programming education for medical students. It improves computational thinking, supports both lecture and practical learning, and addresses challenges related to limited coding exposure. Positive student feedback highlights its effectiveness in providing coding assistance and fostering an interactive, student-centered learning environment. The findings also underscore the importance of professional development for educators to effectively incorporate GenAI tools into teaching. Future enhancements could include AutoML capabilities, enabling medical students to guide AI-driven data analysis and better utilize AI in clinical and research contexts.