| Sumario: | This study presents the redesign of a Python programming course for medical students at the University of Pavia, integrating the Generative AI (GenAI) tool Gemini within Google Colab to support learning and problem-solving. To evaluate students' experiences, attitudes, and learning strategies, a two-part postcourse questionnaire was developed, combining an adapted Technology Acceptance Model (TAM) and the Learning Strategies Scale for Large Language Models (LSS/LLMs-6). A panel of domain experts assessed the questionnaire's content validity, yielding strong overall agreement (CVI = 0.98). The results highlighted the TAM's robustness and identified minor refinements needed for the LSS/LLMs-6 to better distinguish constructive (metacognitive) from dysfunctional (passive) GenAI use. This preliminary work, limited by the small expert panel and the absence of empirical data, sets the foundation for a larger study to be conducted at the end of the 2025/2026 academic year. Overall, the integration of GenAI in medical programming education offers a promising yet delicate opportunity to democratize access to computational tools while ensuring that AI acts as a means to enhance reasoning rather than replace it.
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