AI-Driven Design Thinking: Transforming Learning Efficiency in Pre-Medical Education

Le résumé fourni par la source

The integration of Artificial Intelligence (AI) in pre-medical education has the potential to enhance learning efficiency, problem-solving skills, and interdisciplinary knowledge integration. However, limited research has investigated AI-driven Design Thinking approaches in this domain. This study aims to evaluate the effectiveness of AI-powered learning tools in improving student performance in Mathematics, Physics, Chemistry, and Biology among pre-medical students. A quasi-experimental study was conducted with 50 pre-medical students (ages 17–21) using a pre-test and post-test design over four weeks. Participants engaged with AI-based learning platforms (ChatGPT, OpenAI Playground, Napkin AI, and DeepSeek AI) for structured problem-solving, real-time feedback, and interactive knowledge reinforcement. Learning outcomes were assessed using paired t-tests, correlation analysis, and linear regression modeling. The findings indicate a statistically significant improvement (p < 0.001) in post-test scores across all subjects. Chemistry (R² = 0.862) and Mathematics (R² = 0.822) exhibited the highest learning gains, while Physics (R² = 0.713) and Biology (R² = 0.766) showed moderate improvements. Correlation analysis revealed a strong relationship between Mathematics and Physics (0.82), suggesting AI-assisted numerical reasoning enhanced interdisciplinary problem-solving skills. Meanwhile, Biology had the lowest correlation with Mathematics (0.74), indicating AI models need multimodal integration (e.g., virtual labs) for conceptual subjects. These results highlight AI’s potential to revolutionize pre-medical education by personalizing learning experiences, strengthening interdisciplinary connections, and improving problem-solving skills. Future AI-driven educational models should incorporate longitudinal studies, adaptive learning algorithms, and multimodal AI applications to optimize their effectiveness in conceptual and applied sciences.

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