Artificial Intelligence-Driven Design Thinking: Enhancing Learning Efficiency in Pre-Medical Education
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Artificial Intelligence (AI) has emerged as a transformative tool in education, providing innovative approaches to problem-solving and adaptive learning. Design Thinking, an iterative, human-centered approach to learning, fosters critical thinking and creative problem-solving skills. However, limited research has explored the role of AI-driven Design Thinking in pre-medical education, particularly in Mathematics, Physics, Chemistry, and Biology. This study investigates how AI-assisted learning tools—ChatGPT, OpenAI, Napkin AI, and DeepSeek AI—enhance learning efficiency and problem-solving abilities in pre-medical students.
A quasi-experimental study was conducted with 50 pre-medical students (ages 17–21), employing a pre-test and post-test design. The students underwent AI-based learning interventions for four weeks, followed by assessments and surveys to measure their academic performance and engagement. The statistical analysis, including paired t-tests and correlation analysis, confirmed significant improvements (p < 0.001) in student performance across all subjects. The findings highlight AI’s potential in enhancing problem-solving efficiency, engagement, and conceptual understanding in pre-medical education.
This study provides empirical evidence supporting the integration of AI-driven Design Thinking models in STEM curricula, offering insights for educators and policymakers in shaping future AI-enhanced medical education strategies.
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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
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