AI-Driven Design Thinking: Transforming Learning Efficiency in Pre-Medical Education
Main Article Content
Abstract
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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References
https://www.academia.edu/download/99170672/EIMJ20211303_05.pdf
Althewini, A., & Alobud, O. (2024). Exploring medical students' attitudes on computational thinking in a Saudi university: Insights from a factor analysis study. Frontiers in Education. Retrieved from
https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2024.1444810/full
Benor, D. E. (2014). A new paradigm is needed for medical education in the mid-twenty-first century and beyond: Are we ready? Rambam Maimonides Medical Journal. Retrieved from
https://pmc.ncbi.nlm.nih.gov/articles/PMC4128589/
Berg, C., Philipp, R., & Taff, S. D. (2023). Scoping review of critical thinking literature in healthcare education. Occupational Therapy in Health Care. Retrieved from
https://www.tandfonline.com/doi/abs/10.1080/07380577.2021.1879411
Brown, K. (2022). Integration and evaluation of virtual reality in distance medical education. ProQuest Dissertations and Theses. Retrieved from
https://search.proquest.com/openview/13fc81a5cd80420feb9d32911a70b889/1?pq-origsite=gscholar&cbl=18750&diss=y
Brown, T. (2009). Change by design: How design thinking creates new alternatives for business and society. Harper Business.
Coles, C. R. (1985). A study of the relationships between curriculum and learning in undergraduate medical education. Medical Education. Retrieved from https://eprints.soton.ac.uk/461653/
Ekvitayavetchanukul, P., & Ekvitayavetchanukul, P. (2023). Comparing the effectiveness of distance learning and onsite learning in pre-medical courses. Recent Educational Research, 1(2), 141-147.
https://doi.org/10.59762/rer904105361220231220143511
Ekvitayavetchanukul, P., & Ekvitayavetchanukul, P. (2025). Artificial intelligence-driven design thinking: Enhancing learning efficiency in pre-medical education. Educación XX1. Retrieved from
https://educacionxx1.net/index.php/edu/article/view/51
Ekvitayavetchanukul, P., Bhavani, C., Nath, N., Sharma, L., Aggarwal, G., & Singh, R. (2024). Revolutionizing healthcare: Telemedicine and remote diagnostics in the era of digital health. In Kumar, P., Singh, P., Diwakar, M., & Garg, D. (Eds.), Healthcare industry assessment: Analyzing risks, security, and reliability (pp. 255–277). Springer.
Fehsenfeld, E. A. (2015). The role of the medical humanities and technologies in 21st-century undergraduate medical education curriculum. EducationXX1 Journal. Retrieved from
https://digitalcollections.drew.edu/UniversityArchives/ThesesAndDissertations/CSGS/DMH/2015/Fehsenfeld/openaccess/EAFehsenfeld.pdf
Gandhi, R., Parmar, A., Kagathara, J., & Lakkad, D. (2024). Bridging the artificial intelligence (AI) divide: Do postgraduate medical students outshine undergraduate medical students in AI readiness? Cureus. Retrieved from
https://www.cureus.com/articles/285501-bridging-the-artificial-intelligence-ai-divide-do-postgraduate-medical-students-outshine-undergraduate-medical-students-in-ai-readiness.pdf
Hernandez, A., & Lee, R. (2024). Evaluating the role of AI in self-directed learning among pre-medical students: A comparative analysis. Medical Education Research & Development, 41(3), 301–319.
Hickey, H. (2022). Canadian Conference on Medical Education 2022 abstracts. Canadian Medical Education Journal. Retrieved from https://journalhosting.ucalgary.ca/index.php/cmej/article/download/75002/55807
Jiang, X., & Wang, T. (2024). AI-assisted learning models for enhancing medical students' conceptual understanding of human physiology. International Journal of Medical Education, 55(4), 231–245.
Kawintra, T., Kraikittiwut, R., Ekvitayavetchanukul, P., Muangsiri, K., & Ekvitayavetchanukul, P. (2024). Relationship between sugar-sweetened beverage intake and the risk of dental caries among primary school children: A cross-sectional study in Nonthaburi Province, Thailand. Frontiers in Health Informatics, 13(3), 1716-1723.
Kumar, P., & Singh, M. (2024). A comparative study of AI-driven and instructor-led education in pre-medical chemistry courses. Computers & Education, 177, 104326.
Labov, J. B., Reid, A. H., & Yamamoto, K. R. (2010). Integrated biology and undergraduate science education: A new biology education for the twenty-first century. CBE—Life Sciences Education. Retrieved from https://www.lifescied.org/doi/abs/10.1187/cbe.09-12-0092
Lopez, D., & Chen, H. (2024). Machine learning and human reasoning: AI applications in cognitive learning for medical students. Artificial Intelligence in Medicine, 88(1), 112–130.
Nguyen, T., Rienties, B., & Richardson, J. T. E. (2020). Learning analytics to uncover inequalities in blended and online learning. Distance Education, 41(4), 540–561. https://doi.org/10.1080/01587919.2020.1821604
Rossi, G., & Patel, S. (2024). The future of AI-enhanced medical learning: Challenges and opportunities in integrating design thinking. Journal of Educational Computing Research, 72(3), 311–329.
Singh, J., Kumar, V., Sinduja, K., Ekvitayavetchanukul, P., Agnihotri, A. K., & Imran, H. (2024). Enhancing heart disease diagnosis through particle swarm optimization and ensemble deep learning models. In Nature-inspired optimization algorithms for cyber-physical systems (pp. XX–XX). IGI Global.
https://www.igi-global.com/chapter/enhancing-heart-disease-diagnosis-through-particle-swarm-optimization-and-ensemble-deep-learning-models/364785
Smith, J., & Andersson, C. (2024). AI-based decision-support tools in problem-based learning: The case of pre-medical physics education. Journal of Medical Learning Technologies, 50(1), 87–104.
Taylor, D., & Miflin, B. (2008). Problem-based learning: Where are we now? Medical Teacher. Retrieved from
https://www.tandfonline.com/doi/abs/10.1080/01421590802217199
Taylor, B., & Huang, Y. (2023). Integrating AI-based question-answering systems in pre-medical biology education: Benefits and challenges. Journal of Science Education & Technology, 32(5), 598–612.
Tao, Z., Werry, I. P., Zeng, Z., & Miao, Y. (2024). The role and value of generative AI in medical education and training. International Journal of Information Technology. Retrieved from
https://intjit.org/cms/journal/volume/29/1/291_4.pdf
Venkataraman, S., & Lopez, J. (2024). AI in medical education: Understanding student engagement and learning efficiency. Medical Informatics Review, 36(2), 221–239.
Vimal, V., & Mukherjee, A. (2024). Multi-strategy learning environments: Integrating AI in STEM education. Springer Nature. Retrieved from https://link.springer.com/content/pdf/10.1007/978-981-97-1488-9.pdf
Zhai, X., & Krajcik, J. (2024). Artificial intelligence-based STEM education: How AI tools can revolutionize learning. Springer Nature. Retrieved from https://books.google.com/books?hl=en&lr=&id=JYQoEQAAQBAJ
Zhou, L., & Patel, R. (2023). The impact of AI-driven personalized tutoring systems on anatomy learning for pre-medical students. Advances in Health Sciences Education, 38(2), 145–163.