AI-Driven Personalized Learning in Medical Education: Enhancing Cognitive Skills and Addressing Language Proficiency Challenges

Authors

  • Gang Tao

    School of Foreign Language Studies, Wenzhou Medical University, Wenzhou 325035, China
  • Yu Pan

    School of Foreign Language Studies, Wenzhou Medical University, Wenzhou 325035, China

DOI:

https://doi.org/10.30564/fls.v7i4.8932
Received: 3 March 2025 | Revised: 28 March 2025 | Accepted: 1 April 2025 | Published Online: 8 April 2025

Abstract

This study examines the effectiveness of artificial intelligence-driven (AI-driven) personalized learning strategies in enhancing cognitive skills and language proficiency in medical education. By integrating AI tools into practical scenarios, such as clinical simulations and patient interactions, the research highlights AI’s potential to foster critical thinking, decision-making, and personalized learning experiences. A mixed-methods approach, combining pre- and post-intervention assessments, questionnaires, and interviews, reveals that while AI significantly improves cognitive skills, its impact on language proficiency is modest. A major contribution of this study is the proposed PCLC-AI Framework (Personalized Cognitive and Linguistic Competency Development with Artificial Intelligence), which aligns AI-driven learning with curriculum objectives to enhance both cognitive and linguistic competencies. The framework incorporates adaptive learning paths, interactive simulations, and integrated feedback mechanisms, offering a cohesive approach to skill development. Despite its potential, the study identifies challenges, including cultural adaptability, reliance on a single AI model, and the limited duration of the intervention. Findings thus underscore the need for longer-term studies and localized AI training datasets. By addressing critical gaps in the literature, this research provides actionable insights into optimizing AI integration for medical education.

Keywords:

AI-Driven Personalized Learning; Medical Education; Cognitive Skills; Language Proficiency; Curriculum Integration; AI Learning Framework

References

[1] University Foreign Language Teaching Advisory Board, 2020. Guidelines for College English Teaching (2020 Edition). Higher Education Press: Beijing, China.

[2] U.S. Department of Education, 2017. Reimagining the Role of Technology in Education: 2017 National Education Technology Plan Update. U.S. Department of Education: Washington, D.C., USA.

[3] UNESCO, 2019. AI in education: Guidance for policy-makers. Available from: https://unesdoc.unesco.org/ark:/48223/pf0000373434 (cited 23 February 2025).

[4] Alloway, T.P., Alloway, R.G., 2010. Investigating the predictive roles of working memory and IQ in academic attainment. Journal of experimental child psychology. 106(1), 20–29.

[5] Xie, H., Chu, H.C., Hwang, G.J., et al., 2019. Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education. 140, 103599.

[6] Abrami, P.C., Bernard, R.M., Borokhovski, E., et al., 2015. Strategies for teaching students to think critically: A meta-analysis. Review of educational research. 85(2), 275–314.

[7] Segalowitz, N., 2010. Cognitive Bases of Second Language Fluency. Routledge: New York, NY, USA. DOI: https://doi.org/10.4324/9780203843372.

[8] Qu, X., Yang, J., Chen, T., 2023. Reflections on the changes to medical education models induced by ChatGPT. Journal of Sichuan University (Medical Science Edition). 5, 937–940.

[9] Tao, J., Yu, Z., Pi, H., 2023. A preliminary analysis of the impact of artificial intelligence, exemplified by ChatGPT, on medical education. Journal of Mathematical Medicine. 6, 475-480, 2023.

[10] Wang, X., Wu, X., Lin, Q., et al., 2024. Exploration of the application of ChatGPT in medical teaching. China Medical Education & Technology. 1, 70–74, 86. DOI: https://doi.org/10.13566/j.cnki.cmet.cn61-1317/g4.202401010.

[11] He, M., Wang, S., Ding, R., et al., 2024. Exploration of medical teaching models based on the integration of New Medicine and AI technology. Medical Science Educator. 3, 63–66. DOI: https://doi.org/10.16500/j.cnki.1673-498x.2024.03.015.

[12] Cai, H., Hu, X. Li, J., 2021. The persistence and innovation of teaching reforms in medical schools in the era of artificial intelligence. China Continuing Medical Education. 29, 4–8.

[13] Chen, X., Deng, R., Wu, C., 2024. Discussion on the application of generative AI large language models in medical education practice. Journal of Clinical Emergency. 6, 310–314. DOI: https://doi.org/10.13201/j.issn.1009-5918.2024.06.007.

[14] Li, W. , Tang, J. Qu, Y., 2019. Application and development of artificial intelligence in medical education. Traditional Chinese medicine. 1, 17–18+60.

[15] Zhao, M., 2020., Reflections and explorations on enhancing medical students' AI literacy in the context of new medicine. In Proceeding of 4th Jiang-Zhe-Hu-Wan Medical education annual conference and 2020 Zhejiang Medical Association Medical Education Academic Conference. 19 November 2020. Wenzhou, China. pp. 228–230. DOI: https://doi.org/10.26914/c.cnkihy.2020.071350

[16] Wang, J., 2024. Survey research on medical students' demand for artificial intelligence courses under the context of New Medicine. Cultural Studies Of Science Education. 15, 90–94. DOI: https://doi.org/10.16871/j.cnki.kjwh.2024.15.021.

[17] Kang, N., Hao, Y., Li, F. et al., 2024. Construction of an AI literacy capability framework for medical students. Journal of Librarianship and Information Science. 3, 46–51.

[18] Diao, K., Shan, Y., Huang, Y., et al., 2023. Analysis of ethical issues in the application of artificial intelligence in medical education. Medical Education Management. 1, 122–126.

[19] Zou, L., Gu, Y., Chen, C., 2019. Changes in medical education in the era of artificial intelligence and the importance of humanities education. Southwest Defense Medicine. 5, 623–624.

[20] Shao, H. Liu, Y. Zhang, A. et al., 2024. Development and application of an AI-based empathic language teaching and evaluation system for doctor-patient communication. Chinese General Practice. 34, 4315–4321.

[21] Han, H., Lü, F., Wang, Q., 2024. Application and exploration of artificial intelligence technology in medical talent training—A case study of Capital Medical University. China Medical Education Technology. 3, 261–265+270. DOI: https://doi.org/10.13566/j.cnki.cmet.cn61-1317/g4.202403001.

[22] Zhou, P., Zhang, L., Wang, X. et al., 2024. Exploration of medical students' research innovation capabilities under the ‘AI + X' model. Medical Education and Practice. 3, 252–255+278, 2024. DOI: https://doi.org/10.13555/j.cnki.c.m.e.2024.03.002.

[23] Jin, T., Piao, J., Yang, Y., 2024. Research on the application of artificial intelligence in medical education—Based on a CiteSpace bibliometric analysis. Philosophy of Medicine. 2, 72–75.

[24] Huang, F., Zhang, T., 2023. Enhancing the teaching competencies of medical educators in the context of artificial intelligence. Medical Education Research and Practice. 1, 7–10. DOI: https://doi.org/10.13555/j.cnki.c.m.e.2023.01.002.

[25] Piaget, J., 1954. The Construction of Reality in the Child. Basic Books: New York, NY, USA.

[26] Jonassen, D.H., Land, S.M., 2012. Theoretical Foundations of Learning Environments. Routledge: London, UK.

[27] Davis, F.D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 3, 319–340. DOI: https://doi.org/10.2307/249008.

[28] Venkatesh, V., Davis, F.D., 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science. 46(2), 186–204.

[29] Creswell, J.W., Plano, C.V.L., 2007. Designing and Conducting Mixed Methods Research. Sage Publications: Thousand Oaks, CA, USA.

[30] Polit, D.F., Beck, C.T., 2010. Essentials of Nursing Research: Appraising Evidence for Nursing Practice. Wolters Kluwer Health/Lippincott Williams & Wilkins: Philadelphia, PA, USA. DOI:https://doi.org/10.4236/ojapps.2023.136066

[31] Teddlie, C., Yu, F., 2007. Mixed methods sampling: A typology with examples. Journal of mixed methods research. 1(1), 77–100.

[32] Apple, M.W., 2013. Can Education Change Society? Routledge: London, UK. DOI:https://doi.org/10.4324/9780203083550

[33] Hyland, K., 2007. Genre and Second Language Writing. The University of Michigan Press: Ann Arbor, MI, USA. DOI: https://doi.org/10.3998/mpub.23927

[34] Bain, K., 2004. What the Best College Teachers Do. Harvard University Press： Cambridge, MA， USA.

[35] Fink, A., 2017. Conducting Research Literature Reviews: From the Internet to Paper. Sage Publications: Thousand Oaks, CA, USA.

[36] Rubin, H.J., Rubin, I.S., 2012. Qualitative Interviewing: The Art of Hearing Data. Sage Publications: Thousand Oaks, CA, USA.

[37] Field, A., 2013. Discovering Statistics Using IBM SPSS Statistics. Sage Publications: Thousand Oaks, CA, USA.

[38] Braun, V.,Clarke, V., 2006. Using thematic analysis in psychology. Qualitative research in psychology. 3(2), 77–101.

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How to Cite

Tao, G., & Pan, Y. (2025). AI-Driven Personalized Learning in Medical Education: Enhancing Cognitive Skills and Addressing Language Proficiency Challenges. Forum for Linguistic Studies, 7(4), 366–384. https://doi.org/10.30564/fls.v7i4.8932

Issue

Vol. 7 , Iss. 4 (April 2025): In Progress

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Article

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Copyright © 2025 Gang Tao, Yu Pan

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.