Vibe-coding como competencia docente en el uso de Inteligencia Artificial para la Educación Médica: una revisión de alcance

Josemaria Elizondo-García

doi:10.6018/edumed.705121

Authors

Josemaria Elizondo-García Tecnológico de Monterrey, Mexico https://orcid.org/0000-0002-4743-7096

DOI: https://doi.org/10.6018/edumed.705121

Keywords: Vibe-coding, Simulators, Generative Artificial Intelligence, TPACK, AI-assisted programming

Abstract

Introduction: The emergence of Generative Artificial Intelligence has given rise to vibe-coding, a paradigm that allows educators without technical backgrounds to develop software using natural language. This advancement necessitates redefining the teaching competencies required to transition from users to creators of educational tools. Methods: A scoping review of the literature was conducted following PRISMA-ScR guidelines. Studies retrieved from Web of Science and Scopus databases addressing the use of vibe-coding by educators in higher and medical education were included. After the selection process, four publications from 2025 were included. A deductive reflexive thematic analysis was applied to classify findings according to the TPACK model dimensions (Technological, Pedagogical, and Content Knowledge). Results: Qualitative analysis reveals a reconfiguration of Technological Knowledge (TK), which dissociates from programming syntax to focus on structuring prompts and iterative dialogue with AI. The emergence of the “clinician-developer” role was identified, where Content Knowledge (CK) acts as an indispensable auditing mechanism to validate clinical accuracy and prevent hallucinations in generated algorithms. Technological-Pedagogical Knowledge (TPK) enabled educators to deconstruct complex logic into sequential steps to design simulators and personalized tools. Conclusions: Preliminary evidence suggests that vibe-coding democratizes educational software development, allowing educators to materialize complex pedagogical strategies without external technical dependence. This competency appears to demand robust disciplinary mastery to guarantee the safety and quality of the created resources. It is recommended to foster training in this emerging competency and conduct future empirical studies to evaluate the impact of these tools on student learning and teacher cognitive load.

Downloads

Download data is not yet available.

Metrics

Views/Downloads

Abstract
13
pdf
5
XML
0

References

1. Nair HB. Building Teacher Capacity for Effective Integration of GenAI into Classroom: A Framework for Teacher Education Programs. In Generative Artificial Intelligence Empowered Learning. Chapman and Hall/CRC. 2025, 192-206. https://doi.org/10.1201/9781003422433-9

2. Park J. A systematic literature review of generative artificial intelligence (GenAI) literacy in schools. Computers and Education: Artificial Intelligence. 2025, 100487. https://doi.org/10.1016/j.caeai.2025.100487

3. Treceñe JKD, Patiga ROA, Odal BB, Tapales RG, Tuano EMD, Dayaon MC. The Emergence of Generative AI in Higher Education: Exploring the Perceived Challenges Among Teachers and Students. In Pitfalls of AI Integration in Education: Skill Obsolescence, Misuse, and Bias. IGI Global Scientific Publishing. 2025, 71-94.

4. Ofosu-Asare Y. Guiding the future: developing an ethical framework for generative AI use in education. The International Journal of Information and Learning Technology. 2025, 1-19. https://doi.org/10.1108/IJILT-06-2024-0113

5. Somabut A, Tuamsuk K, Lowatcharin G, Traiyarach S, Kwangmaung P. Preparing for the AI Era: Science Teachers’ Readiness and Professional Development Needs for Generative AI Integration in Secondary Education. Social Science & Humanities Open. 2025, 12, 102259. https://doi.org/10.1016/j.ssaho.2025.102259

6. Symeou L, Louca L, Kavadella A, Mackay J, Danidou Y, Raffay V. Development of Evidence‑Based Guidelines for the Integration of Generative AI in University Education Through a Multidisciplinary, Consensus‑Based Approach. European Journal of Dental Education. 2025, 29(2), 285-303. https://doi.org/10.1111/eje.13069

7. Zou D, Xie H, Kohnke L. Navigating the Future: Establishing a Framework for Educators’ Pedagogic Artificial Intelligence Competence. European Journal of Education. 2025, 60(2), e70117. https://doi.org/10.1111/ejed.70117

8. Ravi M. Using the TPACK Framework for Gen-AI Enabled Learning Activities: Design, Delivery and Evaluation. Journal of Engineering Education Transformations. 2025, 175-181. https://doi.org/10.16920/jeet/2025/v38is2/25021

9. Masfuah S, Fakhriyah F, Hilyana FS, Kiong TT. The effect of technological pedagogy knowledge and technological content knowledge on TPACK of primary prospective teachers. Multidisciplinary Science Journal. 2024, 6(10), 2024188-2024188. https://doi.org/10.31893/multiscience.2024188

10. Diaz SS, Carmona RJC, Escorcia IAP. Caracterización del conocimiento tecnológico del contenido (TCK): un estudio de caso con profesores de matemáticas en formación inicial. Educación Y Humanismo. 2022, 24(42), 9. https://doi.org/10.17081/eduhum.24.42.5142

11. Lan G, Feng X, Du S, Song F, Xiao Q. Integrating ethical knowledge in generative AI education: constructing the GenAI-TPACK framework for university teachers’ professional development. Education and Information Technologies. 2025, 1-24. https://doi.org/10.1007/s10639-025-13427-6

12. Mgeladze A, Kapanadze M, Chakhaia L. From Measuring to Action: The Next Steps in Physics Teachers’ Technological Pedagogical Content Knowledge. Science Education International. 2024, 35(4), 429-438. https://doi.org/10.33828/sei.v35.i4.13

13. Aldemir T, Kilinc S, Bicer A, Grant P, Davis T, Sweany NW. Intelligent‑TPACK in practice: design and evidence from a three‑week teacher preparation module. Computers and Education Open. 2025, 100306. https://doi.org/10.1016/j.caeo.2025.100306

14. Celik I. Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior. 2023, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468

15. Ocak C, Caskurlu S. Unpacking Ethics-Domain of Intelligent-TPACK Scale In Relation to In-Service Teachers’ Trust and Distrust. Computers and Education Open. 2025, 100321. https://doi.org/10.1016/j.caeo.2025.100321

16. Rokis K, Kirikova M. Challenges of low-code/no-code software development: A literature review. In International Conference on Business Informatics Research. Cham: Springer International Publishing. 2022, 3-17. https://doi.org/10.1007/978-3-031-16947-2_1

17. De Silva D, Mills N, Issadeen Z, Moraliyage H, Jennings A, Manic M. Generative AI Vibe Coding for Prototyping Industrial Systems. In 2025 IEEE 34th International Symposium on Industrial Electronics (ISIE). 2025, 1-6. IEEE. https://doi.org/10.1109/ISIE62713.2025.11124737

18. Lee Y, Huh S. How can clinicians leverage vibe coding for machine learning and deep learning research? Endocrinology and Metabolism. 2025, 40(5), 659-667. https://doi.org/10.3803/EnM.2025.2675

19. Meske C, Hermanns T, von der Weiden E, Loser KU, Berger T. Vibe coding as a reconfiguration of intent mediation in software development: Definition, implications, and research agenda. arXiv preprint arXiv:2507.21928. 2025. https://doi.org/10.48550/arXiv.2507.21928

20. Ng O, Chow M. Empowering Health Professions Educators: Developing Educational Tools with AI-Assisted Vibe Coding. Medical Science Educator. 2025, 1-6. https://doi.org/10.1007/s40670-025-02596-1

21. Sarkis-Onofre R, Catalá-López F, Aromataris E, Lockwood C. How to properly use the PRISMA Statement. Systematic Reviews. 2021, 10(1), 117. https://doi.org/10.1186/s13643-021-01671-z

22. Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, ... Straus SE. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Annals of Internal Medicine. 2018, 169(7), 467-473. https://doi.org/10.7326/M18-0850

23. Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Research in Psychology. 2006, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa

24. Braun V, Clarke V. Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health. 2019, 11(4), 589-597. https://doi.org/10.1080/2159676X.2019.1628806

25. Braun V, Clarke V. One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology. 2021, 18(3), 328-352. https://doi.org/10.1080/14780887.2020.1769238

26. Anis S, French JA. Efficient, explicatory, and equitable: Why qualitative researchers should embrace AI, but cautiously. Business & Society. 2023, 62(6), 1139-1144. https://doi.org/10.1177/00076503231163286

27. Bennis I, Mouwafaq S. Advancing AI-driven thematic analysis in qualitative research: a comparative study of nine generative models on Cutaneous Leishmaniasis data. BMC Medical Informatics and Decision Making. 2025, 25(1), 1-14. https://doi.org/10.1186/s12911-025-02961-5

28. Chow M, Ng O. From technology adopters to creators: Leveraging AI-assisted vibe coding to transform clinical teaching and learning. Medical Teacher. 2025, 1-3. https://doi.org/10.1080/0142159X.2025.2488353

29. Pesce F, Cheungpasitporn W. Vibe Coding in nephrology education: clinician-led, AI-assisted development of open-source interactive learning tools. Renal Failure. 2025, 47(1), 2581933. http://doi.org/10.1080/0886022X.2025.2581933

30. Shin D, Lee JH, Lee E, Lee Y. No-code approaches to developing ELT resources with generative artificial intelligence. ELT Journal. 2025, ccaf050. https://doi.org/10.1093/elt/ccaf050

31. Sun D, Ba S, Cha Y, Yu J, Chiang F, Dai H, Lim C. Empowering University Teachers in Higher Education: A Generative AI-Responsive Competency Framework. Computers and Education: Artificial Intelligence. 2026, 10, 100542. https://doi.org/10.1016/j.caeai.2026.100542

32. Lakhe Shrestha BL, Dahal N, Hasan MK, Paudel S, Kapar H. Generative AI on professional development: a narrative inquiry using TPACK framework. Frontiers in Education. 2025, 10, 1550773. https://doi.org/10.3389/feduc.2025.1550773