Juegos didácticos con inteligencia artificial: transformando la táctica deportiva desde el silbato al algoritmo

Authors

Abstract

The integration and collaboration of emerging technologies in physical education represent both a challenge and an opportunity to transform traditional pedagogical practices. This study aimed to analyze the design process of educational games mediated by generative artificial intelligence (AI) tools, specifically Gemini and Canva Code, within the context of sports training. A qualitative approach with a descriptive-exploratory design was implemented. A co-creation workshop was conducted with professors and students, where participants at the Mexico-Colombia international agreement conference utilized prompt engineering to build recreational-educational resources aimed at developing tactical thinking and spatial memory. The findings demonstrate that AI does not replace the teacher; instead, it acts as a "creative co-pilot" that enhances the ability to design complex cognitive environments. Furthermore, the research validated a reproducible four-phase technical pathway based on the Role + Context + Task + Format structure, facilitating techno-pedagogical autonomy in the classroom. It is concluded that the ethical and pedagogically grounded use of these tools democratizes the creation of analytical resources in sports, increasing student motivation and engagement. These results highlight the relevance of transitioning from traditional methods toward a "Smart PE" model, where technology empowers decision-making and real-time feedback.

Author Biography

FELIPE HERNANDEZ ROMERO, Corporación Universitaria Latinoamericana

Corporación Universitaria Latinoamericana

References

An, Y., & James, S. (2025). Generative AI integration in K-12 settings: Teachers' perceptions and levels of integration. TechTrends, 69(3), 1123. https://doi.org/10.1007/s11528-025-01130-9

Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7-74. https://doi.org/10.1080/0969595980050102

Chen, L., et al. (2022). Artificial intelligence–assisted physical education: Adaptive learning and performance feedback. Computers & Education, 189, 104596. https://doi.org/10.1016/j.compedu.2022.104596

Dichev, C., & Dicheva, D. (2017). Gamifying education: What is known, what is believed and what remains uncertain: A critical review. International Journal of Educational Technology in Higher Education, 14(1), 9. https://doi.org/10.1186/s41239-017-0042-5

Gee, J. P. (2007). What video games have to teach us about learning and literacy (2nd ed.). Palgrave Macmillan.

Hattie, J. (2008). Visible learning (0 ed.). Routledge. https://doi.org/10.4324/9780203887332.

Kao, C. C. (2020). The effects of gamification on physical education students' learning motivation and engagement. Journal of Teaching in Physical Education, 39(4), 512-521. https://doi.org/10.1123/jtpe.2019-0212

Kapp, K. M. (2012). The gamification of learning and instruction: Game-based methods and strategies for training and education. Pfeiffer.

Lee, H. S. (2021). Chatbot-assisted learning of sports rules in physical education. Journal of Teaching in Physical Education, 40(3), 456-465. https://doi.org/10.1123/jtpe.2020-0158

Memmert, D., & Harvey, S. (2009). The game performance assessment instrument (GPAI): Some concerns and solutions for further development. Journal of Teaching in Physical Education, 28(2), 220-240. https://doi.org/10.1123/jtpe.28.2.220

Memmert, D., & Perl, J. (2015). Game creativity: Theory and practice in team sports. Meyer & Meyer Sport.

Palao, J. M., et al. (2022). Virtual reality applications in tactical sports training: A systematic review. Journal of Teaching in Physical Education, 41(2), 298-310. https://doi.org/10.1123/jtpe.2021-0123

Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books.

Pérez-López, I. J., et al. (2021). Gamification in physical education: A systematic review. Journal of Teaching in Physical Education, 40(1), 112-124. https://doi.org/10.1123/jtpe.2019-0256

Qian, Y. (2025). Prompt engineering in education: A systematic review of approaches and educational applications. Journal of Educational Computing Research, 63(7-8), 1782-1818. https://doi.org/10.1177/07356331251320654

Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press.

Scherer, R., et al. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.

Serra, P., & Oliveira, Â. (2025). AI-powered prompt engineering for education 4.0: Transforming digital resources into engaging learning experiences. Education and Information Technologies, 30(2), 1897-1925. https://doi.org/10.1007/s10639-024-13145-2

White, J., et al. (2023). A prompt pattern catalog to enhance prompt engineering with ChatGPT. arXiv preprint arXiv:2302.11382. https://doi.org/10.48550/arXiv.2302.11382

Published

2026-07-04

How to Cite

Ledezma Velazco, M., HERNANDEZ ROMERO, F., & Ozuna, J. (2026). Juegos didácticos con inteligencia artificial: transformando la táctica deportiva desde el silbato al algoritmo. Movimiento Y Desarrollo De La Pedagogía Y Educación Física, 4(1). Retrieved from https://revistas.ul.edu.co/index.php/MODEF/article/view/124