Sistemas de aprendizagem adaptativa com IA e engagement multidimensional na Educação Física online: grandes efeitos

Autores

DOI:

https://doi.org/10.47197/retos.v80.119205

Palavras-chave:

Sistema de aprendizagem adaptativa com inteligência artificial, educação física online, envolvimento na aprendizagem, experiência de aprendizagem, efeito mediador

Resumo

Introdução: A educação física online expandiu-se consideravelmente desde a pandemia de COVID-19, mas enfrenta desafios para manter o envolvimento estável dos alunos. Os sistemas de aprendizagem adaptativa baseados em IA oferecem uma solução promissora, embora a investigação empírica sobre os seus efeitos na educação física seja ainda insuficiente.

Objectivo: Este estudo teve como objectivo analisar como os sistemas de aprendizagem adaptativa baseados em IA influenciam o envolvimento dos alunos na educação física online e examinar o papel mediador da experiência de aprendizagem.

Metodologia: Foi utilizado um desenho quase experimental com 203 alunos da província de Jiangsu. Foi conduzido um ciclo quase-experimental de 16 semanas, composto por 4 semanas de pré-teste, 10 semanas de intervenção e 2 semanas de pós-teste. Os dados foram recolhidos através de questionários, análise da plataforma e entrevistas semiestruturadas.

Resultados: Os sistemas de aprendizagem adaptativa baseados em IA aumentaram significativamente o envolvimento global na aprendizagem e nas suas três dimensões. A experiência de aprendizagem mediou parcialmente esta relação, explicando 54,9% do efeito total, e os dados qualitativos corroboraram estes resultados.

Discussão: Os resultados são consistentes com estudos em disciplinas académicas tradicionais e preenchem a lacuna da investigação quantitativa em educação física online.

Conclusões: Os sistemas de aprendizagem adaptativa baseados em IA melhoram eficazmente o envolvimento dos alunos na educação física online, com a experiência de aprendizagem a atuar como um mediador fundamental, fornecendo evidências para a transformação inteligente da educação física online.

Referências

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Deng, R., Benckendorff, P., & Gannaway, D. (2020). Learner engagement in MOOCs: Scale development and validation. British Journal of Educational Technology, 51(1), 245–262. https://doi.org/10.1111/bjet.12810

Ersozlu, Z., Taheri, S., & Koch, I. (2024). A review of machine learning methods used for educational data. Education and Information Technologies, 29(16), 22125–22145. https://doi.org/10.1007/s10639-024-12704-0

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059

Gadhvi, R., Desai, P., & Siddharth, S. (2025). PosePilot: An edge-AI solution for posture correction in yoga. arXiv. https://doi.org/10.48550/arXiv.2505.19186

Goad, T., Jones, E., Bulger, S., Daum, D., Hollett, N., & Elliott, E. (2021). Predicting student success in online physical education. American Journal of Distance Education, 35(1), 17–32. https://doi.org/10.1080/08923647.2020.1829254

Hu, J., & Xiao, Y. (2025). What are the influencing factors of online learning engagement? A systematic literature review. Frontiers in Psychology, 16, 1542652. https://doi.org/10.3389/fpsyg.2025.1542652

Hu, Z., Liu, Z., & Su, Y. (2024). AI-driven smart transformation in physical education: Current trends and future research directions. Applied Sciences, 14(22), 10616. https://doi.org/10.3390/app142210616

Indarto, P., Nasuka, N., Hidayatullah, M. F., Sulaiman, S., Setyawati, H., Raharjo, H. P., et al. (2024). What is the learning model of physical education in the digital era? Literature review of various studies. Retos, 61, 156–163. https://doi.org/10.47197/retos.v61.109583

Kotte, H., Kravcik, M., & Duong-Trung, N. (2023). Real-time posture correction in gym exercises: A computer vision-based approach for performance analysis, error classification and feedback. CEUR Workshop Proceedings, 3499, 9. https://ceur-ws.org/Vol-3499/paper9.pdf

Long, D. Y., Wang, S., & Lu, X. T. (2026). Artificial intelligence in higher education: A systematic review of its impact on student engagement and the mediating role of teaching methods. Frontiers in Education, 10, 1648661. https://doi.org/10.3389/feduc.2025.1648661

Ma, F. (2025). Learning behavior analysis and personalized recommendation system of online education platform based on machine learning. Computers and Education: Artificial Intelligence, 8, 100408. https://doi.org/10.1016/j.caeai.2025.100408

Mănescu, D. C. (2025). Artificial intelligence in elite sports training and prospects for integration into school sports. Retos, 73, 128–141. https://doi.org/10.47197/retos.v73.117261

Medrano, G. L., Engi, S. M. S., & Yurango, C. P. (2025). Usage of AI-based ChatGPT, students' self-efficacy, and engagement in mathematics. Asian Research Journal of Arts & Social Sciences, 23(7), 53–61. https://doi.org/10.9734/arjass/2025/v23i7726

Ministry of Education of the People's Republic of China. (2022). Smart Education of China. https://www.smartedu.cn

Möller, M., Nirmal, G., Fabietti, D., Stierstorfer, Q., Zakhvatkin, M., Sommerfeldt, H., & Schütt, S. (2024). Revolutionising distance learning: A comparative study of learning progress with AI-driven tutoring. arXiv. https://doi.org/10.48550/arXiv.2403.14642

Mulato, N., Hidayatulloh, F., Purnama, S. K., & Syaifullah, R. (2024). Optimization of learning physical education in digital era: A systematic review. Retos, 54, 844–849. https://doi.org/10.47197/retos.v54.105211

Murtagh, E. M., Calderón, A., Scanlon, D., & MacPhail, A. (2023). Online teaching and learning in physical education teacher education: A mixed studies review of literature. European Physical Education Review, 29(3), 369–388. https://doi.org/10.1177/1356336X231155793

Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.

Olmos-Gómez, M. C., Portillo-Sánchez, R., & Parra-González, M. E. (2025). Physical education and artificial intelligence: Validation of an instrument on the use and perception of AI in young people. Retos, 67, 46–56. https://doi.org/10.47197/retos.v67.112460

Omarov, N., Omarov, B., Azhibekova, Z., & Omarov, B. (2024). Applying an augmented reality game-based learning environment in physical education classes to enhance sports motivation. Retos, 60, 269–278. https://doi.org/10.47197/retos.v60.109170

Putra, C. A., Permadi, A. S., & Setiawan, M. A. (2024). Information technology innovation in sports learning: Understanding global trends and challenges. Retos, 58, 844–854. https://doi.org/10.47197/retos.v58.106485

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

Schindler, L. A., Burkholder, G. J., Morad, O. A., & Marsh, C. (2017). Computer-based technology and student engagement: A critical review of the literature. International Journal of Educational Technology in Higher Education, 14(1), 1–28. https://doi.org/10.1186/s41239-017-0063-0

Sevilla-Sanche, M., Xurxo Dopico, C., Morales, J., Iglesias-Sole, E., Fariñas, J., & Carballeira, E. (2023). Gamification in physical education: Evaluation of impact on motivation and motor learning. Retos, 47, 87–95. https://doi.org/10.47197/retos.v47.94686

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.

Shelenbekova, S., Kamalova, G., Iskakova, M., Aldzbergenova, A., Abdykerimova, E., & Shetiyeva, K. (2025). Exploring the use of artificial intelligence and augmented reality tools to improve interactivity in physical education teaching and training methods. Retos, 66, 935–949. https://doi.org/10.47197/retos.v66.113540

Tohănean, D. I., Vulpe, A. M., Mijaica, R., & Alexe, D. I. (2025). Embedding digital technologies (AI and ICT) into physical education: A systematic review of innovations, pedagogical impact, and challenges. Applied Sciences, 15(17), 9826. https://doi.org/10.3390/app15179826

Vasco Delgado, J. C., Macas Padilla, B. A., Vasco Delgado, L. A., & Vasco Delgado, L. J. (2025). Diseño y validación de un modelo evaluativo de Educación Física mediado por inteligencia artificial. Retos, 70, 1446–1460. https://doi.org/10.47197/retos.v70.116530

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167

Wang, Y., & Wang, X. (2024). Artificial intelligence in physical education: Comprehensive review and future teacher training strategies. Frontiers in Public Health, 12, 1484848. https://doi.org/10.3389/fpubh.2024.1484848

Yang, H. (2025). Harnessing generative AI: Exploring its impact on cognitive engagement, emotional engagement, learning retention, reward sensitivity, and motivation through reinforcement theory. Learning and Motivation, 90, 102136. https://doi.org/10.1016/j.lmot.2025.102136

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0

Zhang, F., Wang, X., & Zhang, X. (2025). Applications of deep learning methods of artificial intelligence in education. Education and Information Technologies, 30(2), 1563–1587. https://doi.org/10.1007/s10639-024-12883-w

Zhoc, K. C. H., Webster, B. J., King, R. B., & Li, W. (2019). Higher Education Student Engagement Scale (HESES): Development and psychometric evidence. Research in Higher Education, 60(2), 219–244. https://doi.org/10.1007/s11162-018-9510-6

Zhou, C., & Hou, F. (2024). Can AI empower L2 education? Exploring its influence on the behavioural, cognitive and emotional engagement of EFL teachers and language learners. European Journal of Education, 59(4). https://doi.org/10.1111/ejed.12750

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27-05-2026

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Artigos de caráter científico: trabalhos de pesquisas básicas e/ou aplicadas.

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Huang, S., & Wong, B. E. (2026). Sistemas de aprendizagem adaptativa com IA e engagement multidimensional na Educação Física online: grandes efeitos. Retos, 80, 1008-1027. https://doi.org/10.47197/retos.v80.119205