• Boscardin, C. K., Gin, B., Golde, P. B. & Hauer, K. E. ChatGPT and generative artificial intelligence for medical education: potential impact and opportunity. Acad. Med. 99, 22–27 (2024).

    Article 
    PubMed 

    Google Scholar 

  • Chan, C. K. Y. A comprehensive AI policy education framework for university teaching and learning. Int. J. Educ. Technol. High. Educ. 20, 856. https://doi.org/10.1186/s41239-023-00408-3 (2023).

  • Xue, Y. & Li, N. Research and application of multimedia compression technology in online physical education teaching task. Signal. Image Video Process. 18, 3723–3735. https://doi.org/10.1007/s11760-024-03012-8 (2024).

    Article 

    Google Scholar 

  • Xue, E., Zhang, J. & Li, J. Situation assessment, key challenges and policy paths of family school-society collaboration in science education—empirical analysis based on 9199 questionnaires from 21 provinces in east, central and Western China. China Educational Technol. 27, 276–290 (2024).

    Google Scholar 

  • Falloon, G. From digital literacy to digital competence: the teacher digital competency (TDC) framework. Education Tech. Research Dev. 68, 2449–2472. https://doi.org/10.1007/s11423-020-09767-4 (2020).

    Article 

    Google Scholar 

  • Lee, H. S. & Lee, J. Applying artificial intelligence in physical education and future perspectives. Sustainability 13, 963. https://doi.org/10.3390/su13010351 (2021).

  • Yu, J. E. Exploration of educational possibilities by four metaverse types in physical education. Technologies 10, 104 (2022).

    Article 

    Google Scholar 

  • Osterlie, O., Frantzen, H. & Riomao, A. P. It’s like being there, but not in the way’. Exploring the use of virtual reality tools to stimulate reflection in Norwegian physical education teacher education. Sport Educ. Soc. https://doi.org/10.1080/13573322.2024.2417070 (2024).

    Article 

    Google Scholar 

  • Polechonski, J. Assessment of the intensity and attractiveness of physical exercise while playing table tennis in an immersive virtual environment depending on the game mode. Bmc Sports Sci. Med. Rehabil. 16, 856. https://doi.org/10.1186/s13102-024-00945-y (2024).

  • Ayanwale, M. A., Adelana, O. P., Molefi, R. R., Adeeko, O. & Ishola, A. M. Examining artificial intelligence literacy among pre-service teachers for future classrooms. Comput. Educ. Open. 6, 100179 (2024).

    Article 

    Google Scholar 

  • Hatlevik, O. E., Guðmundsdóttir, G. B. & Loi, M. Digital diversity among upper secondary students: a multilevel analysis of the relationship between cultural capital, self-efficacy, strategic use of information and digital competence. Comput. Educ. 81, 345–353 (2015).

    Article 

    Google Scholar 

  • Masoumi, D. & Noroozi, O. Developing early career teachers’ professional digital competence: a systematic literature review. Eur. J. Teacher Educ. 2023, 1–23 (2023).

  • Mishra, P. & Koehler, M. J. Technological pedagogical content knowledge: a framework for teacher knowledge. Teachers Coll. Record. 108, 1017–1054 (2006).

    Article 

    Google Scholar 

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

  • Zhao, Y., Llorente, A. M. P. & Gómez, M. C. S. Digital competence in higher education research: a systematic literature review. Comput. Educ. 168, 856. https://doi.org/10.1016/j.compedu.2021.104212 (2021).

  • Zhou, J., Shen, L. & Chen, W. How ChatGPT transformed teachers: the role of basic psychological needs in enhancing digital competence. Front. Psychol. 15, 1458551 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Huang, L. & Lajoie, S. P. Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development. Comput. Educ. 166, 104169. https://doi.org/10.1016/j.compedu.2021.104169 (2021).

    Article 

    Google Scholar 

  • Huang, L., Li, S., Poitras, E. G. & Lajoie, S. P. Latent profiles of self-regulated learning and their impacts on teachers’ technology integration. Br. J. Edu. Technol. 52, 695–713. https://doi.org/10.1111/bjet.13050 (2021).

    Article 

    Google Scholar 

  • Huang, L., Zhan, Y. & Ba, S. Modeling student teachers’ self-regulated learning of complex professional knowledge: a sequential and clustering analysis with think-aloud protocols. Comput. Educ. 233, 105310. https://doi.org/10.1016/j.compedu.2025.105310 (2025).

    Article 

    Google Scholar 

  • Fang, J. W. et al. Emotional supports in robot-based self-regulated learning contexts to promote pre-service teachers’ digital learning resource development competences. Educ. Inform. Technol. https://doi.org/10.1007/s10639-024-13059-2 (2024).

    Article 

    Google Scholar 

  • Ryan, R. M. & Deci, E. L. Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemp. Educ. Psychol. 61, 856. https://doi.org/10.1016/j.cedpsych.2020.101860 (2020).

  • Shulman, L. S. Those who understand: knowledge growth in teaching. Educ. Res. 15, 4–14 (1986).

    Article 

    Google Scholar 

  • Chen, A., Li, W. & Fu, W. Unleashing digital superheroes: unravelling the empathy factor in digital competence and online teacher autonomy support. Br. J. Edu. Technol. 55, 1790–1810 (2024).

    Article 

    Google Scholar 

  • Velander, J., Taiye, M. A., Otero, N. & Milrad, M. Artificial intelligence in K-12 education: eliciting and reflecting on Swedish teachers’ Understanding of AI and its implications for teaching & learning. Educ. Inform. Technol. 29, 4085–4105. https://doi.org/10.1007/s10639-023-11990-4 (2024).

    Article 

    Google Scholar 

  • Al-Abdullatif, A. M. Modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of AI literacy, intelligent TPACK, and perceived trust. Educ. Sci. 14, 856. https://doi.org/10.3390/educsci14111209 (2024).

  • Chu, J. et al. Exploring factors influencing pre-service teacher’s digital teaching competence and the mediating effects of data literacy: empirical evidence from China. Humanit. Social Sci. Commun. 10, 508. https://doi.org/10.1057/s41599-023-02016-y (2023).

    Article 

    Google Scholar 

  • Guillén-Gámez, F. D., Ruiz-Palmero, J. & García, M. G. Digital competence of teachers in the use of ICT for research work: development of an instrument from a PLS-SEM approach. Educ. Inform. Technol. 28, 16509–16529. https://doi.org/10.1007/s10639-023-11895-2 (2023).

    Article 

    Google Scholar 

  • Ryan, R. M. & Deci, E. L. Intrinsic and extrinsic motivations: classic definitions and new directions. Contemp. Educ. Psychol. 25, 54–67. https://doi.org/10.1006/ceps.1999.1020 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Ryan, R. M. & Deci, E. L. Self-Determination Theory: Basic Psychological Needs in Motivation, Development, and Wellness (Wiley, 2017).

  • Ting, Y. L. Tapping into students’ digital literacy and designing negotiated learning to promote learner autonomy. Internet High. Educ. 26, 25–32. https://doi.org/10.1016/j.iheduc.2015.04.004 (2015).

    Article 

    Google Scholar 

  • Xia, Q., Chiu, T. K. F. & Chai, C. S. The moderating effects of gender and need satisfaction on self-regulated learning through artificial intelligence (AI). Educ. Inform. Technol. 28, 8691–8713. https://doi.org/10.1007/s10639-022-11547-x (2023).

    Article 

    Google Scholar 

  • Trust, T., Krutka, D. G. & Carpenter, J. P. Together we are better: professional learning networks for teachers. Comput. Educ. 102, 15–34 (2016).

    Article 

    Google Scholar 

  • Ashok, M., Madan, R., Joha, A. & Sivarajah, U. Ethical framework for artificial intelligence and digital technologies. Int. J. Inf. Manag. 62, 75. https://doi.org/10.1016/j.ijinfomgt.2021.102433 (2022).

  • Wang, B. C., Rau, P. L. P. & Yuan, T. Y. Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behav. Inform. Technol. 42, 1324–1337. https://doi.org/10.1080/0144929x.2022.2072768 (2023).

    Article 

    Google Scholar 

  • Chiu, T. K. et al. A self-determination theory approach to teacher digital competence development. Comput. Educ. 214, 105017 (2024).

    Article 

    Google Scholar 

  • Zimmerman, B. J. A social cognitive view of self-regulated academic learning. J. Educ. Psychol. 81, 329 (1989).

    Article 

    Google Scholar 

  • Eckley, D., Allen, A., Millear, P. & Rune, K. T. COVID-19’s impact on learning processes in Australian university students. Soc. Psychol. Educ. 26, 161–189. https://doi.org/10.1007/s11218-022-09739-x (2023).

    Article 
    PubMed 

    Google Scholar 

  • Li, S. C. & Zhu, J. X. Cognitive-motivational engagement in ICT mediates the effect of ICT use on academic achievements: evidence from 52 countries. Comput. Educ. 204, 856. https://doi.org/10.1016/j.compedu.2023.104871 (2023).

  • Demirbag, M. & Bahcivan, E. Comprehensive exploration of digital literacy: embedded with self-regulation and epistemological beliefs. J. Sci. Edu. Technol. 30, 448–459 (2021).

    Article 

    Google Scholar 

  • Anthonysamy, L., Koo, A. C. & Hew, S. H. Self-regulated learning strategies in higher education: fostering digital literacy for sustainable lifelong learning. Educ. Inform. Technol. 25, 2393–2414 (2020).

    Article 

    Google Scholar 

  • Palalas, A. & Wark, N. The relationship between mobile learning and self-regulated learning: a systematic review. Australasian J. Educational Technol. 36, 151–172 (2020).

    Article 

    Google Scholar 

  • Tarhini, A., Hone, K., Liu, X. H. & Tarhini, T. Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: a structural equation modeling of an extended technology acceptance model. Interact. Learn. Environ. 25, 306–328. https://doi.org/10.1080/10494820.2015.1122635 (2017).

    Article 

    Google Scholar 

  • Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. Multivariate Data Analysis: A Global Perspective (Springer, 2014).

  • Liu, J. D. & Chung, P. K. Development and initial validation of the psychological needs satisfaction scale in physical education. Meas. Phys. Educ. Exerc. Sci. 18, 101–122 (2014).

    Article 

    Google Scholar 

  • Pintrich, P., Smith, D., García, T. & McKeachie, W. A manual for the use of the motivational strategies for learning questionnaire (MSLQ). Ann Arbor, MI: university of Michigan, National center for research to improve. Postsecondary Teach. Learn. 313, 936–2741 (1991).

    Google Scholar 

  • Di Battista, R. et al. Student intention to engage in leisure-time physical activity: the interplay of task-involving climate, competence need satisfaction and psychobiosocial States in physical education. Eur. Phys. Educ. Rev. 25, 761–777. https://doi.org/10.1177/1356336×18770665 (2019).

    Article 

    Google Scholar 

  • Broadbent, J. Comparing online and blended learner’s self-regulated learning strategies and academic performance. Internet High. Educ. 33, 24–32. https://doi.org/10.1016/j.iheduc.2017.01.004 (2017).

    Article 

    Google Scholar 

  • Keating, X. D. et al. An analysis of Chinese preservice physical education teachers’ beliefs about the physical education profession. J. Teach. Phys. Educ. 40, 58–65 (2020).

    Article 

    Google Scholar 

  • Kline, R. B. Principles and Practice of Structural Equation Modeling (Guilford, 2015).

  • McDonald, R. P. & Ho, M. H. R. Principles and practice in reporting structural equation analyses. Psychol. Methods. 7, 64 (2002).

    Article 
    PubMed 

    Google Scholar 

  • Bagozzi, R. P. & Yi, Y. On the evaluation of structural equation models. J. Acad. Mark. Sci. 16, 74–94 (1988).

    Article 

    Google Scholar 

  • Fornell, C. & Larcker, D. F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18, 39–50 (1981).

    Article 

    Google Scholar 

  • Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. Multivariate Data Analysis (Pearson Prentice Hall, 2010).

  • Chiu, T. K. F. School learning support for teacher technology integration from a self-determination theory perspective. Etr&D-Educational Technol. Res. Dev. 70, 931–949. https://doi.org/10.1007/s11423-022-10096-x (2022).

    Article 

    Google Scholar 

  • Legrain, P., Gillet, N., Gernigon, C. & Lafreniere, M. A. Integration of information and communication technology and pupils’ motivation in a physical education setting. J. Teach. Phys. Educ. 34, 384–401. https://doi.org/10.1123/jtpe.2014-0013 (2015).

    Article 

    Google Scholar 

  • Bergdahl, J. et al. Self-determination and attitudes toward artificial intelligence: cross-national and longitudinal perspectives. Telematics Inform. 82, 856. https://doi.org/10.1016/j.tele.2023.102013 (2023).

  • Henriksen, D., Oster, N., Mishra, P., McCaleb, L. & Generative, A. I. Creativity, Culture, and the future of learning: a conversation with Mairéad Pratschke. TechTrends 69, 3–9 (2025).

    Article 

    Google Scholar 

  • Lee, H. S. & Lee, J. Applying artificial intelligence in physical education and future perspectives. Sustainability 13, 351 (2021).

    Article 
    ADS 

    Google Scholar 

  • Yang, P. & Qian, S. The factors affecting students’ behavioral intentions to use E-learning for educational purposes: A study of physical education students in China. SAGE Open. 15, 21582440251313654 (2025).

    Article 

    Google Scholar 

  • Ray, P. P. & ChatGPT A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet Things Cyber-Phys. Syst. 3, 121–154 (2023).

    Article 

    Google Scholar 

  • Mishra, P., Oster, N. & Henriksen, D. Generative AI, teacher knowledge and educational research: bridging Short-and Long-Term perspectives. TechTrends 68, 205–210 (2024).

    Article 

    Google Scholar 

  • Petko, D., Mishra, P. & Koehler, M. J. TPACK in context: an updated model. Comput. Educ. Open. 8, 100244. https://doi.org/10.1016/j.caeo.2025.100244 (2025).

    Article 

    Google Scholar 

  • Choi, S. M., Sum, R. K. W., Leung, E. F. L. & Sit, C. The relationship and effect among physical literacy attributes in university physical education during the pandemic quarantine period. J. Teach. Phys. Educ. 43, 39–49 (2023).

    Article 

    Google Scholar 

  • Sahin, F. & Sahin, Y. L. Drivers of technology adoption during the COVID-19 pandemic: the motivational role of psychological needs and emotions for pre-service teachers. Soc. Psychol. Educ. 25, 567–592. https://doi.org/10.1007/s11218-022-09702-w (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mishra, P., Warr, M. & Islam, R. TPACK in the age of ChatGPT and generative AI. J. Digit. Learn. Teacher Educ. 39, 235–251 (2023).

    Article 

    Google Scholar 

  • Oster, N., Henriksen, D. & Mishra, P. Chatgpt for teachers: insights from online discussions. TechTrends 68, 640–646 (2024).

    Article 

    Google Scholar 

  • Bao, L., Soh, K. G., Mohd Nasiruddin, N. J., Xie, H. & Zhang, J. Unveiling the impact of metacognition on academic achievement in physical education and activity settings: A comprehensive systematic review and Meta-Analysis of qualitative insights. Psychol. Res. Behav. Manage. 2024, 973–987 (2024).

  • Source link