Study of Digital Learning Motivations Using Artificial Intelligence Among Sports Management Students

Document Type : Original Article

Authors

1 Professor, Department of Sport Management, Allameh Tabatabaei University, Tehran, Iran

2 Master's Student in Sport Management, Allameh Tabatabaei University, Tehran, Iran

3 PhD Student in Sport Management, Allameh Tabatabaei University, Tehran, Iran

10.22034/jsmk.2025.67638.1134

Abstract

The aim of this study was to identify and analyze the motivations for digital learning among sports management students by leveraging artificial intelligence, in order to understand motivational patterns and improve the learning experience in modern educational environments. The statistical population consisted of sports management students. Sampling was conducted through convenience sampling using Cochran’s formula (n = 200). The data collection instrument was a translated standard questionnaire, the validity of which was assessed through the translation–back translation method and confirmed by experts. Reliability was verified using Cronbach’s alpha (α > 0.70). Data were collected virtually and analyzed using structural equation modeling (SEM) via PLS4 software. The research findings indicated that the constructs of novelty and scientific content generation had a positive and significant effect on students’ intention to use artificial intelligence. The results suggest that cognitive and productive motivations play a fundamental role in students’ willingness to adopt digital learning technologies. The desire to seek knowledge and scientific information—rooted in an intrinsic need for deeper understanding of specialized concepts—is a key factor in the acceptance and use of modern educational tools. The development of scientific freelancing platforms and student participation in the production of high-quality digital content can significantly reduce dependency on free resources and enhance motivation for purposeful and continuous use of digital learning technologies. This approach, in addition to ensuring the financial sustainability of the educational system, can lay the groundwork for a new cultural development in the consumption and production of digital knowledge.

Keywords


Almeida, F., Silva, S., & Monteiro, J. (2023). The role of ChatGPT in academic writing: Challenges and opportunities. Journal of Educational Technology Development and Exchange, 16(2), 45–58.
Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration. Technology Studies, 2(2), 285–309.
Chan, K. H., Cheung, K. L., & Leung, W. Y. (2023). Artificial intelligence-based chatbots in education: A review of literature and directions for future research. Computers & Education: Artificial Intelligence, 4, 100130.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Duong, T., Nguyen, H., & Pham, M. (2023). ChatGPT as an educational tool: Usage patterns and learning outcomes. AI in Education Review, 5(1), 23–38.
Foroughi, B., Iranmanesh, M., Hyun, S. S., & Kim, D. (2023). Drivers of students’ intention to use ChatGPT for academic purposes: An application of UTAUT2 and uses and gratifications theory. Computers & Education, 201, 104831.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.
Han, S., & Kim, J. (2020). The impact of scientific content creation on cognitive engagement and motivation in intelligent learning platforms. Journal of Educational Technology & Society, 23(2), 112-124.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Jishnu, R., Sandeep, S., & Rani, P. (2023). Students’ motivations for using ChatGPT: A uses and gratifications approach. Education and Information Technologies, 28(6), 11321–11342.
Jo, W., & Park, Y. (2023). Exploring employees’ acceptance of ChatGPT in the workplace: An extended technology acceptance model. Technology in Society, 74, 102344.
Le, T., Nguyen, M., Pham, H., & Tran, L. (2024). Understanding AI technology adoption in higher education: A structural equation modeling approach. Journal of Educational Technology Research, 37(1), 45–62.
Leguina, A. (2015). A primer on partial least squares structural equation modeling (PLS-SEM). International Journal of Research & Method in Education, 38(2), 220–221. https://doi.org/10.1080/1743727X.2015.1005806
Ma, L., Zhang, H., & Li, J. (2024). Trends in ChatGPT usage among university students: A cross-national study. International Journal of Educational Technology in Higher Education, 21(1), 1–19.
McLean, G., & Osei-Frimpong, K. (2019). Hey Alexa… examine the variables influencing the use of artificial intelligent smart voice assistants. Computers in Human Behavior, 99, 28–37.
Papacharissi, Z. (2014). A networked self: Identity, community and culture on social network sites. Routledge.
Rudolph, J., Tan, S., & Lee, M. (2023). ChatGPT in higher education: Exploring academic integration and implications for teaching and learning. Journal of Educational Computing Research, 61(4), 778–802.
Seif, A., Zhang, Y., & Lin, J. (2024). The impact of AI tools on students’ creative performance in writing tasks: Evidence from ChatGPT. Educational Technology Research and Development, 72(2), 335–352.
Vinzi, V. E., Trinchera, L., & Amato, S. (2010). PLS path modeling: From foundations to recent developments and open issues for model assessment and improvement. Handbook of Partial Least Squares, 47–82. https://doi.org/10.1007/978-3-540-32827-8_2
Wang, X., Li, Y., & Zhang, Q. (2021). Learner engagement in AI-based digital learning environments: The role of content creation on perceived usefulness and continuous usage intention. Computers & Education, 165, 104141. https://doi.org/10.1016/j.compedu.2021.104141
Xu, X.-Y., Tayyab, S. M. U., Jia, Q., & Huang, A. H. (2025). A multi-model approach for the extension of the use and gratification theory in video game streaming. Information Technology & People, 38(1), 137-179
Yilmaz, R. M., Yilmaz, F. G. K., & Keser, H. (2023). Understanding students’ acceptance of ChatGPT: A technology acceptance model approach. Computers and Education: Artificial Intelligence, 4, 100
Yin, Y., Bai, B., & Xu, S. (2025). The role of social community in influencing purchase intention in live-streaming E-commerce: a social learning theory perspective. Marketing Intelligence & Planning.
Yu, E., Jung, C., Kim, H., & Jung, J. (2018). Impact of viewer engagement on gift-giving in live video streaming. Telematics and Informatics, 35(5), 1450-1460
Zhang, T., Li, B., & Hua, N. (2025). Live-streaming tourism: Model development and validations. Journal of Travel Research, 64(3), 559-575
Zhang, X. (2025). The Impact of Social Presence on Impulsive Purchase Intentions in Live Streaming E-Commerce. International Journal of Science and Business, 43(1), 138-149.