Research Article

The role of social media use motivations in university students’ adoption of AI-supported learning tools: The mediating effect of perceived usefulness

Gulbakyt K. Shashayeva 1 , Akhmetova Aigul Igenovna 1 * , Naziya A. Tassilova 2 , Saltanat B. Beisenova 3 , Aigul K. Nogayeva 1 , Yanjie Song 4, Aliya S. Kosshygulova 1
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1 Abai Kazakh National Pedagogical University, Almaty, KAZAKHSTAN2 Al-Farabi Kazakh National University, Almaty, KAZAKHSTAN3 Pavlodar Pedagogical University named after Alkey Margulan, Pavlodar, KAZAKHSTAN4 The Education University of Hong Kong (EdUHK), Hong Kong SAR, CHINA* Corresponding Author
Online Journal of Communication and Media Technologies, 16(2), April 2026, e202633, https://doi.org/10.30935/ojcmt/18592
Published: 23 May 2026
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ABSTRACT

This study aims to examine the impact of university students’ motivations for social media use (information seeking, socialization, entertainment, and identity formation) on their intentions to use artificial intelligence (AI)-powered learning tools such as ChatGPT, Gemini, and Copilot. Designed within the framework of the technology acceptance model (TAM), the research addresses the mediating role of perceived utility and the moderating role of digital literacy. The study aims to contribute to the literature by understanding how social media habits evolve into academic technology adoption processes. This research, based on a sample of 370 university students, investigates the relationship between various social media motivations (social connection/FOMO, popularity/identity formation, appearance/impression management, and civic/advocacy) and behavioral intention (BI) to use AI learning tools. Perceived usefulness (PU) is included as a mediating variable in this relationship. Results from regression-based mediation analyses (PROCESS Model 4 equivalent) indicate that social media use motivations significantly predict both PU and BI. The indirect effect through PU was statistically significant (ab = 0.248, SE = 0.062, z = 3.988, p < .001), supporting a partial mediation model. Civic/advocacy motivations demonstrated the strongest relationship with PU and BI among subscales. These findings advance understanding of technology adoption in educational contexts and highlight the role of social media usage patterns in shaping AI tool adoption.
Keywords: social media motivations, AI learning tools, perceived usefulness, behavioral intention, technology adoption, TAM, mediation

CITATION (APA)

Shashayeva, G. K., Igenovna, A. A., Tassilova, N. A., Beisenova, S. B., Nogayeva, A. K., Song, Y., & Kosshygulova, A. S. (2026). The role of social media use motivations in university students’ adoption of AI-supported learning tools: The mediating effect of perceived usefulness. Online Journal of Communication and Media Technologies, 16(2), e202633. https://doi.org/10.30935/ojcmt/18592

REFERENCES

  1. Aldraiweesh, A. A., & Alturki, U. (2025). The influence of social support theory on AI acceptance: Examining educational support and perceived usefulness using SEM analysis. IEEE Access, 13, 18366-18385. https://doi.org/10.1109/ACCESS.2025.3534099
  2. Alhabash, S., & Ma, M. (2017). A tale of four platforms: Motivations and uses of Facebook, Twitter, Instagram, and Snapchat among college students? Social Media + Society, 3(1), Article 2056305117691544. https://doi.org/10.1177/2056305117691544
  3. Alkhawaja, M. I., Abd Halim, M. S., Abumandil, M. S. S., & Al-Adwan, A. S. (2022). System quality and student’s acceptance of the e-learning system: The serial mediation of perceived usefulness and intention to use. Contemporary Educational Technology, 14(2), Article ep350. https://doi.org/10.30935/cedtech/11525
  4. Alshammari, S. H., & Babu, E. (2025). The mediating role of satisfaction in the relationship between perceived usefulness, perceived ease of use and students’ behavioural intention to use ChatGPT. Scientific Reports, 15, Article 7169. https://doi.org/10.1038/s41598-025-91634-4
  5. Alturki, U., & Aldraiweesh, A. (2024). The impact of self-determination theory: The moderating functions of social media (SM) use in education and affective learning engagement. Humanities and Social Sciences Communications, 11, Article 693. https://doi.org/10.1057/s41599-024-03150-x
  6. Badr, A. M. M., Al-Abdi, B. S., Rfeqallah, M., Kasim, R., & Ali, F. A. M. (2024). Information quality and students’ academic performance: The mediating roles of perceived usefulness, entertainment and social media usage. Smart Learning Environments, 11, Article 45. https://doi.org/10.1186/s40561-024-00329-2
  7. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173-1182. https://doi.org/10.1037/0022-3514.51.6.1173
  8. Bawa, R., Jain, K., & Goel, P. (2026). Generative AI adoption in universities: How TAM-TTF and neuroticism influence sustained usage. Interactive Technology and Smart Education, 23(2), 266-297. https://doi.org/10.1108/ITSE-02-2025-0030
  9. Buzeta, C., De Keyzer, F., Dens, N., & De Pelsmacker, P. (2024). Branded content and motivations for social media use as drivers of brand outcomes on social media: A cross-cultural study. International Journal of Advertising, 43(4), 637-671. https://doi.org/10.1080/02650487.2023.2215079
  10. Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K., & Qin, J. (2021). Perceptions of and behavioral intentions towards learning artificial intelligence in primary school students. Educational Technology & Society, 24(3), 89-101. https://www.jstor.org/stable/27032858
  11. Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage.
  12. Cuong, T. V., Khai, N. T., Oo, T. Z., & Józsa, K. (2025). The impact of social media use motives on students’ GPA: The mediating role of daily time usage. Education Sciences, 15(3), Article 317. https://doi.org/10.3390/educsci15030317
  13. Damerji, H., & Salimi, A. (2021). Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting. Accounting Education, 30(2), 107-130. https://doi.org/10.1080/09639284.2021.1872035
  14. 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
  15. Fan, C. (2023). English learning motivation with TAM: Undergraduates’ behavioral intention to use Chinese indigenous social media platforms for English learning. Cogent Social Sciences, 9(2), Article 2260566. https://doi.org/10.1080/23311886.2023.2260566
  16. Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis (2nd ed.). Guilford Press.
  17. Jeilani, A., & Abubakar, S. (2025). Perceived institutional support and its effects on student perceptions of AI learning in higher education: The role of mediating perceived learning outcomes and moderating technology self-efficacy. Frontiers in Education, 10, Article 1548900. https://doi.org/10.3389/feduc.2025.1548900
  18. Kim, Y., & Lee, H.-S. (2026). Factors affecting the continuous intention to use ChatGPT: Evidence from Korea and USA. The Journal of Korean Institute of Communications and Information Sciences, 51(1), 146–168. https://doi.org/10.7840/kics.2026.51.1.146
  19. Li, Y., Yang, Y., Chen, L., & Bao, S. (2026). Factors influencing students’ intentions to continue learning in AI-assisted painting courses. Scientific Reports, 16, Article 9846. https://doi.org/10.1038/s41598-026-40663-8
  20. Liu, G., & Ma, C. (2024). Measuring EFL learners’ use of ChatGPT in informal digital learning of English based on the technology acceptance model. Innovation in Language Learning and Teaching, 18(2), 125-138. https://doi.org/10.1080/17501229.2023.2240316
  21. Low, M. P., Soliman, M., Al Balushi, M. K., & Leong, C.-M. (2025). Beyond conventional adoption: New insights into AI facilitators in higher education institutions. Sustainable Futures, 9, Article 100781. https://doi.org/10.1016/j.sftr.2025.100781
  22. Masli, A., Alfatiemy, M., Elshahoubi, I., & Elheddad, M. (2025). Unraveling the path: Assessing compliance and impact of accounting education in Libya with IES 3 standards on students’ academic performance. Journal of Applied Research in Higher Education, 17(1), 289-302. https://doi.org/10.1108/JARHE-08-2023-0351
  23. Park, D. Y., & Kim, H. (2023). Determinants of intentions to use digital mental healthcare content among university students, faculty, and staff: Motivation, perceived usefulness, perceived ease of use, and parasocial interaction with AI chatbot. Sustainability, 15(1), Article 872. https://doi.org/10.3390/su15010872
  24. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879
  25. Rahman, M. K., Ismail, N. A., Hossain, M. A., & Hossen, M. S. (2025). Students’ mindset to adopt AI chatbots for effectiveness of online learning in higher education. Future Business Journal, 11, Article 30. https://doi.org/10.1186/s43093-025-00459-0
  26. Rodgers, R. F., Mclean, S. A., Gordon, C. S., Slater, A., Marques, M. D., Jarman, H. K., & Paxton, S. J. (2021). Development and validation of the motivations for social media use scale (MSMU) among adolescents. Adolescent Research Review, 6(4), 425-435. https://doi.org/10.1007/s40894-020-00139-w
  27. Sharif, A., Soroya, S. H., Ahmad, S., & Mahmood, K. (2021). Antecedents of self-disclosure on social networking sites (SNSs): A study of Facebook users. Sustainability, 13(3), Article 1220. https://doi.org/10.3390/su13031220
  28. Störrle, H. (2017). How are conceptual models used in industrial software development? A descriptive survey. Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering (pp. 160-169). https://doi.org/10.1145/3084226.3084256
  29. Toros, E., Asiksoy, G., & Sürücü, L. (2024). Refreshment students’ perceived usefulness and attitudes towards using technology: A moderated mediation model. Humanities and Social Sciences Communications, 11, Article 333. https://doi.org/10.1057/s41599-024-02839-3
  30. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
  31. Yan, S., Eng, L. G., & Seong, L. C. (2024). Influencing factors of continuous intention to use e-learning system of undergraduates in Guangxi, China: The mediating role of perceived ease of use and perceived usefulness. Sage Open, 14(4), Article 21582440241305231. https://doi.org/10.1177/21582440241305231
  32. Yi, R., Liu, D., Sun, X., & Zhou, B. (2025). Exploring the link between AI usage intention and digital competence among college PE teachers: A moderated mediation model based on SCT and UTAUT. PLoS ONE, 20(11), Article e0334699. https://doi.org/10.1371/journal.pone.0334699
  33. Zhao, Z., An, Q., & Liu, J. (2025). Exploring AI tool adoption in higher education: Evidence from a PLS-SEM model integrating multimodal literacy, self-efficacy, and university support. Frontiers in Psychology, 16, Article 1619391. https://doi.org/10.3389/fpsyg.2025.1619391