Abstract
Artificial intelligence (AI) has transformed social media into a powerful tool for educational learning engagements. WeChat, a Chinese social media platform, is extensively used by students for academic learning, collaboration, and information acquisition. However, research on the adoption of AI-integrated social media for learning engagement remains limited, especially in the Chinese context. Based on the traditional Technology Acceptance Model (TAM), this study proposes an AI-Integrated Social Media Adoption (AISMA) model for learning engagement by integrating the core TAM constructs—perceived usefulness (PU) and perceived ease of use (PEOU), with the external factors of collaborative learning, social support, resource sharing, and facilitating conditions to investigate their influence on students’ intention to adopt AI-integrated WeChat for learning engagement. Data from Chinese university students were analyzed using structural equation modeling (SEM) via Smart-PLS 4. The results confirm that collaborative learning, social support, and resource sharing are significant direct drivers of AI-integrated social media adoption. Furthermore, facilitating conditions significantly influence both PU and PEOU and also directly drive adoption intention. A key finding from the moderating analysis is that PEOU significantly amplifies the effects of all three external factors (collaborative learning, social support, and resource sharing) on adoption intention. While PU significantly strengthens the relationship for social support and resource sharing, it does not moderate the link between collaborative learning and adoption intention. The study concludes by discussing the theoretical contributions and practical implications for educators and platform developers.
Similar content being viewed by others
Data availability
The raw data will be made available upon reasonable request to the corresponding author.
References
Bashiri, M. & Kowsari, K. Transformative Influence of LLM and AI Tools in Student Social Media Engagement: Analyzing Personalization, Communication Efficiency, and Collaborative Learning. arXiv preprint: arXiv:2407.15012 (2024).
Sadiku, M. N. et al. Artificial intelligence in social media. Int. J. Sci. Adv. 2 (1), 15–20 (2021).
Shahzad, M. F. et al. Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning. Heliyon 10(8) (2024).
Carr, C. T. & Hayes, R. A. Social media: Defining, developing, and divining. Atl. J. Communication. 23 (1), 46–65 (2015).
McCay-Peet, L. & Quan-Haase, A. What is social media and what questions can social media research help us answer. In The SAGE Handbook of Social Media Research Methods. 13–26. (2017).
Mishra, S. Social networks, social capital, social support and academic success in higher education: A systematic review with a special focus on ‘underrepresented’students. Educational Res. Rev. 29, p100307 (2020).
Barrot, J. S. Social media as a language learning environment: A systematic review of the literature (2008–2019). Computer-assisted Lang. Learn. 35 (9), 2534–2562 (2022).
Sobaih, A. E. E., Hasanein, A. M. & Abu Elnasr, A. E. Responses to COVID-19 in higher education: Social media usage for sustaining formal academic communication in developing countries. Sustainability 12 (16), 6520 (2020).
Cavus, N. et al. Efficacy of social networking sites for sustainable education in the era of COVID-19: A systematic review. Sustainability 13 (2), 808 (2021).
Astatke, M., Weng, C. & Chen, S. A literature review of the effects of social networking sites on secondary school students’ academic achievement. Interact. Learn. Environ. 31 (4), 2153–2169 (2023).
Ahmed, Y. A. et al. Social media for knowledge-sharing: A systematic literature review. Telematics Inform. 37, 72–112 (2019).
Zachos, G., Paraskevopoulou-Kollia, E. A. & Anagnostopoulos, I. Social media use in higher education: A review. Educ. Sci. 8 (4), 194 (2018).
Tower, M., Latimer, S. & Hewitt, J. Social networking as a learning tool: Nursing students’ perception of efficacy. Nurse Educ. Today. 34 (6), 1012–1017 (2014).
Cheung, C. M., Chiu, P. Y. & Lee, M. K. Online social networks: Why do students use facebook? Comput. Hum. Behav. 27 (4), 1337–1343 (2011).
O’Dowd, R. What do students learn in virtual exchange? A qualitative content analysis of learning outcomes across multiple exchanges. Int. J. Educational Res. 109, 101804 (2021).
Al-Rahmi, A. M. et al. The influence of information system success and technology acceptance model on social media factors in education. Sustainability 13 (14), 7770 (2021).
Muliadi, M. et al. The information sharing among students on social media: The role of social capital and trust. VINE J. Inform. Knowl. Manage. Syst. 54 (4), 823–840 (2024).
Chowdhury, E. K. Examining the benefits and drawbacks of social media usage on academic performance: A study among university students in Bangladesh. J. Res. Innov. Teach. Learn. (2024).
Mpungose, C. B. Are social media sites a platform for formal or informal learning? Students’ experiences in institutions of higher education. Int. J. High. Educ. 9 (5), 300–311 (2020).
Gan, B., Menkhoff, T. & Smith, R. Enhancing students’ learning process through interactive digital media: New opportunities for collaborative learning. Comput. Hum. Behav. 51, 652–663 (2015).
Ullah, R. S. et al. Studying How AI Tools Integrated with Social Media Can Help Teachers Manage Classrooms by Providing Insights into Student Behavior, Engagement Levels, and Areas Needing Improvement. Vol. 7. 413–426 (Review of Education, Administration & Law, 2024).
Fazal, I. et al. Bridging educational gaps: The role of AI and social media in enhancing access to quality education in under-privileged communities. Crit. Rev. Social Sci. Stud. 3 (1), 2413–2431 (2025).
Parra, J. L. & Chatterjee, S. Social media and artificial intelligence: Critical conversations and where do we go from here? Educ. Sci. 14 (1), 68 (2024).
Lin, H. & Chen, Q. Artificial intelligence (AI)-integrated educational applications and college students’ creativity and academic emotions: Students and teachers’ perceptions and attitudes. BMC Psychol. 12 (1), 487 (2024).
Harlow How AI is Transforming Social Media Learning for Students. https://onstipe.com/blog/how-ai-is-transforming-social-media-learning-for-students/(2025). Accessed 13 Nov 2025.
Arshad, M. & Akram, M. S. Social media adoption by the academic community: Theoretical insights and empirical evidence from developing countries. Int. Rev. Res. Open. Distrib. Learn., 19(3). (2018).
Khan, M. N. et al. Social media for knowledge acquisition and dissemination: The impact of the COVID-19 pandemic on collaborative learning driven social media adoption. Front. Psychol. 12, 648253 (2021).
Alismaiel, O. A., Cifuentes-Faura, J. & Al-Rahmi, W. M. Social media technologies used for education: An empirical study on TAM model during the COVID-19 pandemic. In Frontiers in Education (Frontiers Media SA, 2022).
Lai-Lin, T. Social Media in China - Statistics & Facts. https://www.statista.com/topics/1170/social-networks-in-china/#topicOverview. Accessed 18 Nov 2023 (2023).
Gao, C. AI Revolution: How Chinese Social Apps Integrate AI. https://www.theegg.com/social/china/how-chinese-social-apps-integrate-ai/. Accessed 14 Nov 2025 (2025).
Pang, H. & Zhang, K. Determining influence of service quality on user identification, belongingness, and satisfaction on mobile social media: Insight from emotional attachment perspective. J. Retailing Consumer Serv. 77, 103688 (2024).
Pang, H. et al. Do network externalities and perceived gratifications boost WeChat continued use? Combining perspectives of network externalities and uses and gratifications. Acta. Psychol. 251, 104610 (2024).
Pang, H. & Zhang, K. How multidimensional benefits determine cumulative satisfaction and eWOM engagement on mobile social media: Reconciling motivation and expectation disconfirmation perspectives. Telematics Inform. 93, 102174 (2024).
Fengi, C. Tencent adds AI chatbot friend to WeChat to keep users glued to super app. https://www.scmp.com/tech/big-tech/article/3303934/tencent-adds-ai-chatbot-friend-wechat-keep-users-glued-super-app?module=perpetual_scroll_0&pgtype=article. Accessed 13 Nov 2025 (2025).
Dong, C. Tencent launches AI assistant app Yuanbao, linking it to WeChat ecosystem. https://technode.com/2024/05/31/tencent-launches-ai-app-yuanbao-that-brings-rich-content-of-the-wechat-ecosystem/. Accessed 13 Nov 2025 (2024).
Yu, H. Yuanbao deeply integrates with WeChat. https://longbridge.com/en/news/236390989. Accessed 13 Nov 2025 (2025).
Ran, L. Weekend Read | The built-in yuanbao in the WeChat chat box indicates the beginning of another round of evolution for the super app. https://news.futunn.com/en/post/54831290/weekend-read-the-built-in-yuanbao-in-the-wechat-chat?level=1&data_ticket=1763040023189352. Accessed 13 Nov 2025 (2025).
Kovari, A. A systematic review of AI-powered collaborative learning in higher education: Trends and outcomes from the last decade. Social Sci. Humanit. Open. 11, 101335 (2025).
Li, C., Du, X. & Xiao, P. Revolutionising education: Leveraging AI to boost student engagement through constructivist and social collaborative learning-A study of Perusall. In UK Academy for Information Systems Conference Proceedings. Vol. 25 (2025).
Islam, A. et al. An evaluation of AI-enhanced collaborative learning platforms. In International Conference on Communication, Computer Sciences and Engineering (IC3SE). 2024. Gautam Buddha Nagar, India. 207–211. https://doi.org/10.1109/IC3SE62002.2024.10593320 (IEEE, 2024).
Ajzen, I. The theory of planned behavior. Organ. Behav. Hum Decis. Process. 50 (2), 179–211 (1991).
Davis, F. D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 319–340 (1989).
Lorettu, L., Piu, D. & Bellizzi, S. Uncertainty in pandemic times. In Anxiety, Uncertainty, and Resilience During the Pandemic Period-Anthropological and Psychological Perspectives. (IntechOpen, 2021).
Li, L. A critical review of technology acceptance literature. Referred Res. Paper. 4, p2010 (2010).
Al-Qaysi, N., Mohamad-Nordin, N. & Al-Emran, M. Employing the technology acceptance model in social media: A systematic review. Educ. Inform. Technol. 25, 4961–5002 (2020).
Agha, M. Q. Artificial intelligence in social media: Opportunities and perspectives. Cihan University-Erbil J. Humanit. Social Sci. 9 (1), 125–132 (2025).
Kurki, A. L., Koskela, I. & Yli-Kaitala, K. Collaborative development on a digital learning platform: The potential of AI tools for facilitation. Hum. Technol. 20 (3), 525–540 (2024).
Onyejele, F. O., Otigbuo, P. & Nwaokoma, I. A. Collaborative learning in the age of AI: Fostering social interaction and co-operation. J. Educ. Developing Areas. 32 (2), 27–36 (2024).
Biton, Y. & Segal, R. Students’ Insights on Learning Mathematics Via the WhatsApp Social Network, in Social Media in the Changing Mathematics Classroom. 153–172 (Springer, 2025).
Meng, J. et al. Mediated social support for distress reduction: AI chatbots vs. human. Proc. ACM Hum Comput Interact. 7 (CSCW1), 1–25 (2023).
Tan, Y. & Ren, R. WeChat dual-group (mis-) use: a qualitative exploration of Chinese undergraduate students’ use of mobile social media applications and its pedagogical implications. Front. Psychol. 16, 1686513 (2025).
Degu, T. Decentralized Key Protocol (DKP): A Hybrid Framework for Privacy-Preserving File Storage and Sharing in Social Networking. Available at SSRN 5241211 (2025).
Abbad, M. Adopting social networks in higher education: a study on Facebook as e-learning tool. Int. J. Bus. Inform. Syst. 48 (3), 353–376 (2025).
Al-Shorman, H. M. et al. Evaluating artificial intelligence integration in education through integrating TAM and S–O–R. In Intelligence-Driven Circular Economy: Regeneration Towards Sustainability and Social Responsibility. Vol. 1. 353–367 (Springer, 2025).
Resta, P. & Laferrière, T. Technology in support of collaborative learning. Educational Psychol. Rev. 19, 65–83 (2007).
Laal, M. & Ghodsi, S. M. Benefits of collaborative learning. Procedia-social Behav. Sci. 31, 486–490 (2012).
Abdullah, Z. et al. Zakat institutions’ adoption of social media. J. Islamic Acc. Bus. Res. (2023).
Arif, M. & Kanwal, S. Adoption of social media technologies and their impact on students’ academic performance: The only way for future survival of distance education students in Pakistan. Pakistan J. Inform. Manage. Libr. 18 (1), 25–36 (2016).
Al-rahmi, W. M., Othman, M. S. & Yusuf, L. M. Social media for collaborative learning and engagement: Adoption framework in higher education institutions in Malaysia. Mediterranean J. Social Sci. 6 (3 S1), 246 (2015).
Cobb, S. Social support as a moderator of life stress. Psychosom. Med. 38 (5), 300–314 (1976).
McKay, G. Social support, stress and the buffering hypothesis: A theoretical analysis. In Handbook of Psychology and Health (Andrew, B. & Shelley, T. et al. Eds.). 253–267 (Erlbaum, 1984).
Longest, K. & Kang, J. A. Social media, social support, and mental health of young adults during COVID-19. Front. Communication. 7, 828135 (2022).
Selkie, E. et al. Transgender adolescents’ uses of social media for social support. J. Adolesc. Health. 66 (3), 275–280 (2020).
Naslund, J. A. et al. The future of mental health care: Peer-to-peer support and social media. Epidemiol. Psychiatric Sci. 25 (2), 113–122 (2016).
Taylor, S. E. & Friedman, H. Social support. Found. Health Psychol. 145171 (2007).
Naslund, J. et al. The future of mental health care: Peer-to-peer support and social media. Epidemiol. Psychiatr Sci. 25 (2), 113–122. https://doi.org/10.1017/S2045796015001067 (2016).
Lu, W. & Hampton, K. N. Beyond the Power of Networks: Differentiating Network Structure from Social Media Affordances for Perceived Social Support. Vol. 19. 861–879 (New Media & Society, 2017).
DeAndrea, D. C. et al. Serious social media: On the use of social media for improving students’ adjustment to college. Internet High. Educ. 15 (1), 15–23 (2012).
Kaplan, A. M. & Haenlein, M. Users of the world, unite! The challenges and opportunities of social media. Bus. Horiz. 53 (1), 59–68 (2010).
Prestridge, S. Categorising teachers’ use of social media for their professional learning: A self-generating professional learning paradigm. Comput. Educ. 129, 143–158 (2019).
Venkatesh, V. et al. User acceptance of information technology: Toward a unified view. MIS Q.. 425–478 (2003).
Aggelidis, V. P. & Chatzoglou, P. D. Using a modified technology acceptance model in hospitals. Int. J. Med. Informatics. 78 (2), 115–126 (2009).
Im, I., Hong, S. & Kang, M. S. An international comparison of technology adoption: Testing the UTAUT model. Inf. Manag. 48 (1), 1–8 (2011).
Grover, P., Kar, A. K. & Dwivedi, Y. K. Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions. Ann. Oper. Res. 308 (1–2), 177–213 (2022).
Teo, T. Examining the influence of subjective norm and facilitating conditions on the intention to use technology among pre-service teachers: a structural equation modeling of an extended technology acceptance model. Asia Pac. Educ. Rev. 11, 253–262 (2010).
Puriwat, W. & Tripopsakul, S. Explaining social Media adoption for a business purpose: An application of the UTAUT model. Sustainability 13(4), 2082 (2021).
Sukendro, S. et al. Using an extended technology acceptance model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context. Heliyon 6(11) (2020).
Man, S. S. et al. Acceptance of online mapping technology among older adults: Technology acceptance model with facilitating condition, compatibility, and self-satisfaction. ISPRS Int. J. Geo-Information. 11 (11), 558 (2022).
Khan, M. I., Saleh, M. A. & Quazi, A. Social media adoption by health professionals: A TAM-based study. In Informatics (MDPI, 2021).
So, H. J. & Brush, T. A. Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: Relationships and critical factors. Comput. Educ. 51 (1), 318–336 (2008).
Bock, G. W. et al. Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Q. 87–111 (2005).
Baabdullah, A. M. et al. Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model. Int. J. Inf. Manag. 44, 38–52 (2019).
Pokhrel, L. Social media adoption intention among undergraduate business schools students in Kathmandu Valley: A structural equation modeling (SEM) approach. Quest J. Manage. Social Sci. 3 (2), 204–216 (2021).
Bradley, C. Translation of questionnaires for use in different languages and cultures. In Handbook of Psychology and Diabetes. 43–55 (Routledge, 2013).
Chaudhary, A. K. & Israel, G. D. The Savvy Survey# 8: Pilot testing and pretesting questionnaires: AEC402/PD072, 12/2014. EDIS 2015 (1), 6–6 (2015).
Van Teijlingen, E. & Hundley, V. The importance of pilot studies. Nurs. Standard. 16 (40), 33–36 (2002).
Golzar, J., Noor, S. & Tajik, O. Convenience sampling. Int. J. Educ. Lang. Stud. 1 (2), 72–77 (2022).
Ahmed, S. K. How to choose a sampling technique and determine sample size for research: A simplified guide for researchers. Oral Oncol. Rep. 12, 100662 (2024).
Obilor, E. I. Convenience and purposive sampling techniques: Are they the same. Int. J. Innovative Social Sci. Educ. Res. 11 (1), 1–7 (2023).
Hair, J. F. et al. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 31 (1), 2–24 (2019).
Sujati, H. & Akhyar, M. Testing the construct validity and reliability of curiosity scale using confirmatory factor analysis. J. Educ. Soc. Res. 20(4) (2020).
Cheung, G. W. et al. Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pac. J. Manag. 41 (2), 745–783 (2024).
Fornell, C. & Larcker, D. F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18 (1), 39–50 (1981).
Hair, J. F., Ringle, C. M. & Sarstedt, M. PLS-SEM: Indeed a silver bullet. J. Mark. Theory Pract. 19 (2), 139–152 (2011).
Chin, W. W. Commentary: Issues and opinion on structural equation modeling. JSTOR. vii–xvi (1998).
Shmueli, G. et al. Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. Eur. J. Mark. 53 (11), 2322–2347 (2019).
Chowdhury, E. K. Examining the benefits and drawbacks of social media usage on academic performance: A study among university students in Bangladesh. J. Res. Innovative Teach. Learn. 18 (2), 395–411 (2025).
Almogren, A. S., Al-Rahmi, W. M. & Dahri, N. A. Integrated Technological Approaches to Academic Success: Mobile Learning, Social Media, and AI in Higher Education. (IEEE Access, 2024).
Luchtefeld, C. & Jordan, K. D. Individual differences influencing the relationship between online social support and addictive use of social media. Telematics Inf. Rep. 8, 100025 (2022).
Pokhrel, L. Intention of social media adoption among undergraduate students of business schools in Kathmandu valley. J. Bus. Manage. Res. 4 (01), 34–48 (2022).
Talwar, M. et al. Why do consumers resist digital innovations? An innovation resistance theory perspective. Int. J. Emerg. Markets. 19 (11), 4327–4342 (2024).
Cloarec, J., Meyer-Waarden, L. & Munzel, A. Transformative privacy calculus: Conceptualizing the personalization‐privacy paradox on social media. Psychol. Mark. 41 (7), 1574–1596 (2024).
Hossain, Z. A modified innovation resistance theory approach to E-Tourism resistance intention in Bangladesh. J. Technol. Manage. Innov. 18 (4), 59–71 (2023).
Baklouti, F. & Boukamcha, F. Consumer resistance to internet banking services: Implications for the innovation resistance theory. J. Financial Serv. Mark. 29 (2), 364–376 (2024).
Peng, M. H. & Hwang, H. G. An empirical study to explore the adoption of e-learning social media platform in Taiwan: An integrated conceptual adoption framework based on technology acceptance model and technology threat avoidance theory. Sustainability 13 (17), 9946 (2021).
Yu, T. et al. To use or not to use? Generative AI adoption in Chinese business schools. Int. J. Manage. Educ. 24 (1), 101323 (2026).
Ajibade, S. & Zaidi, A. Technological acceptance model for social media networking in e-learning in higher educational institutes. Int. J. Inform. Educ. Technol. 13(2) (2023).
Nilashi, M. & Abumalloh, R. A. i-TAM: A model for immersive technology acceptance. Educ. Inform. Technol. 30 (6), 7689–7717 (2025).
Liu, S. et al. Social media-based collaborative learning effects on student performance/learner performance with moderating role of academic self-efficacy. Front. Psychol. 13, 903919 (2022).
Funding
This research was supported by the High-Level Talent Introduction Program and Hubei Private Enterprise Innovation and Development Research Center (HPEIDRC) of Wuhan College.
Author information
Authors and Affiliations
Contributions
All authors (Isaac Kofi Mensah and Muhammad Khalil Khan) contributed equally to the original draft writing, research design, method, data collection, data analysis, review, and proofreading of this manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethics statement
This study strictly follows the institutional and 2024 Declaration of Helsinki ethical guidelines. The ethical approval (No. NTU-IRB25-SML13) of the study was granted by the Institutional Review Board (IRB) of NingboTech University, Ningbo, China.
Informed consent
Informed consent was obtained from all participants.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Mensah, I.K., Khan, M.K. Artificial intelligence integrated WeChat social media adoption for collaborative learning engagement among university students. Sci Rep (2026). https://doi.org/10.1038/s41598-026-40611-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-40611-6
