Generative Artificial Intelligence and Mental Well-Being of University Students. A Structural Equation Modeling (SEM) Based- Analysis
DOI:
https://doi.org/10.70641/ajbds.v1i2.135Keywords:
Generative artificial intelligence, mental well-being, university, studentsAbstract
The emergence and application of generative artificial intelligence (GAI), typified as ChatGPT and others have the potential for significant impact on the mental well-being. However, there is currently a lack of systematic research on GAI on mental well-being particularly among university students in Kenya. The purpose was to conduct an exploratory study on the relationship between generative artificial intelligence and mental well-being (MWB) among university students in Kenya. The study used convenience sampling technique. The data was collected from 458 respondents using a structured, closed-ended, self-administered questionnaire. It was analyzed through partial least squares structural equation modeling (PLS-SEM), which is frequently used for prediction models. The model was further checked for goodness-of-fit using Amos. The findings of this study establishes that generative artificial intelligence has a positive and significant influence on mental well-being (β = 0.129, t = 1.997, p < 0.046) among university students. These revelations contribute to the discourse on technology-enhanced education, showing that embracing GAI can have a positive impact on student mental well-being. The study recommends the university administrators to prioritize investment in generative artificial intelligence technologies with the view of enhancing students’ mental wellbeing as they undergo their university education.
References
Alharbi, W. (2023). AI in the foreign language classroom: A pedagogical overview of automated writing assistance tools. Education Research International, 3, 1–15. DOI: https://doi.org/10.1155/2023/4253331
Al-Mamary, Y. H., Alfalah, A. A., Alshammari, M. M., & Abubakar, A.A. (2024). Exploring factors influencing university students’ intentions to use ChatGPT: analysing tasktechnology fit theory to enhance behavioural intentions in higher education. Future Business Journal 10 (1), https://doi.org/10.1186/s43093-024- 00406-5. DOI: https://doi.org/10.1186/s43093-024-00406-5
Anderson, K.C., Knight, D.K., Pookulangara, S., & Josiam, B. (2014). Influence of hedonic and utilitarian motivations on retailer loyalty and purchase intention: a Facebook perspective. Journal of Retailing and Consumer Services, 21 (5), 773-779. DOI: https://doi.org/10.1016/j.jretconser.2014.05.007
Bagozzi, R. P., & Yi, Y.-J. (1988). On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16, 74-94. https://doi.org/10.1007/BF02723327. DOI: https://doi.org/10.1177/009207038801600107
Baidoo-Anu, D., & Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62. DOI: https://doi.org/10.61969/jai.1337500
Barrett, A., & Pack, A. (2023). Not quite eye to AI: Student and teacher perspectives on the use of generative artificial intelligence in the writing process. International Journal of Educational Technology in Higher Education, 20, 59. DOI: https://doi.org/10.1186/s41239-023-00427-0
Biswas, S. (2023). Role of ChatGPT in education. https://ssrn.com/abstract=4369981. DOI: https://doi.org/10.2139/ssrn.4403584
Bolick, A.D. & da Silva, R.L. (2024). Exploring artificial intelligence tools and their potential impact to instructional design workflows and organizational systems. TechTrends, Linking Research and Practice to Improve Learning. A Publication of the Association for Educational Communications and Technology, 68 (1), 91-100, doi: 10.1007/s11528-023-00894-2. DOI: https://doi.org/10.1007/s11528-023-00894-2
Byrne, B.M. (2010). Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming, 2nd ed., Routledge, New York, NY.
Cao, Y., Li, S., Yau, Z., Dai, Y., Yu, S.P., & Sun, L. (2023). A comprehensive survey of ai-generated content (aigc): a history of generative ai from gan to chatgpt, arXiv preprintarXiv:2303.04226230304226, https://doi.org/10.48550/arXiv.2303.04226.
Chakraborty, D., Kumar, A., Patre, S., & Gupta, S. (2024). Enhancing trust in online grocery shopping through generative AI chatbots. Journal of Business Research, 180, 114737. DOI: https://doi.org/10.1016/j.jbusres.2024.114737
Chang, W., & Park, J. (2024). A comparative study on the effect of ChatGPT recommendation and AI recommender systems on the formation of a consideration set. Journal of Retailing and Consumer Services, 78, 103743. DOI: https://doi.org/10.1016/j.jretconser.2024.103743
Conroy, D., Chadwick, D., Fullwood, C., & Lloyd, J. (2023). You have to know how to live with it without getting to the addiction part: British young adult experiences of smartphone overreliance and disconnectivity. Psychology of Popular Media, 12(4), 471–480. https://doi.org/10.1037/ppm0000425. DOI: https://doi.org/10.1037/ppm0000425
Cooper, G. (2023). Examining science education in chatgpt: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32, 444–452. DOI: https://doi.org/10.1007/s10956-023-10039-y
Cuello-Garcia, C., P´erez-Gaxiola, G., & van Amelsvoort, L. (2020). Social media can have an impact on how we manage and investigate the COVID-19 pandemic. J Clin Epidemiol; 127:198–201. https://doi.org/10.1016/j.jclinepi.2020.06.028. DOI: https://doi.org/10.1016/j.jclinepi.2020.06.028
Dawoodbhoy, F.M., Delancy, J., Cecula, P., Yu, J., Peacock, J., Tan, J., & Cox, B. (2021). AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units, Heliyon 7 (5) (2021), https://doi.org/10.1016/j.heliyon.2021.e06993.
Dempere, J., Modugu, K. P., Hesham, A., & Ramasamy, L. (2023). The impact of ChatGPT on higher education. Frontiers in Education, 8, Article 1206936. DOI: https://doi.org/10.3389/feduc.2023.1206936
EY.com (2024). How GenAI will help shape the global economy, https://www.ey.com/en_gl/insights/ai/how-gen-ai-will-help-shape-global-conomy.
Eysenbach, G. (2023). The role of chatgpt, generative language models, and artificial intelligence in medical education: A conversation with chatgpt and a call for papers. JMIR Medical Education, 9(1), e46885. DOI: https://doi.org/10.2196/46885
Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18 (1), 39-50. DOI: https://doi.org/10.1177/002224378101800104
Gawrych, M. (2022). Internet addiction in light of social connectedness and connectedness to nature. European Psychiatry, 65(S1), S596–S596. https://doi.org/10.1192/j.eurpsy.2022.1526. DOI: https://doi.org/10.1192/j.eurpsy.2022.1526
Goldman Sachs (2023). Generative AI could raise global GDP by 7%. Accessed (Dec 10, 2024) at: https://www.goldmansachs.com/intelligence/pages/generative-ai-could -raise-global-gdp-by-7-percent.html.
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., & Tatham, R.L. (1998). Multivariate data analysis. Multivariate Data Analysis, 5 (3), 207-219.
Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2018). Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA: Sage. https://doi.org/10.3926/oss.37. DOI: https://doi.org/10.3926/oss.37
Intelliarts.com (2025. The Evolution of ChatGPT: Vital Statistics and Trends for 2025, https://intelliarts.com/blog/chatgpt-statistics/
Javaid, M., Haleem, A., & Pratap, R. (2023). ChatGPT for healthcare services: An emerging stage for an innovative perspective, BenchCouncil Trans. Benchmarks, Stand. Eval 3 (1), 100105, https://doi.org/10.1016/j.tbench.2023.100105. DOI: https://doi.org/10.1016/j.tbench.2023.100105
Kaiser, H. F., & Rice, J. (1974). Little Jiffy, Mark IV. Educational and Psychological Measurement, 34(1), 111–117. https://doi.org/10.1177/001316447403400115 DOI: https://doi.org/10.1177/001316447403400115
Katz, E. (1959). Mass communications research and the study of popular culture: An editorial note on a possible future for this journal. In: Departmental Papers (ASC), 165.
Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. Public Opinion Quarterly, 37(4), 509–523. https://doi.org/10.1086/268109. DOI: https://doi.org/10.1086/268109
Kim, M.J., Lee, C.K., & Preis, M.W. (2020). The impact of innovation and gratification on authentic experience, subjective well-being, and behavioral intention in tourism virtual reality: The moderating role of technology readiness. Telematics Inform. 49, 101349. DOI: https://doi.org/10.1016/j.tele.2020.101349
Kirk, C., & Givi, J. (2025). The AI-authorship effect: Understanding authenticity, moral disgust, and consumer responses to AI-generated marketing communications. Journal of Business Research, 186, 114984. DOI: https://doi.org/10.1016/j.jbusres.2024.114984
Kissinger, H.A., Schmidt, E., Huttenlocher, D. (2021). The Age of AI. Hachette UK.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of E-Collaboration, 11, 1–10. https://doi.org/ 10.4018/ijec.2015100101. DOI: https://doi.org/10.4018/ijec.2015100101
Lee, H., & Cho, C.H., (2020). Uses and gratifications of smart speakers: Modelling the effectiveness of smart speaker advertising. Int. J. Advert. 39 (7), 1150–1171. DOI: https://doi.org/10.1080/02650487.2020.1765657
Lim, W.M., Gunasekara, A., Pallant, J.L., Pallant, J.I,. & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21 (2), 100790, doi: 10. 1016/j.ijme.2023.100790. DOI: https://doi.org/10.1016/j.ijme.2023.100790
Liu, J., Wang, C., & Liu, S. (2023). Utility of ChatGPT in clinical practice. J. Med. Internet Res. 25 e48568, https://doi.org/10.2196/48568. DOI: https://doi.org/10.2196/48568
Luo, M.M., Chea, S., & Chen, J.S. (2011). Web-based information service adoption: a comparison of the motivational model and the uses and gratifications theory. Decision Support Systems, 51 (1), 21-30. DOI: https://doi.org/10.1016/j.dss.2010.11.015
Mahapatra, S. (2024). Impact of chatgpt on esl students’ academic writing skills: A mixed methods intervention study. Smart Learning Environments, 11, 9. DOI: https://doi.org/10.1186/s40561-024-00295-9
Martinelli, D. (2022). Generative machine learning for de novo drug discovery: A systematic review. Computers in Biology and Medicine, 145, 105403. DOI: https://doi.org/10.1016/j.compbiomed.2022.105403
Mousavi Baigi, S.F., Sarbaz, M., Ghaddaripouri, K., Ghaddaripouri, M., Mousavi, A.S., & Kimiafar, K. (2023). Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: a systematic review, Heal. Sci. Reports 6 (3), https://doi.org/10.1002/hsr2.1138. DOI: https://doi.org/10.1002/hsr2.1138
Ng, D. T. K., Tan, C. W., & Leung, J. K. L. (2024). Empowering student self-regulated learning and science education through chatgpt: A pioneering pilot study. British Journal of Educational Technology. DOI: https://doi.org/10.1111/bjet.13454
Nunnally, J.C., & Bernstein, I.H. (1994). Psychometric Theory, 3rd ed., McGraw-Hill, New York, NY.
Pedersen, I. (2023). The rise of generative AI and enculturating AI writing in postsecondary education. Frontiers in Artificial Intelligence 6, 1259407, doi: 10.3389/frai.2023.1259407. DOI: https://doi.org/10.3389/frai.2023.1259407
Rauschnable, P.A. (2018). Virtually enhancing the real world with holograms: An exploration of expected gratifications of using augmented reality smart glasses. Psychol. Mark. 35 (8), 557–572. DOI: https://doi.org/10.1002/mar.21106
Redahan, M., & Kelly, B.D. (2024). Artificial intelligence and mental capacity legislation: Opening Pandora’s modem. International Journal of Law and Psychiatry 94, https://doi.org/10.1016/j.ijlp.2024.101985. DOI: https://doi.org/10.1016/j.ijlp.2024.101985
Reychav, I., & Wu, D. (2014). Exploring mobile tablet training for road safety: a uses and gratifications perspective. Computers and Education, 71, 43-55. DOI: https://doi.org/10.1016/j.compedu.2013.09.005
Rice, S., Crouse, S. R., Winter, S. R., & Rice, C. (2024). The advantages and limitations of using chatgpt to enhance technological research. Technology in Society, 76, Article 102426. DOI: https://doi.org/10.1016/j.techsoc.2023.102426
Ruggiero, T. E. (2000). Uses and gratifications theory in the 21st century. Mass Communication and Society, 3 (1), 3–37. https://doi.org/10.1207/ s15327825mcs0301_02. DOI: https://doi.org/10.1207/S15327825MCS0301_02
Sallam, M., Salam, N. A., Barakat, M., & Al-Tammemi, A.B. (2023). ChatGPT applications in medical, dental, pharmacy, and public health education: A descriptive study highlighting the advantages and limitations, Narrative J 3 (1), e103, https://doi.org/10.52225/narra.v3i1.103. DOI: https://doi.org/10.52225/narra.v3i1.103
Schepman, A., Rodway, P. (2020). Initial validation of the general attitudes towards artificial intelligence scale. Comput. Hum. Behav. Rep. 1, 100014 https://doi.org/ 10.1016/j.chbr.2020.100014. DOI: https://doi.org/10.1016/j.chbr.2020.100014
Schreiber, J. B. (2008). Core reporting practices in structural equation modeling. Research in Social and Administrative Pharmacy, 4, 83–97. https://doi.org/10.1016/j. sapharm.2007.04.003. DOI: https://doi.org/10.1016/j.sapharm.2007.04.003
Sekaran, U., & Bougie, R. (2016). Research Methods for Business A Skill-Building Approach. 7th Edition, Wiley & Sons, West Sussex.
Shahzad, M.F., Xu, S., Lim, W.M; Yang, X., & Khan, Q.R. (2024). Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning. Research Journal of Textile and Apparel, 28 (4), 2024. DOI: https://doi.org/10.1016/j.heliyon.2024.e29523
Sindermann, C., Sha, P., Zhou, M., Wernicke, J., Schmitt, H.S., Li, M., Sariysk, R., Stavron, M., Becker, B & Montage, C. (2021). Assessing the attitude towards Artificial Intelligence: Introduction of a short measure in German, Chinese, and English Language. Künstl Intell 35, (1) 109–118, https://doi.org/10.1007/s13218-020-00689-0 DOI: https://doi.org/10.1007/s13218-020-00689-0
Song, X., Zhang, J., Yan, P., Hahn, J., Kruger, U., & Mohamed, H. (2024). Integrating AI in college education: Positive yet mixed experiences with ChatGPT. Meta-Radiology 2, https://doi.org/10.1016/j.metrad.2024.100113. DOI: https://doi.org/10.1016/j.metrad.2024.100113
Statista.com (2024). Generative artificial intelligence (AI) market size worldwide from 2020 to 2030, https://www.statista.com/forecasts/1449838/generative-ai-market-size-worldwide.
Su, J., & Yang, W. (2023). Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Review of Education, 6 (3), 355-366, doi: 10.1177/20965311231168423. DOI: https://doi.org/10.1177/20965311231168423
Suleyman, M., & Bhaskar, M. (2023). The Coming Wave: Technology, Power, and the Twenty-first Century’s Greatest Dilemma. Crown. DOI: https://doi.org/10.17104/9783406814143
Sun, Y., & Wang, S. (2020). Understanding consumers’ intentions to purchase green products in the social media marketing context. Asia Pacific Journal of Marketing Logistics, 32, 860–878. https://doi.org/10.1108/APJML-03-2019-0178. DOI: https://doi.org/10.1108/APJML-03-2019-0178
Sutanto, J., Palme, E., Tan, C.H., & Phang, C.W. (2013). Addressing the personalization-privacy paradox: an empirical assessment from a field experiment on smartphone users. MIS Quarterly, 37 (4), 1141-1164. DOI: https://doi.org/10.25300/MISQ/2013/37.4.07
Tabachnick, B.G., & Fidell, L.S. (2001). Using Multivariate Statistics, 4th ed., Allyn and Bacon, Needham Heights, MA.
Tabachnick, B.G., Fidell, L.S., & Ullman, J.B. (2007). Using Multivariate Statistics, 5 Pearson, Boston, MA, 481-498.
The Economist. (2023). The race of the AI labs heats up. The Economist. https://www. economist.com/business/2023/01/30/the-race-of-the-ai-labs-heats-up.
Van Wyk, M. M. (2024). Is chatgpt an opportunity or a threat? Preventive strategies employed by academics related to a genAI-based LLM at a faculty of education. Journal of Applied Learning and Teaching, 7. DOI: https://doi.org/10.37074/jalt.2024.7.1.15
Wang, J. (2023). The relationship between loneliness and consumer shopping channel: Evidence from China. Journal of retailing and consumer services, Elsevier 7 (C), DOI: 10.1016/j.jretconser.2022.103125 DOI: https://doi.org/10.1016/j.jretconser.2022.103125
Young, L., Kolubinski, D.C., & Frings, D. (2020). Attachment style moderates the relationship between social media use and user mental health and wellbeing, Heliyon 6 (6) e04056, https://doi.org/10.1016/j.heliyon.2020.e04056. DOI: https://doi.org/10.1016/j.heliyon.2020.e04056
Zastudil, C., Rogalska, M., Kapp, C., Vaughn, J., & MacNeil, S. (2023). Generative AI in computing education: Perspectives of students and instructors. In 2023 IEEE frontiers in education conference (FIE). IEEE 1–9. DOI: https://doi.org/10.1109/FIE58773.2023.10343467
Zewude, G. T., Bereded, D. G., Abera, E., Tegegne, G., Goraw, S., & Segon, T. (2024). The impact of internet addiction on mental health: Exploring the mediating effects of positive psychological capital in university students. Adolescents, 4(2), 200–221. https://doi.org/10.3390/adolescents4020014. DOI: https://doi.org/10.3390/adolescents4020014
Zirar, A., Ali, S. I., & Islam, N. (2023). Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda. Technovation, 124, 102747. DOI: https://doi.org/10.1016/j.technovation.2023.102747
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