Assessing Students' Satisfaction with AI Tools in Higher Education

Nowadays, AI-powered applications are increasingly integrated into academic fields, and numerous studies have discussed the acceptance of this technology among higher education students. With AI worldwide establishment, empirical research remains necessary to evaluate user satisfaction, effectiveness, and long-term sustainability. Since cultural, social, and economic factors influence how AI is implemented in education, the level of acceptance of AI tools among students also may vary from one country to another. This study aims to explore university students' satisfaction with AI tools in the context of higher education in Malaysia, specifically in Kelantan. This study examines how satisfied students are with AI technologies used for their learning, with an emphasis on emotional well-being, content quality, and perceived utility of the tools. Using a cross-sectional approach, 105 undergraduate students from various faculties at Universiti Teknologi MARA (UiTM) Kelantan were selected to participate in the study. Students were given a self-administered questionnaire using Google Forms to obtain the data. Simple random sampling was used in the study, and the data analysis was conducted using Multiple Linear Regression (MLR). The findings showed that the only significant variables influencing students' satisfaction with AI tools in their education are emotional well-being and perceived utility, while the quality of the content is not statistically significant. The findings show that how students feel when using AI tools together with their perception of the tool’s benefit is crucial despite the content itself. The results indicate that, to drive user satisfaction and long-term usage of this technology, developers may prioritize usability, perceived benefits, and emotional engagement rather than solely enhancing algorithmic reliability or refining instructional content.

artificial intelligence tools, student satisfaction, emotional well-being, perceived utility

1. Pataranutaporn, P., Danry, V., Leong, J., Punpongsanon, P., Novy, D., Maes, P. & Sra, M. (2021). AI-generated characters for supporting personalized learning and well-being. Nature Machine Intelligence. 3(12), 1013–1022. http://dx.doi.org/10.1038/s42256-021-00417-9 [Google Scholar] [Crossref]

2. Ma, G., Tian, S., Song, Y., Chen, Y., Shi, H. & Li, J. (2025). When Technology Meets Anxiety:The Moderating Role of AI Usage in the Relationship Between Social Anxiety, Learning Adaptability, and Behavioral Problems Among Chinese Primary School Students. Psychol Res Behav Manag. 18, 151-167. https://doi.org/10.2147/prbm.s502337 [Google Scholar] [Crossref]

3. Almufarreh, A. (2024). Determinants of Students’ Satisfaction with AI Tools in Education: A PLS-SEM-ANN Approach. Sustainability, 16(13), 5354. MDPI Journal. https://doi.org/10.3390/su16135354 [Google Scholar] [Crossref]

4. Alsaiari, O., Baghaei, N., Lahza, H., Lahza, H., Lodge, J., Boden, M. & Khosravi, H. (2024). Emotionally Enriched Feedback via Generative AI. arXiv. 2410, 15077. https://doi.org/10.48550/arXiv.2410.15077 [Google Scholar] [Crossref]

5. Almulla, M. A. (2024). Investigating Influencing Factors of Learning Satisfaction in AI ChatGPT for Research: University Students Perspective. 10(11), e32220. 10.1016/j.heliyon. 2024.e32220. [Google Scholar] [Crossref]

6. Jo, H. (2024). From concerns to benefits: a comprehensive study of ChatGPT usage in education. International Journal of Educational Technology in Higher Education. 21(35). https://doi.org/10.1186/s41239-024-00471-4 [Google Scholar] [Crossref]

7. Yu, C., Yan, J., & Cai, N. (2024). ChatGPT in higher education: factors influencing ChatGPT user satisfaction and continued use intention. Frontiers in Education, 9, 1354929. https://doi.org/10.3389/feduc.2024.1354929 [Google Scholar] [Crossref]

8. Yan, Y., Wu, B. & Zhang, X. (2025). Perceptions of AI in Higher Education: Insights from Students at a Top-Tier Chinese University. Education Science. 15(6), 735. https://doi.org/10.3390/educsci15060735 [Google Scholar] [Crossref]

9. Liu, Z. The Study of Users’ Satisfaction and Acceptance on Artificial Intelligence (AI). Journal of Education Humanities and Social Sciences, 27, 117-122. https://doi.org/10.54097/f4hgqw30 [Google Scholar] [Crossref]

10. Uzir, M.U., Al Halbusi, H., Lim, R., Jerin, I., Hamid, A.B., Ramayah, T., & Haque, A. Applied Artificial Intelligence and usersatisfaction: Smartwatch usage for healthcare in Bangladesh during COVID-19. (2021). Technology in Society. 67, 10178. https://doi.org/10.1016/j.techsoc.2021.101780 [Google Scholar] [Crossref]

11. Xing, J., & Jiang, Q. (2024). Factors influencing user experience in AI chat systems – a satisfaction study based on factor analysis and linear regression. Kybernetes. https://doi.org/10.1108/K-10-2023-2237 [Google Scholar] [Crossref]

12. Cetinkaya, N. E., & Krämer, N. (2025). Between transparency and trust: identifying key factors in AI system perception. Behaviour & Information Technology, 1–15. https://doi.org/10.1080/0144929X.2025.2533358 [Google Scholar] [Crossref]

13. Glassberg, I., Ilan, Y. B. & Zwilling, M. (2025). The key role of design and transparency in enhancing trust in AI-powered digital agents. Journal of Innovation & Knowledge. 10(5), 100770. https://doi.org/10.1016/j.jik.2025.100770. [Google Scholar] [Crossref]

14. Choudhury, A., & Shamszare, H. (2024). The impact of performance expectancy, workload, risk, and satisfaction on trust in ChatGPT: Cross-sectional survey analysis. JMIR Hum Factors. 11, e55399. https://doi.org/10.2196/55399 [Google Scholar] [Crossref]

15. Phua, J. T. K., Neo, H. F., & Teo, C. C. (2025). Evaluating the impact of artificial intelligence tools on enhancing student academic performance: Efficacy amidst security and privacy concerns. Big Data and Cognitive Computing, 9(5), 131. https://doi.org/10.3390/bdcc9050131 [Google Scholar] [Crossref]

16. Izhar, N. A., Teh, W. V. Y., & Adnan, A. (2025). Unlocking AI Potential: Effort Expectancy, Satisfaction, and Usage in Research. Journal of Information Technology Education: Innovations in Practice, 24, 005. https://doi.org/10.28945/5450 [Google Scholar] [Crossref]

17. Jain, G., Pareek, S. & Carlbring, P.. (2024). Revealing the source: How awareness alters perceptions of AI and human-generated mental health responses. Internet Interventions. 36, 100745. https://doi.org/10.1016/j.invent.2024.100745 [Google Scholar] [Crossref]

18. Elhosiny, E. (2024). Prioritizing Mental Well-Being: Placing Student and School Staff Mental Health at the Forefront of Education. [Google Scholar] [Crossref]

19. Maral, S., Naycı, N., Bilmez, H., Erdemir, E. I. & Satici, S. A. (2025). Problematic ChatGPT Use Scale: AI-Human Collaboration or Unraveling the Dark Side of ChatGPT. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-025-01509-y [Google Scholar] [Crossref]

20. Samiullah, M. Awan, W. I., Nasir, N. & Aziz, H. (2025). Harnessing Emotionally Adaptive AI to Boost [Google Scholar] [Crossref]

21. Academic Motivation and Combat Dropout Rates in Virtual Learning Environments. The Critical Review of Social Sciences Studies, 3(2), 1614-1628. https://doi.org/10.59075/hsaqxf40 [Google Scholar] [Crossref]

22. Hu, B., Mao, Y. & Kim, K. J.. (2023). How social anxiety leads to problematic use of conversational AI: The roles of loneliness, rumination, and mind perception. Computers in Human Behavior. 145, 107760. https://doi.org/10.1016/j.chb.2023.107760 [Google Scholar] [Crossref]

23. Tao, M. & She, S. (2022). How Does the Autonomy of AI Products Affect Consumers' Purchase Intention. American Journal of Applied Psychology, 11(6), 162-166. 10.11648/j.ajap.20221106.13 [Google Scholar] [Crossref]

24. Alessandro, G., Dimitri, O., Cristina, B. & Anna, M. (2024). The emotional impact of generative AI: negative emotions and perception of threat. Behaviour and Information Technology. 44. https://doi.org/10.1080/0144929X.2024.2333933 [Google Scholar] [Crossref]

25. Yang, H. & Rui, Y. (2025). Transforming EFL students' engagement: How AI-enhanced environments bridge emotional health challenges like depression and anxiety. Acta Psychologica. 257. https://doi.org/10.1016/j.actpsy.2025.105104 [Google Scholar] [Crossref]

26. Marvi, R., Foroudi, P. & AmirDadbar, N. (2025). Dynamics of user engagement: AI mastery goal and the paradox mindset in AI–employee collaboration. International Journal of Information Management. 83, 102908. https://doi.org/10.1016/j.ijinfomgt.2025.102908 [Google Scholar] [Crossref]

27. Choudhary, L. & Sybol, S. (2025). Impact of AI on Students Mental Health and Well-Being. In book: Human-Centered Learning Design in the AI Era. http://dx.doi.org/10.4018/979-8-3373-5786-7.ch008 [Google Scholar] [Crossref]

28. Bukhari, S. R., Batool, S., Shahab, I., Jalil, R., Arshad, A., Zahra, S. Q., Karim, S., Jamil, N. & Khan, N.. (2025). Role of AI Dependency in Psychological Distress and Academic Performance Among University Students. International Journal of Social Sciences Bulletin. 3(3). [Google Scholar] [Crossref]

29. Kim, B. J. & Kim, M. J. (2024). How artificial intelligence-induced job insecurity shapes knowledge dynamics: the mitigating role of artificial intelligence self-efficacy. Journal of Innovation & Knowledge. 9(4), 100590. https://doi.org/10.1016/j.jik.2024.100590 [Google Scholar] [Crossref]

30. Volpato, R., DeBruine, L. & Stumpf, S. (2025). Trusting emotional support from generative artificial intelligence: a conceptual review. Computers in Human Behavior: Artificial Humans. 5, 100195. https://doi.org/10.1016/j.chbah.2025.100195 [Google Scholar] [Crossref]

31. Dr, Kanchan & Shukla, Kanchan. (2023). Psychological Well-being Related to Life Satisfaction and Happiness in Among Relationships. 11. 289-294. [Google Scholar] [Crossref]

32. Chuang, Y. T., Chiang, H. L. & Lin, A. P. (2025). Insights from the Job Demands–Resources Model: AI's dual impact on employees’ work and life well-being. International Journal of Information Management. 83, 102887. https://doi.org/10.1016/j.ijinfomgt.2025.102887 [Google Scholar] [Crossref]

33. Huang, Q., Lv, C., Lu, L., & Tu, S. (2025). Evaluating the Quality of AI-Generated Digital Educational Resources for University Teaching and Learning. Systems, 13(3), 174. https://doi.org/10.3390/systems13030174 [Google Scholar] [Crossref]

34. Zhao, C. (2025). AI-assisted assessment in higher education: A systematic review. Journal of Educational Technology and Innovation, 6(4). [Google Scholar] [Crossref]

35. Ifraheem, S., Rasheed, M., & Siddiqui, A. (2024). Transforming Education Through Artificial Intelligence: Personalization, Engagement and Predictive Analytics. Journal of Asian Development Studies, 13(2). https://doi.org/10.62345/jads.2024.13.2.22 [Google Scholar] [Crossref]

36. Raheem, B. R., Anjum, F., & Ghafar, Z. N. (2023). Exploring the Profound Impact of Artificial Intelligence Applications (Quillbot, Grammarly and ChatGPT) on English Academic Writing: A Systematic Review [Google Scholar] [Crossref]

37. Sanosi, A. B., & Mohammed, M. O. M. (2024). The effectiveness of automated writing evaluation: A structural analysis approach. International Journal of Evaluation and Research in Education, 13(2), 1216–1226. https://doi.org/10.11591/ijere.v13i2.25372. [Google Scholar] [Crossref]

38. Kartika, S. (2024). Enhancing writing proficiency through AI-powered feedback: A quasi-experimental study using Google Gemini. LinguaEducare: Journal of English and Linguistic Studies, 1(2), 83–96. [Google Scholar] [Crossref]

39. Mosae, T., & Kaushal, R. (2025). Navigating the influence of AI tools on students’ learning experiences in higher education: A qualitative study. Journal of Informatics Education and Research, 5(2). https://doi.org/10.52783/jier.v5i2.2627 [Google Scholar] [Crossref]

40. Jabeen, F., Kazmi, S. M., & Asif, F. (2025). Use of Various Artificial Intelligence Tools for Academic Writing by University Students in Rawalpindi / Islamabad. Physical Education, Health and Social Sciences, 3(2). Wisdom Education & Research Hub. View the study [Google Scholar] [Crossref]

41. Xu, W. & Ouyang, F. The application of AI technologies in STEM education: a systematic review from 2011 to 2021. IJ STEM Ed 9, 59 (2022). https://doi.org/10.1186/s40594-022-00377-5 [Google Scholar] [Crossref]

42. Ota, R., Ray, S. S., & Alli, S. S. (2024). Exploring the Impact of Artificial Intelligence on Content Creation: A Comprehensive Study. International Journal of Research Publication and Reviews, 5(7), 597–604. https://doi.org/10.55248/gengpi.5.0724.1620 [Google Scholar] [Crossref]

43. Heung, Y. M. E., & Chiu, T. K. F. (2025). How ChatGPT impacts student engagement from a systematic review and meta-analysis study. Computers and Education: Artificial Intelligence, 8, 100361. Available via ScienceDirect. [Google Scholar] [Crossref]

44. Mat Yusoff, S., Mohamad Marzaini, A. F., Hao, L., Zainuddin, Z., & Basal, M. H. (2025). Understanding the role of AI in Malaysian higher education curricula: An analysis of student perceptions. Discover Computing, 28, Article 62. https://link.springer.com/article/10.1007/s10791-025-09567-5 [Google Scholar] [Crossref]

45. Mabborang, J. M., Pulintan, P. M. B., Javier, G. T., Tacang, D. T. S., & Tindowen, D. J. C. (2025). The use of AI tools in enhancing student learning. Psychology and Education: A Multidisciplinary Journal, 37(4), 313–317. https://doi.org/10.70838/PEMJ.370401 [Google Scholar] [Crossref]

46. Rodway, P., & Schepman, A. (2023). The impact of adopting AI educational technologies on projected course satisfaction in university students. Computers and Education: Artificial Intelligence, 5, 100150. https://doi.org/10.1016/j.caeai.2023.100150 [Google Scholar] [Crossref]

47. Mustaffa, S. N. N. B., & Bahador, Z. B. (2025). Analysis of the usage of artificial intelligence (AI) applications among university students in Malaysia. International Journal of Information Technology and Systems, 7(2), 45–52. [Google Scholar] [Crossref]

48. Louly, M. (2024). Application of AI tools in education: A conceptual framework. REST Publisher. https://restpublisher.com/wp-content/uploads/2024/07/1.-Application-of-AI-Tools-in-Education-A-Conceptual-Framework.pdf [Google Scholar] [Crossref]

49. Yusoff, S. M., Marzaini, A. F. M., Hao, L., Zainuddin, & Basal, M. H. (2025). Understanding the role of AI in Malaysian higher education curricula: An analysis of student perceptions. Discover Computing, 28, Article 62. [Google Scholar] [Crossref]

50. Randieri, Cristian. “Personalized Learning and AI: Revolutionizing Education.” Forbes Technology Council, July 22, 2024. https://www.forbes.com/councils/forbestechcouncil/2024/07/22/personalized-learning-and-ai-revolutionizing-education. [Google Scholar] [Crossref]

51. Jegede, A. (2024). AI and English language learning: A study on personalization and feedback. Universal Library of Languages and Literatures, 2(1), 22–39. https://ulopenaccess.com/ulpages/fulltextULLLI?PublishID=ULLLI20240102_002 [Google Scholar] [Crossref]

52. Hair, J.F., Black, W.C. & Babin, B.J. (2010). Multivariate data analysis: a global perspective. Pearson Prentice Hall. [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study