The Role of Artificial Intelligence in the Behavior of Higher Education Service Consumers (A Conceptual Review)

The article examines the role of artificial intelligence in modern higher education. The transformative changes associated with the use of artificial intelligence in higher education are shown. The fact that artificial intelligence has significant potential to improve teaching, learning, and higher education management is emphasized. Based on a review of diverse literature, the following are considered to be the main determinants of the impact of artificial intelligence on the behavior of students in higher education institutions: expected effectiveness, expected effort, social influence, conditions conducive to use, hedonic motivation, trust, information quality, habit, personal innovativeness, perceived risk, attitude, behavioral intention, and usage behavior. The paper critically analyzes the impact of each determinant on the behavior of students in higher education institutions. The approaches developed as a result of the analysis are useful from both theoretical and practical points of view. It enriches the existing theoretical perspectives on the use of artificial intelligence in higher education. The paper will contribute to the design of research to collect empirical data for further studies, as well as help higher education practitioners develop student-centered approaches.

Artificial intelligence, higher education, consumer behavior, conceptual review

1. Abu Gharrah, A. S., & Aljaafreh, A. (2021). Why students use social networks for education: Extension of UTAUT2. Journal of Technology and Science Education, 11(1), 53–66. https://doi.org/10.3926/jotse.1081 [Google Scholar] [Crossref]

2. Adapa, A., Nah, F. F. H., Hall, R. H., Siau, K., & Smith, S. N. (2018). Factors influencing the adoption of smart wearable devices. International Journal of Human–Computer Interaction, 34(5), 399–409. https://doi.org/10.1080/10447318.2017.1357902 [Google Scholar] [Crossref]

3. Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215. https://doi.org/10.1287/isre.9.2.204 [Google Scholar] [Crossref]

4. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T [Google Scholar] [Crossref]

5. Al-Azawei, A., & Alowayr, A. (2020). Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries. Technology in Society, 62, Article 101325. https://doi.org/10.1016/j.techsoc.2020.101325 [Google Scholar] [Crossref]

6. Al-Emran, M., AlQudah, A. A., Abbasi, G. A., Al-Sharafi, M. A., & Iranmanesh, M. (2023). Determinants of using AI-based chatbots for knowledge sharing: Evidence from PLS-SEM and fuzzy sets (fsQCA). IEEE Transactions on Engineering Management, 71, 4985–4999. https://doi.org/10.1109/TEM.2023.3237789 [Google Scholar] [Crossref]

7. Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating e-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/j.chb.2019.08.004 [Google Scholar] [Crossref]

8. Alagarsamy, S., & Mehrolia, S. (2023). Exploring chatbot trust: Antecedents and behavioural outcomes. Heliyon, 9(5), e16074. https://doi.org/10.1016/j.heliyon.2023.e16074 [Google Scholar] [Crossref]

9. Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002 [Google Scholar] [Crossref]

10. Alam, M. S., Khan, T.-U.-Z., Dhar, S. S., & Munira, K. S. (2020). HR professionals’ intention to adopt and use of artificial intelligence in recruiting talents. Business Perspective Review, 2(2), 15–30. https://doi.org/10.38157/business-perspective-review.v2i2.122 [Google Scholar] [Crossref]

11. Ali, M. B., Tuhin, R., Abdul Alim, M., Rokonuzzaman, M., Rahman, S. M., & Nuruzzaman, M. (2022). Acceptance and use of ICT in tourism: The modified UTAUT model. Journal of Tourism Futures, 10(2), 334–349. https://doi.org/10.1108/JTF-06-2021-0137 [Google Scholar] [Crossref]

12. Almahri, F. A. J., Bell, D., & Merhi, M. (2020). Understanding student acceptance and use of chatbots in the United Kingdom universities: A structural equation modelling approach. In 2020 6th International Conference on Information Management (ICIM) (pp. 284–288). IEEE. https://doi.org/10.1109/ICIM49319.2020.244712 [Google Scholar] [Crossref]

13. Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access, 7, 174673–174686. https://doi.org/10.1109/ACCESS.2019.2957206 [Google Scholar] [Crossref]

14. Alotumi, M. (2022). Factors influencing graduate students’ behavioral intention to use Google Classroom: Case study–mixed methods research. Education and Information Technologies, 27(7), 10035–10063. https://doi.org/10.1007/s10639-022-11051-2 [Google Scholar] [Crossref]

15. Ayanwale, M. A., & Molefi, R. R. (2024). Exploring intention of undergraduate students to embrace chatbots: From the vantage point of Lesotho. International Journal of Educational Technology in Higher Education, 21, Article 20. https://doi.org/10.1186/s41239-024-00451-8 [Google Scholar] [Crossref]

16. Azizi, S. M., Roozbahani, N., & Khatony, A. (2020). Factors affecting the acceptance of blended learning in medical education: Application of UTAUT2 model. BMC Medical Education, 20, Article 367. https://doi.org/10.1186/s12909-020-02302-2 [Google Scholar] [Crossref]

17. Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: Measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20(4), 644–656. https://doi.org/10.1086/209376 [Google Scholar] [Crossref]

18. Bearden, W. O., Netemeyer, R. G., & Teel, J. E. (1989). Measurement of consumer susceptibility to interpersonal influence. Journal of Consumer Research, 15(4), 473–481. https://doi.org/10.1086/209186 [Google Scholar] [Crossref]

19. Bender, E. M., & Koller, A. (2020). Climbing towards NLU: On meaning, form, and understanding in the age of data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 5185–5198). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.463 [Google Scholar] [Crossref]

20. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921 [Google Scholar] [Crossref]

21. Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21, Article 4. https://doi.org/10.1186/s41239-023-00436-z [Google Scholar] [Crossref]

22. Boubker, O. (2024). From chatting to self-educating: Can AI tools boost student learning outcomes? Expert Systems with Applications, 238, Article 121820. https://doi.org/10.1016/j.eswa.2023.121820 [Google Scholar] [Crossref]

23. Bouteraa, M., Bin-Nashwan, S. A., Al-Daihani, M., Dirie, K. A., Benlahcene, A., Sadallah, M., Zaki, H. O., Lada, S., Ansar, R., Fook, L. M., & Chekima, B. (2024). Understanding the diffusion of AI-generative (ChatGPT) in higher education: Does students’ integrity matter? Computers in Human Behavior Reports, 14, Article 100402. https://doi.org/10.1016/j.chbr.2024.100402 [Google Scholar] [Crossref]

24. Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 29(3), 399–426. https://doi.org/10.2307/25148690 [Google Scholar] [Crossref]

25. Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, Article 43. https://doi.org/10.1186/s41239-023-00411-8 [Google Scholar] [Crossref]

26. Chao, C.-M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, Article 1652. https://doi.org/10.3389/fpsyg.2019.01652 [Google Scholar] [Crossref]

27. Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3443–3463. https://doi.org/10.1007/s10639-020-10159-7 [Google Scholar] [Crossref]

28. Chen, C.-C., & Huang, T.-C. (2012). Learning in a u-Museum: Developing a context-aware ubiquitous learning environment. Computers & Education, 59(3), 873–883. https://doi.org/10.1016/j.compedu.2012.04.003 [Google Scholar] [Crossref]

29. Chen, Y., & Zahedi, F. M. (2016). Individuals’ Internet security perceptions and behaviors: Polycontextual contrasts between the United States and China. MIS Quarterly, 40(1), 205–222. https://doi.org/10.25300/MISQ/2016/40.1.09 [Google Scholar] [Crossref]

30. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510 [Google Scholar] [Crossref]

31. Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160–175. https://doi.org/10.1016/j.compedu.2012.12.003 [Google Scholar] [Crossref]

32. Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, Article 100118. https://doi.org/10.1016/j.caeai.2022.100118 [Google Scholar] [Crossref]

33. Choudhury, A., & Shamszare, H. (2023). Investigating the impact of user trust on the adoption and use of ChatGPT: Survey analysis. Journal of Medical Internet Research, 25, e47184. https://doi.org/10.2196/47184 [Google Scholar] [Crossref]

34. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688 [Google Scholar] [Crossref]

35. Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148 [Google Scholar] [Crossref]

36. Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, Article 22. https://doi.org/10.1186/s41239-023-00392-8 [Google Scholar] [Crossref]

37. Dajani, D., & Abu Hegleh, A. S. (2019). Behavior intention of animation usage among university students. Heliyon, 5(10), e02536. https://doi.org/10.1016/j.heliyon.2019.e02536 [Google Scholar] [Crossref]

38. 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 [Google Scholar] [Crossref]

39. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982 [Google Scholar] [Crossref]

40. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748 [Google Scholar] [Crossref]

41. El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educational Technology Research and Development, 65, 743–763. https://doi.org/10.1007/s11423-016-9508-8 [Google Scholar] [Crossref]

42. Emon, M. M. H., Hassan, F., Nahid, M. H., & Rattanawiboonsom, V. (2023). Predicting adoption intention of artificial intelligence: A study on ChatGPT. AIUB Journal of Science and Engineering, 22(2), 189–196. https://doi.org/10.53799/ajse.v22i2.797 [Google Scholar] [Crossref]

43. Fagan, M. H. (2019). Factors influencing student acceptance of mobile learning in higher education. Computers in the Schools, 36(2), 105–121. https://doi.org/10.1080/07380569.2019.1603051 [Google Scholar] [Crossref]

44. Faqih, K. M. S., & Jaradat, M.-I. R. M. (2021). Integrating TTF and UTAUT2 theories to investigate the adoption of augmented reality technology in education: Perspective from a developing country. Technology in Society, 67, Article 101787. https://doi.org/10.1016/j.techsoc.2021.101787 [Google Scholar] [Crossref]

45. Farooq, M. S., Salam, M., Jaafar, N., Fayolle, A., Ayupp, K., Radovic-Markovic, M., & Sajid, A. (2017). Acceptance and use of lecture capture system in executive business studies: Extending UTAUT2. Interactive Technology and Smart Education, 14(4), 329–348. https://doi.org/10.1108/ITSE-06-2016-0015 [Google Scholar] [Crossref]

46. Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3 [Google Scholar] [Crossref]

47. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley. [Google Scholar] [Crossref]

48. Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2024). Determinants of intention to use ChatGPT for educational purposes: Findings from PLS-SEM and fsQCA. International Journal of Human–Computer Interaction, 40(17), 4501–4520. https://doi.org/10.1080/10447318.2023.2226495 [Google Scholar] [Crossref]

49. Gatzioufa, P., & Saprikis, V. (2022). A literature review on users’ behavioral intention toward chatbots’ adoption. Applied Computing and Informatics. Advance online publication. https://doi.org/10.1108/ACI-01-2022-0021 [Google Scholar] [Crossref]

50. Grassini, S. (2023a). Development and validation of the AI attitude scale (AIAS-4): A brief measure of general attitude toward artificial intelligence. Frontiers in Psychology, 14, Article 1191628. https://doi.org/10.3389/fpsyg.2023.1191628 [Google Scholar] [Crossref]

51. Grassini, S. (2023b). Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), Article 692. https://doi.org/10.3390/educsci13070692 [Google Scholar] [Crossref]

52. Haq, M. Z. U., Cao, G., & Abukhait, R. M. Y. (2025). Examining students’ attitudes and intentions towards using ChatGPT in higher education. British Journal of Educational Technology, 56(6), 2428–2452. https://doi.org/10.1111/bjet.13582 [Google Scholar] [Crossref]

53. Ho, K. K. (2014). The role of learners’ academic background on e-learning: An empirical study on the use of discussion forum. International Journal of Systems and Service-Oriented Engineering, 4(4), 51–64. https://doi.org/10.4018/IJSSOE.2014100104 [Google Scholar] [Crossref]

54. Hooda, A., Gupta, P., Jeyaraj, A., Giannakis, M., & Dwivedi, Y. K. (2022). The effects of trust on behavioral intention and use behavior within e-government contexts. International Journal of Information Management, 67, Article 102553. https://doi.org/10.1016/j.ijinfomgt.2022.102553 [Google Scholar] [Crossref]

55. Hu, S., Laxman, K., & Lee, K. (2020). Exploring factors affecting academics’ adoption of emerging mobile technologies: An extended UTAUT perspective. Education and Information Technologies, 25(5), 4615–4635. https://doi.org/10.1007/s10639-020-10171-x [Google Scholar] [Crossref]

56. Huang, D. H., & Chueh, H. E. (2022). Behavioral intention to continuously use learning apps: A comparative study from Taiwan universities. Technological Forecasting and Social Change, 177, Article 121531. https://doi.org/10.1016/j.techfore.2022.121531 [Google Scholar] [Crossref]

57. Ibrahim, A., & Nemt-allah, M. (2025). Cyberpsychology: Validity of the AI Chatbots Usage Scale for University Students [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-5953281/v1 [Google Scholar] [Crossref]

58. Jo, H. (2023). Understanding AI tool engagement: A study of ChatGPT usage and word-of-mouth among university students and office workers. Telematics and Informatics, 85, Article 102067. https://doi.org/10.1016/j.tele.2023.102067 [Google Scholar] [Crossref]

59. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415 [Google Scholar] [Crossref]

60. Kabra, G., Ramesh, A., Akhtar, P., & Dash, M. K. (2017). Understanding behavioural intention to use information technology: Insights from humanitarian practitioners. Telematics and Informatics, 34(7), 1250–1261. https://doi.org/10.1016/j.tele.2017.05.010 [Google Scholar] [Crossref]

61. Kang, M., Liew, B. Y. T., Lim, H., Jang, J., & Lee, S. (2015). Investigating the determinants of mobile learning acceptance in Korea using UTAUT2. In G. Chen, V. Kumar, Kinshuk, R. Huang, & S. C. Kong (Eds.), Emerging issues in smart learning (pp. 209–216). Springer. https://doi.org/10.1007/978-3-662-44188-6_29 [Google Scholar] [Crossref]

62. Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004 [Google Scholar] [Crossref]

63. Kasilingam, D. L. (2020). Understanding the attitude and intention to use smartphone chatbots for shopping. Technology in Society, 62, Article 101280. https://doi.org/10.1016/j.techsoc.2020.101280 [Google Scholar] [Crossref]

64. Kim, H.-W., Xu, Y., & Koh, J. (2004). A comparison of online trust building factors between potential customers and repeat customers. Journal of the Association for Information Systems, 5(10), 392–420. https://doi.org/10.17705/1jais.00056 [Google Scholar] [Crossref]

65. Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544–564. https://doi.org/10.1016/j.dss.2007.07.001 [Google Scholar] [Crossref]

66. Kumar, J. A., & Bervell, B. (2019). Google Classroom for mobile learning in higher education: Modelling the initial perceptions of students. Education and Information Technologies, 24, 1793–1817. https://doi.org/10.1007/s10639-018-09858-z [Google Scholar] [Crossref]

67. Kwak, Y., Seo, Y. H., & Ahn, J.-W. (2022). Nursing students’ intent to use AI-based healthcare technology: Path analysis using the unified theory of acceptance and use of technology. Nurse Education Today, 119, Article 105541. https://doi.org/10.1016/j.nedt.2022.105541 [Google Scholar] [Crossref]

68. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539 [Google Scholar] [Crossref]

69. Leow, L. P., Phua, L. K., & Teh, S. Y. (2021). Extending the social influence factor: Behavioural intention to increase the usage of information and communication technology-enhanced student-centered teaching methods. Educational Technology Research and Development, 69(3), 1853–1879. https://doi.org/10.1007/s11423-021-10017-4 [Google Scholar] [Crossref]

70. Liébana-Cabanillas, F., Marinković, V., Ramos de Luna, I., & Kalinić, Z. (2018). Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach. Technological Forecasting and Social Change, 129, 117–130. https://doi.org/10.1016/j.techfore.2017.12.015 [Google Scholar] [Crossref]

71. 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), Article 100790. https://doi.org/10.1016/j.ijme.2023.100790 [Google Scholar] [Crossref]

72. Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–737. https://doi.org/10.2307/25148817 [Google Scholar] [Crossref]

73. Ling, E. C., Tussyadiah, I., Liu, A., & Stienmetz, J. (2025). Perceived intelligence of artificially intelligent assistants for travel: Scale development and validation. Journal of Travel Research, 64(2), 299–321. https://doi.org/10.1177/00472875231217899 [Google Scholar] [Crossref]

74. Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), Article 410. https://doi.org/10.3390/educsci13040410 [Google Scholar] [Crossref]

75. Lu, J., Yao, J. E., & Yu, C.-S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245–268. https://doi.org/10.1016/j.jsis.2005.07.003 [Google Scholar] [Crossref]

76. Macedo, I. M. (2017). Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Computers in Human Behavior, 75, 935–948. https://doi.org/10.1016/j.chb.2017.06.013 [Google Scholar] [Crossref]

77. Magni, M., Taylor, M. S., & Venkatesh, V. (2010). “To play or not to play”: A cross-temporal investigation using hedonic and instrumental perspectives to explain user intentions to explore a technology. International Journal of Human-Computer Studies, 68(9), 572–588. https://doi.org/10.1016/j.ijhcs.2010.03.004 [Google Scholar] [Crossref]

78. Marafon, D. L., Basso, K., Espartel, L. B., de Barcellos, M. D., & Rech, E. (2018). Perceived risk and intention to use internet banking: The effects of self-confidence and risk acceptance. International Journal of Bank Marketing, 36(2), 277–289. https://doi.org/10.1108/IJBM-11-2016-0166 [Google Scholar] [Crossref]

79. Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/10.5465/amr.1995.9508080335 [Google Scholar] [Crossref]

80. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth Summer Research Project on artificial intelligence, August 31, 1955. AI Magazine, 27(4), 12–14. https://doi.org/10.1609/aimag.v27i4.1904 [Google Scholar] [Crossref]

81. Mehta, A., Morris, N. P., Swinnerton, B., & Homer, M. (2019). The influence of values on e-learning adoption. Computers & Education, 141, Article 103617. https://doi.org/10.1016/j.compedu.2019.103617 [Google Scholar] [Crossref]

82. Menon, D., & Shilpa, K. (2023). “Chatting with ChatGPT”: Analyzing the factors influencing users’ intention to use OpenAI’s ChatGPT using the UTAUT model. Heliyon, 9(11), e20962. https://doi.org/10.1016/j.heliyon.2023.e20962 [Google Scholar] [Crossref]

83. Mohammadi, H. (2015). Social and individual antecedents of m-learning adoption in Iran. Computers in Human Behavior, 49, 191–207. https://doi.org/10.1016/j.chb.2015.03.006 [Google Scholar] [Crossref]

84. Mohd Rahim, N. I., Iahad, N. A., Yusof, A. F., & Al-Sharafi, M. A. (2022). AI-based chatbots adoption model for higher-education institutions: A hybrid PLS-SEM-neural network modelling approach. Sustainability, 14(19), Article 12726. https://doi.org/10.3390/su141912726 [Google Scholar] [Crossref]

85. Mohammed, I. A., Owan, V. J., Thomas, G., & Ekpoto, D. F. (2025). Multi-group analysis of demographic differences in higher education students’ ChatGPT use behaviour within a modified UTAUT2 model. Discover Education. https://doi.org/10.1007/s44217-025-01018-z [Google Scholar] [Crossref]

86. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. https://doi.org/10.1287/isre.2.3.192 [Google Scholar] [Crossref]

87. Mostafa, R. B., & Kasamani, T. (2022). Antecedents and consequences of chatbot initial trust. European Journal of Marketing, 56(6), 1748–1771. https://doi.org/10.1108/EJM-02-2020-0084 [Google Scholar] [Crossref]

88. Mouta, A., Pinto-Llorente, A. M., & Torrecilla-Sánchez, E. M. (2024). Uncovering blind spots in education ethics: Insights from a systematic literature review on artificial intelligence in education. International Journal of Artificial Intelligence in Education, 34, 1166–1205. https://doi.org/10.1007/s40593-023-00384-9 [Google Scholar] [Crossref]

89. Mu, H. L., & Lee, Y. C. (2017). Examining the influencing factors of third-party mobile payment adoption: A comparative study of Alipay and WeChat Pay. The Journal of Information Systems, 26(4), 247–284. https://doi.org/10.5859/KAIS.2017.26.4.247 [Google Scholar] [Crossref]

90. Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2020). Acceptance of mobile phone by university students for their studies: An investigation applying UTAUT2 model. Education and Information Technologies, 25(5), 4139–4155. https://doi.org/10.1007/s10639-020-10157-9 [Google Scholar] [Crossref]

91. Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2021). Habit, hedonic motivation, performance expectancy and technological pedagogical knowledge affect teachers’ intention to use mobile internet. Computers and Education Open, 2, Article 100041. https://doi.org/10.1016/j.caeo.2021.100041 [Google Scholar] [Crossref]

92. Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56–73. https://doi.org/10.1016/j.compedu.2017.02.005 [Google Scholar] [Crossref]

93. Oluwajana, D., Idowu, A., Nat, M., Vanduhe, V., & Fadiya, S. (2019). The adoption of students’ hedonic motivation system model to gamified learning environment. Journal of Theoretical and Applied Electronic Commerce Research, 14(3), 156–167. https://doi.org/10.4067/S0718-18762019000300109 [Google Scholar] [Crossref]

94. Ortiz-Bonnin, S., García-Lázaro, I., & Prieto, G. (2025). Chat or cheat? Academic dishonesty, risk perceptions, and ChatGPT usage in higher education students. Social Psychology of Education. https://doi.org/10.1007/s11218-025-10080-2 [Google Scholar] [Crossref]

95. Osei, H. V., Kwateng, K. O., & Boateng, K. A. (2022). Integration of personality trait, motivation and UTAUT 2 to understand e-learning adoption in the era of COVID-19 pandemic. Education and Information Technologies, 27(8), 10705–10730. https://doi.org/10.1007/s10639-022-11047-y [Google Scholar] [Crossref]

96. Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893–7925. https://doi.org/10.1007/s10639-022-10925-9 [Google Scholar] [Crossref]

97. Rahman, M. K., Hossain, M. A., Ismail, N. A., Hossen, M. S., & Sultana, M. (2025). Determinants of students’ adoption of AI chatbots in higher education: The moderating role of tech readiness. Interactive Technology and Smart Education, 22(3), 448–480. https://doi.org/10.1108/ITSE-12-2024-0312 [Google Scholar] [Crossref]

98. Raman, A., & Don, Y. (2013). Preservice teachers’ acceptance of learning management software: An application of the UTAUT2 model. International Education Studies, 6(7), 157–164. https://doi.org/10.5539/ies.v6n7p157 [Google Scholar] [Crossref]

99. Raza, S. A., Qazi, Z., Qazi, W., & Ahmed, M. (2022). E-learning in higher education during COVID-19: Evidence from Blackboard learning system. Journal of Applied Research in Higher Education, 14(4), 1603–1622. https://doi.org/10.1108/JARHE-02-2021-0054 [Google Scholar] [Crossref]

100. Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after all: A cross-discipline view of trust. Academy of Management Review, 23(3), 393–404. https://doi.org/10.5465/amr.1998.926617 [Google Scholar] [Crossref]

101. Rudhumbu, N. (2022). Applying the UTAUT2 to predict the acceptance of blended learning by university students. Asian Association of Open Universities Journal, 17(1), 15–36. https://doi.org/10.1108/AAOUJ-08-2021-0084 [Google Scholar] [Crossref]

102. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68 [Google Scholar] [Crossref]

103. Salifu, I., Arthur, F., Arkorful, V., Nortey, S. A., & Osei-Yaw, R. S. (2024). Economics students’ behavioural intention and usage of ChatGPT in higher education: A hybrid structural equation modelling–artificial neural network approach. Cogent Social Sciences, 10(1), Article 2300177. https://doi.org/10.1080/23311886.2023.2300177 [Google Scholar] [Crossref]

104. Sallam, M., Elsayed, W., Al-Shorbagy, M., Barakat, M., El Khatib, S., Ghach, W., Alwan, N., Hallit, S., & Malaeb, D. (2024). ChatGPT usage and attitudes are driven by perceptions of usefulness, ease of use, risks, and psycho-social impact: A study among university students in the UAE. Frontiers in Education, 9, Article 1414758. https://doi.org/10.3389/feduc.2024.1414758 [Google Scholar] [Crossref]

105. Shahsavar, Y., & Choudhury, A. (2023). User intentions to use ChatGPT for self-diagnosis and health-related purposes: Cross-sectional survey study. JMIR Human Factors, 10, e47564. https://doi.org/10.2196/47564 [Google Scholar] [Crossref]

106. Shittu, T. A., & Taiwo, Y. H. (2023). Acceptance of WhatsApp social media platform for learning in Nigeria: A test of unified theory of acceptance and use of technology. Journal of Digital Educational Technology, 3(2), Article ep2309. https://doi.org/10.30935/jdet/13460 [Google Scholar] [Crossref]

107. Shroff, R. H., Deneen, C. C., & Ng, E. M. W. (2011). Analysis of the technology acceptance model in examining students’ behavioural intention to use an e-portfolio system. Australasian Journal of Educational Technology, 27(4), 600–618. https://doi.org/10.14742/ajet.940 [Google Scholar] [Crossref]

108. Sing, C. C., Teo, T., Huang, F., Chiu, T. K. F., & Wei, W. X. (2022). Secondary school students’ intentions to learn AI: Testing moderation effects of readiness, social good and optimism. Educational Technology Research and Development, 70(3), 765–782. https://doi.org/10.1007/s11423-022-10111-1 [Google Scholar] [Crossref]

109. Sitthipon, T., Siripipatthanakul, S., Phayaphrom, B., Siripipattanakul, S., & Limna, P. (2022). Determinants of customers’ intention to use healthcare chatbots and apps in Bangkok, Thailand. International Journal of Behavioral Analytics, 2(2), 1–15. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4045661 [Google Scholar] [Crossref]

110. Solihat, I., Hamundu, F. M., & Wahyu, M. (2023). Determinants of behavior intention to adopt peer-to-peer lending services among Indonesia MSMEs. International Journal of Business and Society, 24(1), 543–558. https://doi.org/10.33736/ijbs.5633.2023 [Google Scholar] [Crossref]

111. Strzelecki, A. (2024a). Students’ acceptance of ChatGPT in higher education: An extended unified theory of acceptance and use of technology. Innovative Higher Education, 49, 223–245. https://doi.org/10.1007/s10755-023-09686-1 [Google Scholar] [Crossref]

112. Strzelecki, A. (2024b). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 32(9), 5142–5155. https://doi.org/10.1080/10494820.2023.2209881 [Google Scholar] [Crossref]

113. Tamilmani, K., Rana, N. P., Prakasam, N., & Dwivedi, Y. K. (2019). The battle of brain vs. heart: A literature review and meta-analysis of “hedonic motivation” use in UTAUT2. International Journal of Information Management, 46, 222–235. https://doi.org/10.1016/j.ijinfomgt.2019.01.008 [Google Scholar] [Crossref]

114. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. https://doi.org/10.1287/isre.6.2.144 [Google Scholar] [Crossref]

115. Terblanche, N., & Kidd, M. (2022). Adoption factors and moderating effects of age and gender that influence the intention to use a non-directive reflective coaching chatbot. SAGE Open, 12(2), 21582440221096136. https://doi.org/10.1177/21582440221096136 [Google Scholar] [Crossref]

116. Terzis, V., & Economides, A. A. (2011). The acceptance and use of computer based assessment. Computers & Education, 56(4), 1032–1044. https://doi.org/10.1016/j.compedu.2010.11.017 [Google Scholar] [Crossref]

117. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10, Article 15. https://doi.org/10.1186/s40561-023-00237-x [Google Scholar] [Crossref]

118. Todua, N., & Kartsivadze, G. (2025). Students' perceptions of chatgpt integration in higher education. The New Economist, 20(4), 36-49. https://doi.org/10.36962/NEC20042025-36 [Google Scholar] [Crossref]

119. Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, EM-29(1), 28–45. https://doi.org/10.1109/TEM.1982.6447463 [Google Scholar] [Crossref]

120. Trivedi, J. (2019). Examining the customer experience of using banking chatbots and its impact on brand love: The moderating role of perceived risk. Journal of Internet Commerce, 18(1), 91–111. https://doi.org/10.1080/15332861.2019.1567188 [Google Scholar] [Crossref]

121. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433 [Google Scholar] [Crossref]

122. Twum, K. K., Ofori, D., Keney, G., & Korang-Yeboah, B. (2022). Using the UTAUT, personal innovativeness and perceived financial cost to examine students’ intention to use e-learning. Journal of Science and Technology Policy Management, 13(3), 713–737. https://doi.org/10.1108/JSTPM-12-2020-0168 [Google Scholar] [Crossref]

123. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (Vol. 30, pp. 5998–6008). Curran Associates, Inc. https://doi.org/10.48550/arXiv.1706.03762 [Google Scholar] [Crossref]

124. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926 [Google Scholar] [Crossref]

125. Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: U.S. vs. China. Journal of Global Information Technology Management, 13(1), 5–27. https://doi.org/10.1080/1097198X.2010.10856507 [Google Scholar] [Crossref]

126. 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 [Google Scholar] [Crossref]

127. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412 [Google Scholar] [Crossref]

128. Wach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzyński, P., Mazurek, G., Paliszkiewicz, J., & Ziemba, E. (2023). The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT. Entrepreneurial Business and Economics Review, 11(2), 7–30. https://doi.org/10.15678/EBER.2023.110201 [Google Scholar] [Crossref]

129. Wang, Y.-S., Wu, M.-C., & Wang, H.-Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92–118. https://doi.org/10.1111/j.1467-8535.2007.00809.x [Google Scholar] [Crossref]

130. Wang, S. W., Ngamsiriudom, W., & Hsieh, C. H. (2015). Trust disposition, trust antecedents, trust, and behavioral intention. The Service Industries Journal, 35(10), 555–572. https://doi.org/10.1080/02642069.2015.1047827 [Google Scholar] [Crossref]

131. Wang, J., Li, X., Wang, P., Liu, Q., Deng, Z., & Wang, J. (2021). Research trend of the unified theory of acceptance and use of technology theory: A bibliometric analysis. Sustainability, 14(1), Article 10. https://doi.org/10.3390/su14010010 [Google Scholar] [Crossref]

132. Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85–102. https://doi.org/10.1287/isre.1050.0042 [Google Scholar] [Crossref]

133. Wut, T. M., & Lee, D. (2022). Effects of e-learning system quality on users’ satisfaction and learning outcomes. Journal of Educational Technology Systems, 50(3), 329–346. https://doi.org/10.1177/00472395211047815 [Google Scholar] [Crossref]

134. Yin, J., Goh, T.-T., Yang, B., & Xiaobin, Y. (2021). Conversation technology with micro-learning: The impact of chatbot-based learning on students’ learning motivation and performance. Journal of Educational Computing Research, 59(1), 154–177. https://doi.org/10.1177/0735633120952067 [Google Scholar] [Crossref]

135. Yu, C.-W., Chao, C.-M., Chang, C.-F., Chen, R.-J., Chen, P.-C., & Liu, Y.-X. (2021). Exploring behavioral intention to use a mobile health education website: An extension of the UTAUT 2 model. SAGE Open, 11(4), 1–12. https://doi.org/10.1177/21582440211055721 [Google Scholar] [Crossref]

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

137. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0 [Google Scholar] [Crossref]

138. Zwain, A. A. A. (2019). Technological innovativeness and information quality as neoteric predictors of users’ acceptance of learning management system: An expansion of UTAUT2. Interactive Technology and Smart Education, 16(3), 239–254. https://doi.org/10.1108/ITSE-09-2018-0065 [Google Scholar] [Crossref]

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