Research Article
Exploring graduate students’ acceptance and use of generative AI: An application of the UTAUT2 model
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1 Department of English Language Teaching, Hacettepe University, Ankara, TURKEY2 Corporate Communication Office, Karadeniz Technical University, Trabzon, TURKEY* Corresponding Author
Contemporary Educational Technology, 18(3), July 2026, ep666, https://doi.org/10.30935/cedtech/18745
Published: 12 June 2026
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ABSTRACT
Generative artificial intelligence (GenAI) has recently gained significant attention within educational contexts, particularly among university students. Regardless of its significance, there are still a limited number of empirical studies on graduate students’ intention to use this technology in higher education settings. Accordingly, this study investigated the acceptance of GenAI tools by master’s and doctoral students for academic purposes. The research adapted the unified theory of acceptance and use of technology 2 model and surveyed 145 graduate students from various universities through convenience sampling. Data collected through online surveys were analyzed using the partial least squares approach to structural equation modelling. Key findings revealed that factors, including habit (HB), performance expectancy, and hedonic motivation, had a significant effect on students’ behavioral intention (BI) to use GenAI tools. Additionally, the study indicated that the most important predictors for actual GenAI use were HB and BI. Notably, demographic variables, age, gender, and the level of study, showed no significant moderating influence on the relationships among the constructs. This study provides further insight into our understanding of how GenAI tools are accepted by graduate students for academic purposes and contribute to the literature on the factors affecting their intention to use these tools.
CITATION (APA)
Yıldırım-Samuk, Ş., & Altay, İ. F. (2026). Exploring graduate students’ acceptance and use of generative AI: An application of the UTAUT2 model. Contemporary Educational Technology, 18(3), ep666. https://doi.org/10.30935/cedtech/18745
REFERENCES
- Alneyadi, S., & Wardat, Y. (2023). ChatGPT: Revolutionizing student achievement in the electronic magnetism unit for eleventh-grade students in Emirates schools. Contemporary Educational Technology, 15(4), Article ep448. https://doi.org/10.30935/cedtech/13417
- Alotumi, M. (2022). Factors influencing graduate students’ behavioral intention to use Google Classroom: Case study-mixed methods research. Education and Information Technologies, 27, 10035-10063. https://doi.org/10.1007/s10639-022-11051-2
- Ameri, A., Khajouei, R., Ameri, A., & Jahani, Y. (2020). Acceptance of a mobile-based educational application (LabSafety) by pharmacy students: An application of the UTAUT2 model. Education and Information Technologies, 25, 419-435. https://doi.org/10.1007/s10639-019-09965-5
- Cambra-Fierro, J. J., Blasco, M. F., López-Pérez, M. E. E., & Trifu, A. (2025). ChatGPT adoption and its influence on faculty well-being: An empirical research in higher education. Education and Information Technologies, 30, 1517-1538. https://doi.org/10.1007/s10639-024-12871-0
- Camilleri, M. A. (2024). Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework. Technological Forecasting and Social Change, 201, Article 123247. https://doi.org/10.1016/j.techfore.2024.123247
- Chen, A., Jia, J., Li, Y., & Fu, L. (2025). Investigating the effect of role-play activity with GenAI agent on EFL students’ speaking performance. Journal of Educational Computing Research, 63(1), 99-125. https://doi.org/10.1177/07356331241299058
- Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64-73. https://doi.org/10.1177/002224377901600110
- Dao, X. Q., Le, N. B., Vo, T. D., Phan, X. D., Ngo, B. B., Nguyen, V. T., Nguyen, T. M. T., & Nguyen, H. P. (2023). VNHSGE: VietNamese high school graduation examination dataset for large language models. arXiv. https://doi.org/10.48550/arXiv.2305.12199
- Dixit, R. S., Choudhary, S. L., & Govil, N. (2025). Analyzing the impact of artificial intelligence on the online purchase decision-making process through the lens of the UTAUT 2 model. Discover Computing, 28(1), Article 88. https://doi.org/10.1007/s10791-025-09575-5
- Duong, C. D., Bui, D. T., Pham, H. T., Vu, A. T., & Nguyen, V. H. (2024). How effort expectancy and performance expectancy interact to trigger higher education students’ uses of ChatGPT for learning. Interactive Technology and Smart Education, 21(3), 356-380. https://doi.org/10.1108/ITSE-05-2023-0096
- Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., & Eirug, A., ... Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, Article 101994. https://doi.org/10.1016/J.IJINFOMGT.2019.08.002
- Edumadze, J. K. E., Barfi, K. A., Arkorful, V., & Baffour Jr, N. O. (2023). Undergraduate student’s perception of using video conferencing tools under lockdown amidst COVID-19 pandemic in Ghana. Interactive Learning Environments, 31(9), 5799-5810. https://doi.org/10.1080/10494820.2021.2018618
- Farrell, A. M. (2010). Insufficient discriminant validity: A comment on Bove, Pervan, Beatty, and Shiu (2009). Journal of Business Research, 63(3), 324-327. https://doi.org/10.1016/j.jbusres.2009.05.003
- Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382-388. https://doi.org/10.2307/3150980
- 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
- Gansser, O. A., & Reich, C. S. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 65, Article 101535. https://doi.org/10.1016/j.techsoc.2021.101535
- García de Blanes Sebastián, M., Sarmiento Guede, J. R., Azuara Grande, A., & Filipe, A. F. (2025). UTAUT-2 predictors and satisfaction: Implications for mobile-learning adoption among university students. Education and Information Technologies, 30, 3201-3237. https://doi.org/10.1007/s10639-024-12927-1
- Geriş, A., & Kulaksız, T. (2025). Predicting teachers’ intentions to use virtual reality in education: A study based on the UTAUT-2 framework. Research in Learning Technology, 33, Article 3429. https://doi.org/10.25304/rlt.v33.3429
- Ghimire, S. N., Bhattarai, U., & Baral, R. K. (2024). Implications of ChatGPT for higher education institutions: Exploring Nepali university students’ perspectives. Higher Education Research & Development, 43(8), 1769-1783. https://doi.org/10.1080/07294360.2024.2366323
- Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernandez-Leo, D., Jarvela, S., Mavrikis, M., & Rienties, B. (2025). The promise and challenges of generative AI in education. Behaviour & Information Technology, 44(11), 2518-2544. https://doi.org/10.1080/0144929X.2024.2394886
- Grassini, S., Aasen, M. L., & Møgelvang, A. (2024). Understanding university students’ acceptance of ChatGPT: Insights from the UTAUT2 model. Applied Artificial Intelligence, 38(1), Article 2371168. https://doi.org/10.1080/08839514.2024.2371168
- Habibi, A., Muhaimin, M., Danibao, B. K., Wibowo, Y. G., Wahyuni, S., & Octavia, A. (2023). ChatGPT in higher education learning: Acceptance and use. Computers and Education: Artificial Intelligence, 5, Article 100190. https://doi.org/10.1016/j.caeai.2023.100190
- Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage. https://www.cengage.uk/c/multivariate-data-analysis-8e-hair-babin-anderson-black/9781473756540/?searchIsbn=9781473756540
- Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage Publications. https://uk.sagepub.com/en-gb/eur/a-primer-on-partial-least-squares-structural-equation-modeling-pls-sem/book270548
- Hair, J., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), Article 100027. https://doi.org/10.1016/j.rmal.2022.100027
- 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, 2428-2452. https://doi.org/10.1111/bjet.13582
- Hays, L., Jurkowski, O., & Sims, S. K. (2024). ChatGPT in K-12 education. TechTrends, 68(2), 281-294. https://doi.org/10.1007/s11528-023-00924-z
- Henke, J. (2024). Navigating the AI era: University communication strategies and perspectives on generative AI tools. Journal of Science Communication, 23(3), Article A05. https://doi.org/10.22323/2.23030205
- Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
- Hoi, V. N. (2020). Understanding higher education learners’ acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Computers & Education, 146, Article 103761. https://doi.org/10.1016/j.compedu.2019.103761
- Hu, L., Wang, H., & Xin, Y. (2025). Factors influencing Chinese pre-service teachers’ adoption of generative AI in teaching: An empirical study based on UTAUT2 and PLS-SEM. Education and Information Technologies, 30, 12609-12631. https://doi.org/10.1007/s10639-025-13353-7
- Huang, W., Ong, W. C., Wong, M. K. F., Ng, E. Y. K., Koh, T., Chandramouli, C., Ng, C. T., Hummel, Y., Huang, F., Lam, C. S. P., & Tromp, J. (2024). Applying the UTAUT2 framework to patients’ attitudes toward healthcare task shifting with artificial intelligence. BMC Health Services Research, 24(1), Article 455. https://doi.org/10.1186/s12913-024-10861-z
- Huh, S. (2023). Are ChatGPT’s knowledge and interpretation ability comparable to those of medical students in Korea for taking a parasitology examination? A descriptive study. Journal of Educational Evaluation for Health Professions, 20, 1-5. https://doi.org/10.3352/jeehp.2023.20.1
- Koltovskaia, S., Rahmati, P., & Saeli, H. (2024). Graduate students’ use of ChatGPT for academic text revision: Behavioral, cognitive, and affective engagement. Journal of Second Language Writing, 65, Article 101130. https://doi.org/10.1016/j.jslw.2024.101130
- Korinek, A. (2023). Generative AI for economic research: Use cases and implications for economists. Journal of Economic Literature, 61(4), 1281-1317. https://doi.org/10.1257/jel.20231736
- 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
- Liu, Y., Park, J., & McMinn, S. (2024). Using generative artificial intelligence/ChatGPT for academic communication: Students’ perspectives. International Journal of Applied Linguistics, 34, 1437-1461. https://doi.org/10.1111/ijal.12574
- Lou, Y. (2023). Exploring the application of ChatGPT to English teaching in a Malaysia primary school. Journal of Advanced Research in Education, 2(4), 47-54. https://doi.org/10.56397/JARE.2023.07.08
- Lövdén, M., Fratiglioni, L., Glymour, M. M., Lindenberger, U., & Tucker-Drob, E. M. (2020). Education and cognitive functioning across the life span. Psychological Science in the Public Interest, 21(1), 6-41. https://doi.org/10.1177/1529100620920576
- Menon, D., & Shilpa, K. (2023). “Chatting with ChatGPT”: Analyzing the factors influencing users’ intention to use the Open AI’s ChatGPT using the UTAUT model. Heliyon, 9(11), Article e20962. https://doi.org/10.1016/j.heliyon.2023.e20962
- Mijwil, M., & Aljanabi, M. (2023). Towards artificial intelligence-based cybersecurity: The practices and ChatGPT generated ways to combat cybercrime. Iraqi Journal for Computer Science and Mathematics, 4(1), 65-70. https://doi.org/10.52866/ijcsm.2023.01.01.0019
- Munyoka, W., & Maharaj, M. (2017). The effect of UTAUT2 moderator factors on citizens’ intention to adopt e-government: The case of two SADC countries. Problems and Perspectives in Management, 15(1), 115-123. https://doi.org/10.21511/ppm.15(1).2017.12
- Namatovu, A., & Kyambade, M. (2025). Leveraging AI in academia: University students’ adoption of ChatGPT for writing coursework (take home) assignments through the lens of UTAUT2. Cogent Education, 12(1), Article 2485522. https://doi.org/10.1080/2331186X.2025.2485522
- 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, 4139-4155. https://doi.org/10.1007/s10639-020-10157-9
- Ooi, K. B., Tan, G. W. H., Al-Emran, M., Al-Sharafi, M. A., Capatina, A., Chakraborty, A., Dwivedi, Y. K., Huang, T.-L., Kar, A. K., Lee, V.-H., Loh, X.-M., Micu, A., Mikalef, P., Mogaji, E., Pandey, N., Raman, R., Rana, N. P., Sarker, P., Sharma, A., ... Wong, L. W. (2025). The potential of generative artificial intelligence across disciplines: Perspectives and future directions. Journal of Computer Information Systems, 65(1), 76-107. https://doi.org/10.1080/08874417.2023.2261010
- 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, 10705 10730. https://doi.org/10.1007/s10639-022-11047-y
- Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, 40(2), 269-275. https://doi.org/10.1016/j.ijresmar.2023.03.001
- Permana, I. P. H., Aristana, I. D. G., Prayana, I. K. W. D., Wijaya, B. K., & Pratiwi, N. W. A. D. (2024). Analyzing user acceptance of Balindo Paradiso University information system using UTAUT 2 model. TECHNOVATE: Journal of Information Technology and Strategic Innovation Management, 1(2), 96-109. https://garuda.kemdiktisaintek.go.id/documents/detail/4159580
- Permana, I. S., Hidayat, T., & Mahardiko, R. (2021). Users’ intentions and behaviors toward portable scanner application–Do education and employment background moderates the effect of UTAUT main theory? Journal of Physics: Conference Series, 1803, Article 012034. https://doi.org/10.1088/1742-6596/1803/1/012034
- Putra, R. R., & Gilda, V. (2023). Analysis of education level as a moderating variable on the interest in using accounting software for MSMEs in the UTAUT2 model. Account and Financial Management Journal, 8(2), 3073-3080. https://doi.org/10.47191/afmj/v8i2.02
- R Core Team. (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
- Rahim, N. I. M., 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
- 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
- Ringle, C. M., Wende, S., & Becker, J.-M. (2022). SmartPLS 4. SmartPLS. https://www.smartpls.com/
- Romero-Rodríguez, J. M., Ramírez-Montoya, M. S., Buenestado-Fernández, M., & Lara-Lara, F. (2023). Use of ChatGPT at university as a tool for complex thinking: Students’ perceived usefulness. Journal of New Approaches in Educational Research, 12(2), 323-339. https://doi.org/10.7821/naer.2023.7.1458
- Salifu, I., Arthur, F., Acquah, B. Y. S., Opoku, E., Nortey, S. A., & Boateng, E. (2025). Exploring graduate students’ use of generative artificial intelligence in Ghana: Insights from an extended UTAUT2 model, PLS-SEM, IPMA and fsQCA. Discover Education, 4(1), Article 305. https://doi.org/10.1007/s44217-025-00603-6
- Sengar, S. S., Hasan, A. B., Kumar, S., & Carroll, F. (2025). Generative artificial intelligence: A systematic review and applications. Multimedia Tools and Applications, 84(21), 23661-23700. https://doi.org/10.1007/s11042-024-20016-1
- Sergeeva, O. V., Zheltukhina, M. R., Shoustikova, T., Tukhvatullina, L. R., Dobrokhotov, D. A., & Kondrashev, S. V. (2025). Understanding higher education students’ adoption of generative AI technologies: An empirical investigation using UTAUT2. Contemporary Educational Technology, 17(2), Article ep571. https://doi.org/10.30935/cedtech/16039
- Sobaih, A. E. E., Elshaer, I. A., & Hasanein, A. M. (2024). Examining students’ acceptance and use of ChatGPT in Saudi Arabian higher education. European Journal of Investigation in Health, Psychology and Education, 14(3), 709-721. https://doi.org/10.3390/ejihpe14030047
- Strzelecki, A. (2024a). Students’ acceptance of ChatGPT in higher education: An extended unified theory of acceptance and use of technology. Innovative Higher Education, 49(2), 223-245. https://doi.org/10.1007/s10755-023-09686-1
- 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
- Surya Bahadur G. C., S. B., Bhandari, P., Gurung, S. K., Srivastava, E., Ojha, D., & Dhungana, B. R. (2024). Examining the role of social influence, learning value and habit on students’ intention to use ChatGPT: The moderating effect of information accuracy in the UTAUT2 model. Cogent Education, 11(1), Article 2403287. https://doi.org/10.1080/2331186X.2024.2403287
- Teng, Z., Cai, Y., Gao, Y., Zhang, X., & Li, X. (2022). Factors affecting learners’ adoption of an educational metaverse platform: An empirical study based on an extended UTAUT model. Mobile Information Systems, 2022, Article 5479215. https://doi.org/10.1155/2022/5479215
- Twum, K. K., Ofori, D., Keney, G., & Korang-Yeboah, B. (2022). Using the UTAUT, personal innovativeness and perceived financial cost to examine student’s 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
- 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
- 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
- Wang, T., & Xue, B. (2024). The role of GenAI in EFL: Impact on learning motivation and outcome. Research Square. https://doi.org/10.21203/rs.3.rs-5144171/v1
- Woo, D. J., Guo, K., & Susanto, H. (2025). EFL secondary students’ use of ChatGPT for writing task completion pathways. The Journal of Educational Research, 118(6), 596-609. https://doi.org/10.1080/00220671.2025.2510382
- Xu, S., Chen, P., & Zhang, G. (2024). Exploring Chinese university educators’ acceptance and intention to use AI tools: An application of the UTAUT2 model. Sage Open, 14(4). https://doi.org/10.1177/21582440241290013
- Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90-112. https://doi.org/10.1111/bjet.13370
- Yan, L., Greiff, S., Teuber, Z., & Gašević, D. (2024). Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour, 8, 1839–1850. https://doi.org/10.1038/s41562-024-02004-5
- Yang, F., Ren, L., & Gu, C. (2022). A study of college students’ intention to use metaverse technology for basketball learning based on UTAUT2. Heliyon, 8(9), Article e10562. https://doi.org/10.1016/j.heliyon.2022.e10562
- 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). https://doi.org/10.1177/21582440211055721
- Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21(1), Article 21. https://doi.org/10.1186/s41239-024-00453-6
- Zacharis, G., & Nikolopoulou, K. (2022). Factors predicting university students’ behavioral intention to use eLearning platforms in the post-pandemic normal: An UTAUT2 approach with ‘learning value’. Education and Information Technologies, 27, 12065-12082. https://doi.org/10.1007/s10639-022-11116-2
- Zheng, Y., Wang, Y., Liu, K. S. X., & Jiang, M. Y. C. (2024). Examining the moderating effect of motivation on technology acceptance of generative AI for English as a foreign language learning. Education and Information Technologies, 29, 23547-23575. https://doi.org/10.1007/s10639-024-12763-3
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