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Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy
Abstract
Generative artificial intelligence (GAI) advancements have ignited new expectations for artificial intelligence (AI)-enabled educational transformations. Based on the theory of planned behavior (TPB), this study combines structural equation modeling and interviews to analyze the influencing factors of Chinese university students’ GAI technology usage intention. Regarding AI literacy, students’ cognitive literacy in AI ethics scored the highest (M = 5.740), while AI awareness literacy scored the lowest (M = 4.578). Students’ attitudes toward GAI significantly and positively influenced their usage intention, with the combined TPB framework and AI literacy explaining 59.3% of the variance. AI literacy and subjective norms positively influenced students’ attitudes toward GAI technology and perceived behavioral control, and attitude mediated the impact of AI literacy and subjective norms on GAI usage intention. Further, the interviews provide new insights for university management and educational leadership regarding the construction of an educational ecosystem under the application of GAI technology.
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Cites
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
On the Evaluation of Structural Equation Models
Bagozzi, R. P., & Yi, Y. (1988) · Journal of the Academy of Marketing Science · doi:10.1007/bf02723327 · Cited by 26,163
The Discovery of Grounded Theory: Strategies for Qualitative Research
Glaser, B. G., & Strauss, A. L. (2017) · Aldine (Routledge reprint) · doi:10.4324/9780203793206 · Cited by 25,789
From Intentions to Actions: A Theory of Planned Behavior
Ajzen, I. (1985) · Action Control · doi:10.1007/978-3-642-69746-3_2 · Cited by 19,057
Hu, L.-T., & Bentler, P. M. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.424 · Cited by 12,298
Beyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium
Hayes, A. F. (2009) · Communication Monographs · doi:10.1080/03637750903310360 · Cited by 9,757
A comparison of methods to test mediation and other intervening variable effects
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.1.83 · Cited by 9,406 · cited 1×
“Therefore, this study employed the bootstrapping method with confidence intervals (CI) to conduct a mediation analysis; this is widely recognized as the most advantageous approach in current mediation testing (MacKinnon et al., 2002).”
An assessment of the use of partial least squares structural equation modeling in marketing research
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-011-0261-6 · Cited by 8,088
Predicting and Changing Behavior
Fishbein, M., & Ajzen, I. (2011) · doi:10.4324/9780203838020 · Cited by 2,807
Students' voices on generative ai: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · arXiv · doi:10.48550/arxiv.2305.00290 · Cited by 2,504 · cited 1×
“Both models exemplify the transformative potential of GAI technology in education by reshaping the logic of educational resource creation and dissemination, altering teachers’ roles, and rede-fining educational objectives (Abbas et al., 2024; Chan & Hu, 2023).”
Students’ voices on generative AI: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00411-8 · Cited by 2,301 · cited 1×
“Both models exemplify the transformative potential of GAI technology in education by reshaping the logic of educational resource creation and dissemination, altering teachers’ roles, and rede-fining educational objectives (Abbas et al., 2024; Chan & Hu, 2023).”
Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.04.015 · Cited by 1,057
Wang, B., Rau, P.-L. P., & Yuan, T. (2023) · Behaviour & Information Technology · doi:10.1080/0144929x.2022.2072768 · Cited by 1,036
Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00436-z · Cited by 852 · cited 1×
“…et al., 2023), programming (Jing et al., 2024; Yilmaz & Yilmaz, 2023b), online learning (Hwang et al., 2021), intelligent learning (Chen et al., 2021), medical education (Winkler-Schwartz et al., 2019; Zhang et al., 2024), higher education (Bond et al., 2024), and e-learning (Tang et al., 2023).”
Strzelecki, A. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2023.2209881 · Cited by 807 · cited 2×
“…confirmation theory (Tawafak et al., 2021), and the unified theory of acceptance and use of technology (Chu & Dai, 2021; Foroughi et al., 2023; Strzelecki, 2023) have been successfully applied to examine the factors that influence university students’ intention to use new technology in…”
Abbas, M., Jam, F. A., & Khan, T. I. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00444-7 · Cited by 531 · cited 1×
“Both models exemplify the transformative potential of GAI technology in education by reshaping the logic of educational resource creation and dissemination, altering teachers’ roles, and rede-fining educational objectives (Abbas et al., 2024; Chan & Hu, 2023).”
Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2023.100014 · Cited by 488
Ivanov, S., Soliman, M., Tuomi, A., Alkathiri, N. A., & Al-Alawi, A. N. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102521 · Cited by 397
Liang, J.-C., Hwang, G.-J., Chen, M.-R. A., & Darmawansah, D. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2021.1958348 · Cited by 378
Hadi Mogavi, R., Deng, C., Juho Kim, J., Zhou, P., D. Kwon, Y., Hosny Saleh Metwally, A., Tlili, A., Bassanelli, S., Bucchiarone, A., Gujar, S., et al. (2024) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2023.100027 · Cited by 369
Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2023.100005 · Cited by 344
Schiavo, G., Businaro, S., & Zancanaro, M. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102537 · Cited by 284
Hornberger, M., Bewersdorff, A., & Nerdel, C. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100165 · Cited by 276 · cited 2×
“Ng et al. (2023) propose an AI literacy questionnaire that is validated through confirmatory factor analysis, and Hornberger et al. (2023) develop an AI literacy test to measure university students’ AI knowledge.”
Laupichler, M. C., Aster, A., Haverkamp, N., & Raupach, T. (2023) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2023.100338 · Cited by 269
Using digital story writing as a pedagogy to develop AI literacy among primary students
Ng, D. T. K., Luo, W., Chan, H. M. Y., & Chu, S. K. W. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100054 · Cited by 266 · cited 1×
“Educators can achieve this through pedagogical activities (Ng et al., 2022) and curricula (Dai, 2024) that guide students in cultivating values and ethical perspectives, thereby enhancing their understanding and awareness of AI.”
Generative artificial intelligence acceptance scale: A validity and reliability study
Yilmaz, F. G. K., Yilmaz, R., & Ceylan, M. (2024) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2023.2288730 · Cited by 244
Chai, C. S., Wang, X., & Xu, C. (2020) · Mathematics · doi:10.3390/math8112089 · Cited by 226
Factors Influencing Learner Attitudes Towards ChatGPT-Assisted Language Learning in Higher Education
Cai, Q., Lin, Y., & Yu, Z. (2024) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2023.2261725 · Cited by 180
Ma, S., & Lei, L. (2024) · Asia Pacific Journal of Education · doi:10.1080/02188791.2024.2305155 · Cited by 148
AI in Medical Education: Global situation, effects and challenges
Zhang, W., Cai, M., Lee, H. J., Evans, R., Zhu, C., & Ming, C. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12009-8 · Cited by 148
Su, J., & Yang, W. (2024) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12867 · Cited by 116
Kooli, C., & Yusuf, N. (2024) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2338330 · Cited by 63
Cited by
Modeling AI-assisted writing: How self-regulated learning influences writing outcomes
Jin, F., Lin, C.-H., & Lai, C. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108538 · Cited by 93
Wang, F., Li, N., Cheung, A. C. K., & Wong, G. K. W. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00547-9 · Cited by 69
The AI literacy heptagon: A structured approach to AI literacy in higher education
Hackl, V., Müller, A. E., & Sailer, M. (2026) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2026.100540 · Cited by 41
Shang, X., Liu, Z., Gong, C., Hu, Z., Wu, Y., & Wang, C. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03658-2 · Cited by 37
Self-citationDing, N., Chen, M., & Hu, L. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105650 · Cited by 11
Metadata
- Authors
- Wang, C., Wang, H., Li, Y., Dai, J., Gu, X., & Yu, T.
- Venue
- International Journal of Human–Computer Interaction
- Publisher
- Informa UK Limited
- Published
- 2024-07-29
- Volume
- 41
- Issue
- 11
- Pages
- 6649-6671
- ISSN
- 1044-7318 (ISSN-L)
- 1532-7590
- OpenAlex
- W4401077562
- Cited by
- 353
- References
- 32
- FWCI
- 135.09
- Type
- journal article
- Language
- en
- Open access
- closed
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