Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy

References

• Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21(1), 10. https://doi.org/10.1186/s41239-024-00444-7
   Web of Science ®Google Scholar
• Adelana, O. P., Ayanwale, M. A., & Sanusi, I. T. (2024). Exploring pre-service biology teachers’ intention to teach genetics using an AI intelligent tutoring-based system. Cogent Education, 11(1), 2310976. https://doi.org/10.1080/2331186X.2024.2310976
   Web of Science ®Google Scholar
• Ajzen, I. (1985). From intentions to actions: A theory of planned behavior (pp. 11–39). Springer. https://doi.org/10.1007/978-3-642-69746-3_2
   Google Scholar
• Alajmi, Q., Al-Sharaf, M. A., & Abuali, A. (2020). Smart learning gateways for Omani HEIs towards educational technology: Benefts, challenges and solutions. International Journal of Information Technology and Language Studies, 4(1), 12–17.
   Google Scholar
• Arkorful, V. E., Hammond, A., Lugu, B. K., Basiru, I., Sunguh, K. K., & Charmaine‐Kwade, P. (2020). Investigating the intention to use technology among medical students: An application of an extended model of the theory of planned behavior. Journal of Public Affairs, 22(2), e2460. https://doi.org/10.1002/pa.2460
   Web of Science ®Google Scholar
• Arteaga Sánchez, R., Duarte Hueros, A., & García Ordaz, M. (2013). E‐learning and the University of Huelva: A study of WebCT and the technological acceptance model. Campus-Wide Information Systems, 30(2), 135–160. https://doi.org/10.1108/10650741311306318
   Google Scholar
• Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327
   Google Scholar
• Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17(1), 42. https://doi.org/10.1186/s41239-020-00218-x
   Web of Science ®Google Scholar
• Bazelais, P., Doleck, T., & Lemay, D. J. (2017). Investigating the predictive power of TAM: A case study of CEGEP students’ intentions to use online learning technologies. Education and Information Technologies, 23(1), 93–111. https://doi.org/10.1007/s10639-017-9587-0
   Web of Science ®Google Scholar
• 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(1), 4. https://doi.org/10.1186/s41239-023-00436-z
   Web of Science ®Google Scholar
• Brown, J. D. (2011). Likert items and scales of measurement. Statistics, 15(1), 10–14. https://teval.jalt.org/test/bro_34.htm
   Google Scholar
• Cai, Q., Lin, Y., & Yu, Z. (2023). Factors influencing learner attitudes towards ChatGPT-assisted language learning in higher education. International Journal of Human–Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2023.2261725
   Web of Science ®Google Scholar
• Calvani, A., Fini, A., & Ranieri, M. (2009). Assessing digital competence in secondary education—Issues, models and instruments. In Issues in information and media literacy: Education, practice and pedagogy (Vol. 2). Informing Science Press. https://flore.unifi.it/handle/2158/328911
   Google Scholar
• Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS-Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change-and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014. https://doi.org/10.1016/j.chbah.2023.100014
   Google Scholar
• Çelebi, C., Yılmaz, F., Demir, U., & Karakuş, F. (2023). Artificial intelligence literacy: An adaptation study. Instructional Technology and Lifelong Learning, 4(2), 291–306. https://doi.org/10.52911/itall.1401740
   Google Scholar
• Chai, C. S., Chiu, T. K., Wang, X., Jiang, F., & Lin, X. F. (2022). Modeling Chinese secondary school students’ behavioral intentions to learn artificial intelligence with the theory of planned behavior and self-determination theory. Sustainability, 15(1), 605. https://doi.org/10.3390/su15010605
   Web of Science ®Google Scholar
• Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K., & Qin, J. (2021). Perceptions of and behavioral intentions towards learning artificial intelligence in primary school students. Educational Technology & Society, 24(3), 89–101. https://www.jstor.org/stable/27032858
   Web of Science ®Google Scholar
• Chai, C. S., Wang, X., & Xu, C. (2020). An extended theory of planned behavior for the modelling of Chinese secondary school students’ intention to learn artificial intelligence. Mathematics, 8(11), 2089. https://doi.org/10.3390/math8112089
   Web of Science ®Google Scholar
• Cham, T. H., Cheng, B. L., Low, M. P., & Cheok, J. B. C. (2020). Brand image as the competitive edge for hospitals in medical tourism. European Business Review, 33(1), 31–59. https://doi.org/10.1108/EBR-10-2019-0269
   Web of Science ®Google Scholar
• 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(1), 43. https://doi.org/10.1186/s41239-023-00411-8
   Web of Science ®Google Scholar
• Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128–143. https://doi.org/10.1016/j.compedu.2017.04.010
   Web of Science ®Google Scholar
• Chen, X. J., Hu, Z. B., & Wang, C. L. (2024). Empowering education development through AIGC: A systematic literature review. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/s10639-024-12549-7
   PubMed Web of Science ®Google Scholar
• Chen, X. J., Zhu, K. K., Jing, Y. H., & Wang, C. L. (2023). The application landscape and research status of artificial intelligence in language learning: A visual analysis [Paper presentation]. The 15th International Conference on Education Technology and Computers (ICETC 2023), Barcelona, Spain (pp. 461–468). ACM. https://doi.org/10.1145/3629296.3629370
   Google Scholar
• Chen, X., Zou, D., Xie, H., & Wang, F. L. (2021). Past, present, and future of smart learning: A topic-based bibliometric analysis. International Journal of Educational Technology in Higher Education, 18(1), 2. https://doi.org/10.1186/s41239-020-00239-6
   Web of Science ®Google Scholar
• Cheng, E. W. L., Chu, S. K. W., & Ma, C. S. M. (2015). Tertiary students’ intention to e-collaborate for group projects: Exploring the missing link from an extended theory of planned behaviour model. British Journal of Educational Technology, 47(5), 958–969. https://doi.org/10.1111/bjet.12379
   Web of Science ®Google Scholar
• Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054–1064. https://doi.org/10.1016/j.compedu.2012.04.015
   Web of Science ®Google Scholar
• Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), vii–xvi. https://www.jstor.org/stable/249674
   Web of Science ®Google Scholar
• Chu, J., & Dai, Y.-Y. (2021). Extending the UTAUT model to study the acceptance behavior of MOOCs by university students and the moderating roles of free time management and leisure-study conflict. International Journal of Technology and Human Interaction, 17(4), 35–57. https://doi.org/10.4018/IJTHI.2021100103
   Web of Science ®Google Scholar
• Cong-Lem, N., Soyoof, A., & Tsering, D. (2024). A systematic review of the limitations and associated opportunities of ChatGPT. International Journal of Human–Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2024.2344142
   Web of Science ®Google Scholar
• Dai, J., Zhang, X., & Wang, C. L. (2024). A meta-analysis of learners’ continuance intention toward online education platforms. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/s10639-024-12654-7
   PubMed Web of Science ®Google Scholar
• Dai, Y. (2024). Dual-contrast pedagogy for AI literacy in upper elementary schools. Learning and Instruction, 91, 101899. https://doi.org/10.1016/j.learninstruc.2024.101899
   Web of Science ®Google Scholar
• Dawes, J. (2002). Five point vs. eleven point scales: Does it make a difference to data characteristics? Australasian Journal of Market Research, 10(1), 1–17.
   Google Scholar
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Contemporary Sociology, 6(2), 244. https://doi.org/10.2307/2065853
   Web of Science ®Google Scholar
• Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. Routledge. https://doi.org/10.4324/9780203838020
   Google Scholar
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
   Web of Science ®Google Scholar
• Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2023). Determinants of intention to use ChatGPT for educational purposes: Findings from PLS-SEM and fsQCA. International Journal of Human–Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2023.2226495
   Web of Science ®Google Scholar
• Glaser, B. G., & Strauss, A. L. (2017). Discovery of grounded theory. Routledge. https://doi.org/10.4324/9780203793206
   Google Scholar
• Habibi, A., Riady, Y., Samed Al-Adwan, A., & Awni Albelbisi, N. (2023). Beliefs and knowledge for pre-service teachers’ technology integration during teaching practice: An extended theory of planned behavior. Computers in the Schools, 40(2), 107–132. https://doi.org/10.1080/07380569.2022.2124752
   Web of Science ®Google Scholar
• 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., Nacke, L. E., & Hui, P. (2024). ChatGPT in education: A blessing or a curse? A qualitative study exploring early adopters’ utilization and perceptions. Computers in Human Behavior: Artificial Humans, 2(1), 100027. https://doi.org/10.1016/j.chbah.2023.100027
   Google Scholar
• Hagger, M. S., Cheung, M. W. L., Ajzen, I., & Hamilton, K. (2022). Perceived behavioral control moderating effects in the theory of planned behavior: A meta-analysis. Health Psychology: official Journal of the Division of Health Psychology, American Psychological Association, 41(2), 155–167. https://doi.org/10.1037/hea0001153
   PubMed Web of Science ®Google Scholar
• Hair, J. F., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128
   Google Scholar
• Hair, J. F., Black, W. C., Babin, B., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Prentice Hall.
   Google Scholar
• Hair, J. F., Babin, B. J., & Krey, N. (2017). Covariance-based structural equation modeling in the Journal of Advertising: Review and recommendations. Journal of Advertising, 46(1), 163–177. https://doi.org/10.1080/00913367.2017.1281777
   Web of Science ®Google Scholar
• Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
   Google Scholar
• Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433. https://doi.org/10.1007/s11747-011-0261-6
   Web of Science ®Google Scholar
• Hamad, F., Shehata, A., & Al Hosni, N. (2024). Predictors of blended learning adoption in higher education institutions in Oman: Theory of planned behavior. International Journal of Educational Technology in Higher Education, 21(1), 13. https://doi.org/10.1186/s41239-024-00443-8
   Web of Science ®Google Scholar
• Han, H., Hsu, L.-T., & Sheu, C. (2010). Application of the theory of planned behavior to green hotel choice: Testing the effect of environmental friendly activities. Tourism Management, 31(3), 325–334. https://doi.org/10.1016/j.tourman.2009.03.013
   Web of Science ®Google Scholar
• Hayduk, L. S. (1987). Structural equation modeling with LISREL: Essentials and advances. Johns Hopkins University Press.
   Google Scholar
• Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76(4), 408–420. https://doi.org/10.1080/03637750903310360
   Web of Science ®Google Scholar
• Ho, S. M., Ocasio-Velázquez, M., & Booth, C. (2017). Trust or consequences? Causal effects of perceived risk and subjective norms on cloud technology adoption. Computers & Security, 70, 581–595. https://doi.org/10.1016/j.cose.2017.08.004
   Web of Science ®Google Scholar
• Holland, D. D., & Piper, R. T. (2016). A technology integration education (tie) model for millennial preservice teachers: Exploring the canonical correlation relationships among attitudes, subjective norms, perceived behavioral controls, motivation, and technological, pedagogical, and content knowledge (TPACK) competencies. Journal of Research on Technology in Education, 48(3), 212–226. https://doi.org/10.1080/15391523.2016.1172448
   Web of Science ®Google Scholar
• Hornberger, M., Bewersdorff, A., & Nerdel, C. (2023). What do university students know about artificial intelligence? Development and validation of an AI literacy test. Computers and Education: Artificial Intelligence, 5, 100165. https://doi.org/10.1016/j.caeai.2023.100165
   Google Scholar
• Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. https://doi.org/10.1037/1082-989X.3.4.424
   Web of Science ®Google Scholar
• Hwang, G. J., Tu, Y. F., & Lin, C. J. (2021). Advancements and hot research topics of artificial intelligence in mobile learning: A review of journal publications from 1995 to 2019. International Journal of Mobile Learning and Organisation, 15(4), 427–447. https://doi.org/10.1504/IJMLO.2021.118444
   Google Scholar
• Hyde, M. K., & Knowles, S. R. (2013). What predicts Australian university students’ intentions to volunteer their time for community service? Australian Journal of Psychology, 65(3), 135–145. https://doi.org/10.1111/ajpy.12014
   Web of Science ®Google Scholar
• Ivanov, S., Soliman, M., Tuomi, A., Alkathiri, N. A., & Al-Alawi, A. N. (2024). Drivers of generative AI adoption in higher education through the lens of the theory of planned behaviour. Technology in Society, 77, 102521. https://doi.org/10.1016/j.techsoc.2024.102521
   Web of Science ®Google Scholar
• Jing, Y. H., Wang, H. M., Chen, X. J., & Wang, C. L. (2024). What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study. Humanities & Social Sciences Communications, 11, 319. https://doi.org/10.1057/s41599-024-02751-w
   Google Scholar
• Jong, M. S. Y., Chan, T., Hue, M. T., & Tam, V. W. (2018). Gamifying and mobilising social enquiry-based learning in authentic outdoor environments. Educational Technology & Society, 21(4), 277–292. https://www.jstor.org/stable/26511554
   Web of Science ®Google Scholar
• Kang, E. Y. N., Chen, D. R., & Chen, Y. Y. (2023). Associations between literacy and attitudes toward artificial intelligence–assisted medical consultations: The mediating role of perceived distrust and efficiency of artificial intelligence. Computers in Human Behavior, 139, 107529. https://doi.org/10.1016/j.chb.2022.107529
   Google Scholar
• Khan, T. A., Alam, M., Rizvi, S. A., Shahid, Z., & Mazliham, M. S. (2024). Introducing AI applications in engineering education (PBL): An implementation of power generation at minimum wind velocity and turbine faults classification using AI. Computer Applications in Engineering Education, 32(1), e22691. https://doi.org/10.1002/cae.22691
   Web of Science ®Google Scholar
• Kong, S. C., Cheung, W. M. Y., & Zhang, G. (2021). Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds. Computers and Education: Artificial Intelligence, 2, 100026. https://doi.org/10.1016/j.caeai.2021.100026
   Google Scholar
• Kooli, C., & Yusuf, N. (2024). Transforming educational assessment: Insights into the use of ChatGPT and large language models in grading. International Journal of Human–Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2024.2338330
   Web of Science ®Google Scholar
• Lam, T., Cho, V., & Qu, H. (2007). A study of hotel employee behavioral intentions towards adoption of information technology. International Journal of Hospitality Management, 26(1), 49–65. https://doi.org/10.1016/j.ijhm.2005.09.002
   Google Scholar
• Laupichler, M. C., Aster, A., Haverkamp, N., & Raupach, T. (2023). Development of the “scale for the assessment of non-experts’ AI literacy”-An exploratory factor analysis. Computers in Human Behavior Reports, 12, 100338. https://doi.org/10.1016/j.chbr.2023.100338
   Google Scholar
• Liang, J.-C., Hwang, G.-J., Chen, M.-R. A., & Darmawansah, D. (2023). Roles and research foci of artificial intelligence in language education: An integrated bibliographic analysis and systematic review approach. Interactive Learning Environments, 31(7), 4270–4296. https://doi.org/10.1080/10494820.2021.1958348
   Web of Science ®Google Scholar
• Liesa-Orús, M., Latorre-Cosculluela, C., Sierra-Sánchez, V., & Vázquez-Toledo, S. (2023). Links between ease of use, perceived usefulness and attitudes towards technology in older people in university: A structural equation modelling approach. Education and Information Technologies, 28(3), 2419–2436. https://doi.org/10.1007/s10639-022-11292-1
   PubMed Web of Science ®Google Scholar
• Lin, W.-S. (2012). Perceived fit and satisfaction on web learning performance: IS continuance intention and task-technology fit perspectives. International Journal of Human-Computer Studies, 70(7), 498–507. https://doi.org/10.1016/j.ijhcs.2012.01.006
   Web of Science ®Google Scholar
• Liu, T., Yang, B., Geng, Y., & Du, S. (2021). Research on face recognition and privacy in China—based on social cognition and cultural psychology. Frontiers in Psychology, 12, 809736. https://doi.org/10.3389/fpsyg.2021.809736
   PubMed Web of Science ®Google Scholar
• Lune, H., & Berg, B. L. (2021). Qualitative research methods for the social sciences. Pearson. https://www.pearson.com/en-us/subject-catalog/p/qualitative-research-methods-for-the-social-sciences/P200000002971/9780137561469
   Google Scholar
• Lung-Guang, N. (2019). Decision-making determinants of students participating in MOOCs: Merging the theory of planned behavior and self-regulated learning model. Computers & Education, 134, 50–62. https://doi.org/10.1016/j.compedu.2019.02.004
   Web of Science ®Google Scholar
• Ma, S., & Lei, L. (2024). The factors influencing teacher education students’ willingness to adopt artificial intelligence technology for information-based teaching. Asia Pacific Journal of Education, 44(1), 94–111. https://doi.org/10.1080/02188791.2024.2305155
   Web of Science ®Google Scholar
• Mac Callum, K., Jeffrey, L., Kinshuk. (2014). Comparing the role of ICT literacy and anxiety in the adoption of mobile learning. Computers in Human Behavior, 39, 8–19. https://doi.org/10.1016/j.chb.2014.05.024
   Web of Science ®Google Scholar
• MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7(1), 83–104. https://doi.org/10.1037/1082-989x.7.1.83
   PubMed Web of Science ®Google Scholar
• Madden, T. J., Ellen, P. S., & Ajzen, I. (1992). A comparison of the theory of planned behavior and the theory of reasoned action. Personality and Social Psychology Bulletin, 18(1), 3–9. https://doi.org/10.1177/0146167292181001
   Web of Science ®Google Scholar
• Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common method variance in IS research: A comparison of alternative approaches and a reanalysis of past research. Management Science, 52(12), 1865–1883. https://doi.org/10.1287/mnsc.1060.0597
   Web of Science ®Google Scholar
• Mei, B., Brown, G. T. L., & Teo, T. (2017). Toward an understanding of preservice English as a foreign language teachers’ acceptance of computer-assisted language learning 2.0 in the People’s Republic of China. Journal of Educational Computing Research, 56(1), 74–104. https://doi.org/10.1177/0735633117700144
   Web of Science ®Google Scholar
• Meng, B., & Choi, K. (2019). Tourists’ intention to use location-based services (LBS). International Journal of Contemporary Hospitality Management, 31(8), 3097–3115. https://doi.org/10.1108/IJCHM-09-2018-0734
   Web of Science ®Google Scholar
• Mohammadyari, S., & Singh, H. (2015). Understanding the effect of e-learning on individual performance: The role of digital literacy. Computers & Education, 82, 11–25. https://doi.org/10.1016/j.compedu.2014.10.025
   Web of Science ®Google Scholar
• Moore, D. R. (2010). Technology literacy: The extension of cognition. International Journal of Technology and Design Education, 21(2), 185–193. https://doi.org/10.1007/s10798-010-9113-9
   Web of Science ®Google Scholar
• Nachmias, C., Nachmias, D., & Dewaard, J. (2015). Research methods in the social sciences. Worth Publishers, A Macmillan Education Company.
   Google Scholar
• Ng, D. T. K., Luo, W., Chan, H. M. Y., & Chu, S. K. W. (2022). Using digital story writing as a pedagogy to develop AI literacy among primary students. Computers and Education: Artificial Intelligence, 3, 100054. https://doi.org/10.1016/j.caeai.2022.100054
   Google Scholar
• Ng, D. T. K., Wu, W., Leung, J. K. L., & Chu, S. K. W. (2023). Artificial intelligence (AI) literacy questionnaire with confirmatory factor analysis [Paper presentation]. 2023 IEEE International Conference on Advanced Learning Technologies (ICALT) (pp. 233–235). IEEE. https://doi.org/10.1109/ICALT58122.2023.00074
   Google Scholar
• Obenza, B., Salvahan, A., Rios, A. N., Solo, A., Alburo, R. A., & Gabila, R. J. (2024). University students’ perception and use of ChatGPT: Generative artificial intelligence (AI) in higher education. International Journal of Human Computing Studies, 5(12), 5–18. Available at SSRN: https://ssrn.com/abstract=4724968
   Google Scholar
• Onesi-Ozigagun, O., Ololade, Y. J., Eyo-Udo, N. L., & Ogundipe, D. O. (2024). Revolutionizing education through AI: A comprehensive review of enhancing learning experiences. International Journal of Applied Research in Social Sciences, 6(4), 589–607. https://doi.org/10.51594/ijarss.v6i4.1011
   Google Scholar
• Otache, I. (2019). Entrepreneurship education and undergraduate students’ self- and paid-employment intentions. Education + Training, 61(1), 46–64. https://doi.org/10.1108/ET-10-2017-0148
   Web of Science ®Google Scholar
• Pan, Y., Huang, Y., Kim, H., & Zheng, X. (2023). Factors influencing students’ intention to adopt online interactive behaviors: Merging the theory of planned behavior with cognitive and motivational factors. The Asia-Pacific Education Researcher, 32(1), 27–36. https://doi.org/10.1007/s40299-021-00629-y
   Web of Science ®Google Scholar
• Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of Method Bias in Social Science Research and Recommendations on How to Control It. Annual Review of Psychology, 63(1), 539–569. https://doi.org/10.1146/annurev-psych-120710-100452
   PubMed Web of Science ®Google Scholar
• Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. The Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
   PubMed Web of Science ®Google Scholar
• Revythi, A., & Tselios, N. (2019). Extension of technology acceptance model by using system usability scale to assess behavioral intention to use e-learning. Education and Information Technologies, 24(4), 2341–2355. https://doi.org/10.1007/s10639-019-09869-4
   Web of Science ®Google Scholar
• Schiavo, G., Businaro, S., & Zancanaro, M. (2024). Comprehension, apprehension, and acceptance: Understanding the influence of literacy and anxiety on acceptance of artificial intelligence. Technology in Society, 77, 102537. https://doi.org/10.1016/j.techsoc.2024.102537
   Web of Science ®Google Scholar
• Shiau, W.-L., Sarstedt, M., & Hair, J. F. (2019). Internet research using partial least squares structural equation modeling (PLS-SEM). Internet. Internet Research, 29(3), 398–406. https://doi.org/10.1108/IntR-10-2018-0447
   Web of Science ®Google Scholar
• Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Sage Publications Ltd.
   Google Scholar
• Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2023.2209881
   Google Scholar
• Su, J., & Yang, W. (2024). AI literacy curriculum and its relation to children’s perceptions of robots and attitudes towards engineering and science: An intervention study in early childhood education. Journal of Computer Assisted Learning, 40(1), 241–253. https://doi.org/10.1111/jcal.12867
   Web of Science ®Google Scholar
• Sungur-Gül, K., & Ateş, H. (2021). Understanding pre-service teachers’ mobile learning readiness using theory of planned behavior. Educational Technology & Society, 24(2), 44–57. https://www.jstor.org/stable/27004930
   Web of Science ®Google Scholar
• Tang, K.-Y., Chang, C.-Y., & Hwang, G.-J. (2023). Trends in artificial intelligence-supported e-learning: A systematic review and co-citation network analysis (1998–2019). Interactive Learning Environments, 31(4), 2134–2152. https://doi.org/10.1080/10494820.2021.1875001
   Web of Science ®Google Scholar
• Tawafak, R., Malik, S., Mathew, R., Ashfaque, M., Jabbar, J., AlNuaimi, M., et al. (2021). A combined model for continuous intention to use e-learning system. International Association of Online Engineering. https://www.learntechlib.org/p/219005/
   Google Scholar
• Teo, T. (2012). Examining the intention to use technology among pre-service teachers: An integration of the technology acceptance model and theory of planned behavior. Interactive Learning Environments, 20(1), 3–18. https://doi.org/10.1080/10494821003714632
   Web of Science ®Google Scholar
• UNESCO (2022). Enabling AI in adult education the role of artificial intelligence in adult education. https://www.uil.unesco.org/sites/default/files/medias/fichiers/2022/06/Mario_Franco_FINAL_CONFITEA_AI%40EDU-final.pptx_.pdf
   Google Scholar
• UNESCO (2023). ChatGPT and artificial intelligence in higher education. https://www.iesalc.unesco.org/wp-content/uploads/2023/04/ChatGPT-and-Artificial-Intelligence-in-higher-education-Quick-Start-guide_EN_FINAL.pdf
   Google Scholar
• Ursavaş, Ö. F., Yalçın, Y., & Bakır, E. (2019). The effect of subjective norms on preservice and in‐service teachers’ behavioural intentions to use technology: A multigroup multimodel study. British Journal of Educational Technology, 50(5), 2501–2519. https://doi.org/10.1111/bjet.12834
   Web of Science ®Google Scholar
• Valtonen, T., Kukkonen, J., Kontkanen, S., Sormunen, K., Dillon, P., & Sointu, E. (2015). The impact of authentic learning experiences with ICT on pre-service teachers’ intentions to use ICT for teaching and learning. Computers & Education, 81, 49–58. https://doi.org/10.1016/j.compedu.2014.09.008
   Google Scholar
• Van Lange, P. A. M., Kruglanski, A. W., & Higgins, E. T. (2011). Handbook of theories of social psychology. SAGE publications.
   Google Scholar
• Vicente, G., & Partidário, M. R. (2006). SEA–Enhancing communication for better environmental decisions. Environmental Impact Assessment Review, 26(8), 696–706. https://doi.org/10.1016/j.eiar.2006.06.005
   Web of Science ®Google Scholar
• Vujinović, A., Luburić, N., Slivka, J., & Kovačević, A. (2024). Using ChatGPT to annotate a dataset: A case study in intelligent tutoring systems. Machine Learning with Applications, 16, 100557. https://doi.org/10.1016/j.mlwa.2024.100557
   Google Scholar
• Wang, B., Rau, P.-L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929X.2022.2072768
   Web of Science ®Google Scholar
• Wang, C. L., Chen, X. J., Yu, T., Liu, Y. D., & Jing, Y. H. (2024). Education reform and change driven by digital technology: A bibliometric study from a global perspective. Humanities & Social Sciences Communications, 11, 256. https://doi.org/10.1057/s41599-024-02717-y
   Google Scholar
• Wang, C. L., Dai, J., Zhu, K. K., Yu, T., & Gu, X. Q. (2023). Understanding the continuance intention of college students toward new e-learning spaces based on an integrated model of the TAM and TTF. International Journal of Human–Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2023.2291609
   Web of Science ®Google Scholar
• Wang, C., Dai, J., Chen, Y., Zhang, X., & Xu, L. (2022). A learning analytics model based on expression recognition and affective computing: Review of techniques and survey of acceptance [Paper presentation]. Proceedings of the 2022 International Conference on Educational Innovation and Multimedia Technology (EIMT 2022) (pp. 169–178). https://doi.org/10.2991/978-94-6463-012-1_19
   Google Scholar
• Wilson, M., Scalise, K., & Gochyyev, P. (2015). Rethinking ICT literacy: From computer skills to social network settings. Thinking Skills and Creativity, 18, 65–80. https://doi.org/10.1016/j.tsc.2015.05.001
   Web of Science ®Google Scholar
• Winkler-Schwartz, A., Bissonnette, V., Mirchi, N., Ponnudurai, N., Yilmaz, R., Ledwos, N., Siyar, S., Azarnoush, H., Karlik, B., & Del Maestro, R. F. (2019). Artificial intelligence in medical education: Best practices using machine learning to assess surgical expertise in virtual reality simulation. Journal of Surgical Education, 76(6), 1681–1690. https://doi.org/10.1016/j.jsurg.2019.05.015
   PubMed Web of Science ®Google Scholar
• WIPO (2019). Artificial intelligence. https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf.
   Google Scholar
• Xia, Y. Q., Deng, Y. L., Tao, X. Y., Zhang, S. N., & Wang, C. L. (2024). Digital art exhibitions and psychological well-being in Chinese Generation Z: An analysis based on the S-O-R framework. Humanities & Social Sciences Communications, 11, 266. https://doi.org/10.1057/s41599-024-02718-x
   Google Scholar
• Yan, J., Huang, T., & Xiao, Y. (2023). Assessing the impact of entrepreneurial education activity on entrepreneurial intention and behavior: Role of behavioral entrepreneurial mindset. Environmental Science and Pollution Research International, 30(10), 26292–26307. https://doi.org/10.1007/s11356-022-23878-w
   PubMed Web of Science ®Google Scholar
• Yang, M., Shao, Z., Liu, Q., & Liu, C. (2017). Understanding the quality factors that influence the continuance intention of students toward participation in MOOCs. Educational Technology Research and Development, 65(5), 1195–1214. https://doi.org/10.1007/s11423-017-9513-6
   Web of Science ®Google Scholar
• Yilmaz, F. G. K., Yilmaz, R., & Ceylan, M. (2023). Generative artificial intelligence acceptance scale: A validity and reliability study. International Journal of Human–Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2023.2288730
   Web of Science ®Google Scholar
• Yilmaz, R., & Yilmaz, F. G. K. (2023a). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, 100147. https://doi.org/10.1016/j.caeai.2023.100147
   Google Scholar
• Yilmaz, R., & Yilmaz, F. G. K. (2023b). Augmented intelligence in programming learning: Examining student views on the use of ChatGPT for programming learning. Computers in Human Behavior: Artificial Humans, 1(2), 100005. https://doi.org/10.1016/j.chbah.2023.100005
   Google Scholar
• Yu, T., Dai, J., & Wang, C. (2023). Adoption of blended learning: Chinese university students’ perspectives. Humanities & Social Sciences Communications, 10, 390. https://doi.org/10.1057/s41599-023-01904-7
   Google Scholar
• Yu, X., Xu, S., & Ashton, M. (2023). Antecedents and outcomes of artificial intelligence adoption and application in the workplace: The socio-technical system theory perspective. Information Technology & People, 36(1), 454–474. https://doi.org/10.1108/ITP-04-2021-0254
   Web of Science ®Google Scholar
• Yusoff, R., & Mohd Janor, R. (2014). Generation of an interval metric scale to measure attitude. SAGE Open, 4(1), 215824401351676. https://doi.org/10.1177/2158244013516768
   Google Scholar
• Zhang, W., Cai, M., Lee, H. J., Evans, R., Zhu, C., & Ming, C. (2024). AI in medical education: Global situation, effects and challenges. Education and Information Technologies, 29(4), 4611–4633. https://doi.org/10.1007/s10639-023-12009-8
   Web of Science ®Google Scholar
• Zhang, Y., Yang, X., & Tong, W. (2024). University students’ attitudes toward ChatGPT profiles and their relation to ChatGPT intentions. International Journal of Human–Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2024.2331882
   Web of Science ®Google Scholar
• Zhuang, X., Hou, X., Feng, Z., Lin, Z., & Li, J. (. (2021). Subjective norms, attitudes, and intentions of AR technology use in tourism experience: The moderating effect of millennials. Leisure Studies, 40(3), 392–406. https://doi.org/10.1080/02614367.2020.1843692
   Web of Science ®Google Scholar