Abstract
This research examines the influence of integrating generative artificial intelligence (GAI) in education, focusing on its acceptance and utilization among elementary education students. Grounded in the Task-Technology Fit (TTF) Theory and an expanded iteration of the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study analyzes key constructs—Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions—on students’ behavioral intentions and usage behaviors concerning GAI. The UTAUT model, which integrates elements from multiple theories and is widely applied in educational contexts to understand technology adoption behaviors, provides a robust theoretical framework. Additionally, TTF theory, emphasizing the alignment of technology with specific instructional tasks, enhances our understanding of GAI acceptance. This study also investigates the moderating effects of TTF and gender within this framework. Data analysis, conducted through PLS-SEM, is based on responses from 279 elementary education students in China who completed an 8-week course incorporating GAI. Results indicate that Performance Expectancy, Social Influence, and Effort Expectancy significantly influence Behavioral Intention, while Facilitating Conditions have the strongest impact on actual Use Behavior, surpassing their influence on Behavioral Intention. Furthermore, Task-Technology Fit moderates both Performance Expectancy and Effort Expectancy in students’ consideration of GAI use. However, gender does not demonstrate a moderating effect in the overall model. These findings deepen our understanding of elementary school students’ acceptance of GAI technology and provide practical guidance for developers, educational policymakers, teachers, and researchers to effectively integrate GAI into elementary education while maintaining teaching quality.
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References
Abramski, K., Citraro, S., Lombardi, L., Rossetti, G., & Stella, M. (2023). Cognitive network science reveals bias in GPT-3, GPT-3.5 turbo, and GPT-4 mirroring math anxiety in high-school students. Big Data and Cognitive Computing, 7(3), 124. https://doi.org/10.3390/bdcc7030124.
Almusawi, H. A., & Durugbo, C. M. (2024). Linking task-technology fit, innovativeness, and teacher readiness using structural equation modelling. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12440-x. Advance online publication.
An, X., Chai, C., Li, Y., Zhou, Y., & Yang, B. (2023). Modeling students’ perceptions of artificial intelligence assisted language learning, Computer Assisted Language Learning Advance online publication. https://doi.org/10.1080/09588221.2023.2246519.
Bourgonjon, J., Valcke, M., Soetaert, R., & Schellens, T. (2010). Students’ perceptions about the use of video games in the classroom. Computers & Education, 54, 1145–1156. https://doi.org/10.1016/j.compedu.2009.10.022.
Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K. F., & 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.
Chen, J. (2011). The effects of education compatibility and technological expectancy on e-learning acceptance. Computers & Education, 57(2), 1501–1511. https://doi.org/10.1016/j.compedu.2011.02.009.
Chen, Y., Li, R., & Liu, X. (2023). Problematic smartphone usage among Chineseadolescents: Role of social/non-social loneliness, use motivations, and grade difference. Current Psychology: Research & Reviews, 42(14), 11529–11538. https://doi.org/10.1007/s12144-021-02458-0.
Chen, X., Hu, Z., & Wang, C. (2024). Empowering education development through AIGC: A systematic literature review. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12549-7. Advance online publication.
Cislaghi, B., & Heise, L. (2020). Gender norms and social norms: Differences, similarities and why they matter in prevention science. Sociology of Health & Illness, 42(2), 407–422. https://doi.org/10.1111/1467-9566.13008.
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y.
Gefen, D., & Straub, D. (2000). The relative importance of perceived ease of use in IS adoption: A study of E-Commerce adoption. Journal of the Association for Information Systems. Advance online publication. https://doi.org/10.17705/1jais.00008.
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. Mis Quarterly, 19(2), 213–236. https://doi.org/10.2307/249689.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46, 1–12. https://doi.org/10.1016/j.lrp.2013.01.001.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203.
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 Edition. Sage.
Hamhuis, E., Glas, C., & Meelissen, M. (2020). Tablet assessment in primary education: Are there performance differences between TIMSS’ paper-and‐pencil test and tablet test among Dutch grade‐four. Students? British Journal of Educational Technology, 51(6), 2340–2358. https://doi.org/10.1111/bjet.12914.
Helsper, E. J., & Eynon, R. (2010). Digital natives: Where is the evidence? British Educational Research Journal, 36(3), 503–520. http://www.jstor.org/stable/27823621.
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.
Hsu, L. (2021). EFL learners’ self-determination and acceptance of LMOOCs: The UTAUT model. Computer Assisted Language Learning. https://doi.org/10.1080/09588221.2021.1976210. Advance online publication.
Jauhiainen, J. S., & Guerra, A. G. (2023). GAI and ChatGPT in School Children’s education: Evidence from a school lesson. Sustainability, 15(18), 14025. https://doi.org/10.3390/su151814025.
Kasneci, E., Seßler, K., Küchemann, S. (2023). ChatGPT for Good? On opportunities and challenges of large Language models for Education. Learning and individual differences. 103. 102274. https://doi.org/10.1016/j.lindif.2023.102274.
Lin, W. S., & Wang, C. H. (2012). Antecedences to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit. Computers & Education, 58(1), 88–99. https://doi.org/10.1016/j.compedu.2011.07.008.
Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410.
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.
Lozano, A., & Blanco Fontao, C. (2023). Is the education system prepared for the irruption of artificial intelligence? A study on the perceptions of students of primary education degree from a dual perspective: Current pupils and future teachers. Education Sciences, 13(7), 733. https://doi.org/10.3390/educsci13070733.
Ma, N., Du, L., & Lu, Y. (2022). A model of factors influencing in-service teachers’ social network prestige in online peer assessment. Australasian Journal of Educational Technology, 38(5), 100–118. https://doi.org/10.14742/ajet.7622.
Maheshwari, G. (2023). Factors influencing students’ intention to adopt and use ChatGPT in higher education: A study in the Vietnamese context. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12333-z. Advance online publication.
Purković, D., Suman, D., & Jelaska, I. (2021). Age and gender differences between pupils’ preferences in teaching general and compulsory technology education in Croatia. International Journal of Technology and Design Education, 31(5), 919–937. https://doi.org/10.1007/s10798-020-09586-x.
Rani, G., Singh, J., & Khanna, A. (2023). Comparative analysis of generative AI models. In 2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT) (pp. 760–765). Faridabad, India: IEEE. https://doi.org/10.1109/ICAICCIT60255.2023.10465941.
Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2021). Social isolation and Acceptance of the Learning Management System (LMS) in the time of COVID-19 pandemic: An expansion of the UTAUT Model. Journal of Educational Computing Research, 59(2), 183–208. https://doi.org/10.1177/0735633120960421.
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.
Shirolkar, S. D., & Kadam, R. (2023). Determinants of adoption and usage of the online examination portal (OEP) in Indian universities. Education & Training (London), 65(6/7), 827–847. https://doi.org/10.1108/ET-09-2022-0360.
Steinberg, L., & Monahan, K. C. (2007). Age differences in resistance to peer influence. Developmental Psychology, 43, 1531–1543. https://doi.org/10.1037/0012-1649.43.6.1531.
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. https://doi.org/10.1080/10494820.2023.2209881. Advance online publication.
Strzelecki, A., & Elarabawy, S. (2024). Investigation of the moderation effect of gender and study level on the acceptance and use of GAI by higher education students: Comparative evidence from Poland and Egypt. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13425. Advance online publication.
Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis, 48(1), 159–205. https://doi.org/10.1016/j.csda.2004.03.005.
Tian, S., & Yang, W. (2023). Modeling the use behavior of interpreting technology for student interpreters: An extension of UTAUT model. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12225-2.
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(1), 15. https://doi.org/10.1186/s40561-023-00237-x.
Ustun, A. B., Karaoglan-Yilmaz, F. G., Yilmaz, R., Ceylan, M., & Uzun, O. (2023). Development of UTAUT-based augmented reality acceptance scale: A validity and reliability study. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12321-3. Advance online publication.
Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: US vs. China. Journal of Global Information Technology Management, 13(1), 5–27. https://doi.org/10.1080/1097198X.2010.10856507.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478. https://doi.org/10.2307/30036540.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 157–178. https://doi.org/10.2307/41410412.
Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428.
Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232. https://doi.org/10.1016/j.chb.2016.10.028.
Zhang, P., & Tur, G. (2023). A systematic review of ChatGPT use in K-12 education. European Journal of Education. https://doi.org/10.1111/ejed.12599. Advance online publication.
Zheng, L., Gao, L., & Huang, Z. (2024). Can Chatbots based on generative Artificial Intelligence Facilitate OnlineCollaborative Learning Performance? E-education Research, 03, 70–76. https://doi.org/10.13811/j.cnki.eer.2024.03.010.
Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760–767. https://doi.org/10.1016/j.chb.2010.01.013.
Acknowledgements
This research was funded by the Jiangsu Province Education Science “14th Five-Year Plan” Project (C/2023/01/64), and Interdisciplinary Research Foundation for the Doctoral Candidates of Beijing Normal University (Grant Number BNUXKJC2326).
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Du, L., Lv, B. Factors influencing students’ acceptance and use generative artificial intelligence in elementary education: an expansion of the UTAUT model. Educ Inf Technol 29, 24715–24734 (2024). https://doi.org/10.1007/s10639-024-12835-4
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DOI: https://doi.org/10.1007/s10639-024-12835-4