Factors influencing university students' behavior in using artificial intelligence in learning: An empirical study from Ho Chi Minh City
Main Article Content
Abstract
This study examines the factors affecting university students' behavior regarding the use of artificial intelligence (AI) tools for learning. We utilize a mixed-methods approach, combining qualitative and quantitative research methodologies. The qualitative phase involves focus group discussions with 10 students to modify the measurement scales. Subsequently, quantitative research is conducted to test hypotheses and assess the research model, using survey data from 357 university students in Ho Chi Minh City. The research model is evaluated through the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. The results reveal that Perceived Usefulness and Perceived Ease of Use have a positive impact on students' Attitude toward using AI. Additionally, Attitudes toward using AI, Self-regulation, Information System Quality, and Hedonic Motivation are found to positively influence students' behavior toward the adoption of AI tools in learning. Based on these findings, several recommendations are proposed to enhance using AI in students’ learning activities.
Article Details
Keywords
Artificial intelligence, AI technology, AI tools, Higher education
References
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Al-Emran, M., Arpaci, I., & Salloum, S. A. (2020). An empirical examination of continuous intention to use m-learning: An integrated model. Education and Information Technologies, 25(4), 2899–2918. https://doi.org/10.1007/s10639-019- 10094-2
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology Acceptance Model in M-learning context: A systematic review. Computers & Education, 125(10/2018), 389–412. https://doi.org/10.1016/j.compedu.2018.06.008
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(July 2020), 1-25. https://doi.org/10.1016/j.techsoc.2020.101325
Alshurideh, M., Al Kurdi, B., Salloum, S. A., Arpaci, I., & Al-Emran, M. (2023). Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms. Interactive Learning Environments, 31(3), 1214-1228. https://doi.org/10.1080/10494820.2020.1826982
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
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, 1-15. https://doi.org/10.1080/10447318.2023.2261725
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), 1-18. https://doi.org/10.3390/math8112089
Cruz-Benito, J., Garcia-Penalvo, F. J., & Theron, R. (2019). Analyzing the software architectures supporting HCI/HMI processes through a systematic review of the literature. Telematics and Informatics, 38, 118-132. https://doi.org/10.1016/j.tele.2018.09.006
Đặng Văn Em, Nguyễn Đình Loan Phương và Nguyễn Thị Hảo (2024). Thực trạng ứng dụng ChatGPT trong việc học tập, nghiên cứu của sinh viên Đại học Quốc gia Thành phố Hồ Chí Minh. Tạp chí Giáo dục, 24(1), 36-41. https://tcgd.tapchigiaoduc.edu.vn/index.php/tapchi/article/view/1212
Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 318-339. https://doi.org/10.2307/249008
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
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.
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. doi:10.1108/EBR-11-2018-0203
Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2010). Multivariate Data Analysis. 7th Edition. NY: Pearson.
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, 115-135. https://doi.org/10.1007/s11747-014-0403-8
Huta, V., & Waterman, A. S. (2014). Eudaimonia and its distinction from hedonia: Developing a classification and terminology for understanding conceptual and operational definitions. Journal of Happiness Studies: An Interdisciplinary Forum on Subjective Well-Being, 15(6), 1425–1456. https://doi.org/10.1007/s10902-013-9485-0
Kim, C., & Hodges, C. B. (2012). Efects of an emotion control treatment on academic emotions, motivation, and achieve ment in an online mathematics course. Instructional Science, 40(1), 173–192. https://doi.org/10.1007/s11251-011-9165-6
Kohnke, L. (2023). L2 learners' perceptions of a chatbot as a potential independent language learning tool. International Journal of Mobile Learning and Organisation, 17(1/2), 214-226. https://doi.org/10.1504/IJMLO.2023.128339
Lin, Y., & Yu, Z. (2023). Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers. International Journal of Educational Technology in Higher Education, 20(1), 1-24. https://doi.org/10.1186/s41239-023-00403-8
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), 1-18. https://doi.org/10.1016/j.heliyon.2023.e20962
Morwitz, V. G., Steckel, J. H., & Gupta, A. (2007). When do purchase intentions predict sales?. International Journal of Forecasting, 23(3), 347-364. https://doi.org/10.1016/j.ijforecast.2007.05.015
Nguyễn Thị Phước (2023). Sử dụng chat GPT làm công cụ hỗ trợ trong việc dạy và học ngành truyền thông. Tạp Chí Khoa học Trường Đại học Quốc tế Hồng Bàng, 25, 95-100. https://doi.org/10.59294/HIUJS.25.2023.507
Salas‐Pilco, S. Z., Yang, Y., & Zhang, Z. (2022). Student engagement in online learning in Latin American higher education during the COVID‐19 pandemic: A systematic review. British Journal of Educational Technology, 53(3), 593-619. Doi: 10.1111/bjet.13190
Sewandono, R. E., Thoyib, A., Hadiwidjojo, D., & Rofiq, A. (2023). Performance expectancy of E-learning on higher institutions of education under uncertain conditions: Indonesia context. Education and information technologies, 28(4), 4041-4068. https://doi.org/10.1007/s10639-022-11074-9
Taub, M., Azevedo, R., Bouchet, F., & Khosravifar, B. (2014). Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments? Computers in Human Behavior, 39, 356–367. https://doi.org/10.1016/j.chb.2014.07.018
Thái Thị Cẩm Trang (2023). Thái độ và kì vọng của sinh viên sư phạm tiếng Anh đối với ChatGPT: nghiên cứu tại Trường Đại học Sư phạm Hà Nội. Tạp chí Giáo dục, 23(10), 51-56. https://tcgd.tapchigiaoduc.edu.vn/index.php/tapchi/article/view/767
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), 1-24. https://doi.org/10.1186/s40561-023-00237-x
Uchenna, E. O., & Oluchukwu, N. U. (2022). An appraisal of students’ adoption of e-learning communication tools: a SEM analysis. Education and Information Technologies, 27(7), 10239-10260. https://doi.org/10.1007/s10639-022-10975-z
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
Wang, S., Yu, H., Hu, X., & Li, J. (2020). Participant or spectator? Comprehending the willingness of faculty to use intelligent tutoring systems in the artificial intelligence era. British Journal of Educational Technology, 51(5), 1657-1673. https://doi.org/10.1111/bjet.12998
Wang, Y., Liu, C., & Tu, Y. F. (2021). Factors affecting the adoption of AI-based applications in higher education. Educational Technology & Society, 24(3), 116-129. https://www.jstor.org/stable/27032860
Wu, W., Zhang, B., Li, S., & Liu, H. (2022). Exploring factors of the willingness to accept AI-assisted learning environments: an empirical investigation based on the UTAUT model and perceived risk theory. Frontiers in Psychology, 13(June 2022), 1-10. https://doi.org/10.3389/fpsyg.2022.870777
Xue, L., Rashid, A. M., & Ouyang, S. (2024). The Unified Theory of Acceptance and Use of Technology (UTAUT) in Higher Education: A Systematic Review. SAGE Open, 14(1), https://doi.org/10.1177/21582440241229570
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/10.1177/07356331209520
Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological develop ments, and future prospects. American Educational Research Journal, 45(1), 166–183. https://doi.org/10.3102/0002831207312909
Zimmerman, B. J., & Schunk, D. H. (2011). Self-Regulated Learning and Performance. In B. J. Zimmerman, & D. H. Schunk (Eds.), Handbook of Self-Regulation of Learning and Performance (pp. 1-12). New York: Routledge.
Wogu, I. A. P., Misra, S., Olu-Owolabi, E. F., Assibong, P. A., Udoh, O. D., Ogiri, S. O., & Damasevicius, R. (2018). Artificial intelligence, artificial teachers and the fate of learners in the 21st century education sector: Implications for theory and practice. International Journal of Pure and Applied Mathematics, 119(16), 2245-2259. url: http://www.acadpubl.eu/hub/
Al-Emran, M., Arpaci, I., & Salloum, S. A. (2020). An empirical examination of continuous intention to use m-learning: An integrated model. Education and Information Technologies, 25(4), 2899–2918. https://doi.org/10.1007/s10639-019- 10094-2
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology Acceptance Model in M-learning context: A systematic review. Computers & Education, 125(10/2018), 389–412. https://doi.org/10.1016/j.compedu.2018.06.008
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(July 2020), 1-25. https://doi.org/10.1016/j.techsoc.2020.101325
Alshurideh, M., Al Kurdi, B., Salloum, S. A., Arpaci, I., & Al-Emran, M. (2023). Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms. Interactive Learning Environments, 31(3), 1214-1228. https://doi.org/10.1080/10494820.2020.1826982
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
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, 1-15. https://doi.org/10.1080/10447318.2023.2261725
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), 1-18. https://doi.org/10.3390/math8112089
Cruz-Benito, J., Garcia-Penalvo, F. J., & Theron, R. (2019). Analyzing the software architectures supporting HCI/HMI processes through a systematic review of the literature. Telematics and Informatics, 38, 118-132. https://doi.org/10.1016/j.tele.2018.09.006
Đặng Văn Em, Nguyễn Đình Loan Phương và Nguyễn Thị Hảo (2024). Thực trạng ứng dụng ChatGPT trong việc học tập, nghiên cứu của sinh viên Đại học Quốc gia Thành phố Hồ Chí Minh. Tạp chí Giáo dục, 24(1), 36-41. https://tcgd.tapchigiaoduc.edu.vn/index.php/tapchi/article/view/1212
Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 318-339. https://doi.org/10.2307/249008
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
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.
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. doi:10.1108/EBR-11-2018-0203
Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2010). Multivariate Data Analysis. 7th Edition. NY: Pearson.
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, 115-135. https://doi.org/10.1007/s11747-014-0403-8
Huta, V., & Waterman, A. S. (2014). Eudaimonia and its distinction from hedonia: Developing a classification and terminology for understanding conceptual and operational definitions. Journal of Happiness Studies: An Interdisciplinary Forum on Subjective Well-Being, 15(6), 1425–1456. https://doi.org/10.1007/s10902-013-9485-0
Kim, C., & Hodges, C. B. (2012). Efects of an emotion control treatment on academic emotions, motivation, and achieve ment in an online mathematics course. Instructional Science, 40(1), 173–192. https://doi.org/10.1007/s11251-011-9165-6
Kohnke, L. (2023). L2 learners' perceptions of a chatbot as a potential independent language learning tool. International Journal of Mobile Learning and Organisation, 17(1/2), 214-226. https://doi.org/10.1504/IJMLO.2023.128339
Lin, Y., & Yu, Z. (2023). Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers. International Journal of Educational Technology in Higher Education, 20(1), 1-24. https://doi.org/10.1186/s41239-023-00403-8
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), 1-18. https://doi.org/10.1016/j.heliyon.2023.e20962
Morwitz, V. G., Steckel, J. H., & Gupta, A. (2007). When do purchase intentions predict sales?. International Journal of Forecasting, 23(3), 347-364. https://doi.org/10.1016/j.ijforecast.2007.05.015
Nguyễn Thị Phước (2023). Sử dụng chat GPT làm công cụ hỗ trợ trong việc dạy và học ngành truyền thông. Tạp Chí Khoa học Trường Đại học Quốc tế Hồng Bàng, 25, 95-100. https://doi.org/10.59294/HIUJS.25.2023.507
Salas‐Pilco, S. Z., Yang, Y., & Zhang, Z. (2022). Student engagement in online learning in Latin American higher education during the COVID‐19 pandemic: A systematic review. British Journal of Educational Technology, 53(3), 593-619. Doi: 10.1111/bjet.13190
Sewandono, R. E., Thoyib, A., Hadiwidjojo, D., & Rofiq, A. (2023). Performance expectancy of E-learning on higher institutions of education under uncertain conditions: Indonesia context. Education and information technologies, 28(4), 4041-4068. https://doi.org/10.1007/s10639-022-11074-9
Taub, M., Azevedo, R., Bouchet, F., & Khosravifar, B. (2014). Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments? Computers in Human Behavior, 39, 356–367. https://doi.org/10.1016/j.chb.2014.07.018
Thái Thị Cẩm Trang (2023). Thái độ và kì vọng của sinh viên sư phạm tiếng Anh đối với ChatGPT: nghiên cứu tại Trường Đại học Sư phạm Hà Nội. Tạp chí Giáo dục, 23(10), 51-56. https://tcgd.tapchigiaoduc.edu.vn/index.php/tapchi/article/view/767
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), 1-24. https://doi.org/10.1186/s40561-023-00237-x
Uchenna, E. O., & Oluchukwu, N. U. (2022). An appraisal of students’ adoption of e-learning communication tools: a SEM analysis. Education and Information Technologies, 27(7), 10239-10260. https://doi.org/10.1007/s10639-022-10975-z
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
Wang, S., Yu, H., Hu, X., & Li, J. (2020). Participant or spectator? Comprehending the willingness of faculty to use intelligent tutoring systems in the artificial intelligence era. British Journal of Educational Technology, 51(5), 1657-1673. https://doi.org/10.1111/bjet.12998
Wang, Y., Liu, C., & Tu, Y. F. (2021). Factors affecting the adoption of AI-based applications in higher education. Educational Technology & Society, 24(3), 116-129. https://www.jstor.org/stable/27032860
Wu, W., Zhang, B., Li, S., & Liu, H. (2022). Exploring factors of the willingness to accept AI-assisted learning environments: an empirical investigation based on the UTAUT model and perceived risk theory. Frontiers in Psychology, 13(June 2022), 1-10. https://doi.org/10.3389/fpsyg.2022.870777
Xue, L., Rashid, A. M., & Ouyang, S. (2024). The Unified Theory of Acceptance and Use of Technology (UTAUT) in Higher Education: A Systematic Review. SAGE Open, 14(1), https://doi.org/10.1177/21582440241229570
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/10.1177/07356331209520
Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological develop ments, and future prospects. American Educational Research Journal, 45(1), 166–183. https://doi.org/10.3102/0002831207312909
Zimmerman, B. J., & Schunk, D. H. (2011). Self-Regulated Learning and Performance. In B. J. Zimmerman, & D. H. Schunk (Eds.), Handbook of Self-Regulation of Learning and Performance (pp. 1-12). New York: Routledge.
Wogu, I. A. P., Misra, S., Olu-Owolabi, E. F., Assibong, P. A., Udoh, O. D., Ogiri, S. O., & Damasevicius, R. (2018). Artificial intelligence, artificial teachers and the fate of learners in the 21st century education sector: Implications for theory and practice. International Journal of Pure and Applied Mathematics, 119(16), 2245-2259. url: http://www.acadpubl.eu/hub/
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