Tác động của trí tuệ nhân tạo: Ý định hành vi, kết quả học tập và sự hài lòng
Nội dung chính của bài viết
Tóm tắt
Sự phát triển của trí tuệ nhân tạo (AI) đã thay đổi thói quen sử dụng công cụ học tập của sinh viên tại TP.HCM. Nghiên cứu này đánh giá tác động của việc chấp nhận AI lên kết quả học tập thông qua các yếu tố: ý định hành vi, đổi mới cá nhân, cải thiện thành tích học tập, sự hài lòng và độ chính xác của thông tin. Dữ liệu từ 326 sinh viên sử dụng AI được phân tích bằng PLS-SEM trên SmartPLS. Mẫu nghiên cứu được thu thập từ sinh viên thuộc các trường cao đẳng, đại học và học viện, đại diện cho nhiều ngành học khác nhau như khoa học tự nhiên, khoa học xã hội và nhân văn, kinh tế, kỹ thuật và công nghệ, y học…Kết quả cho thấy, sự hài lòng chịu ảnh hưởng mạnh từ cải thiện thành tích học tập và độ chính xác của thông tin, trong khi ý định hành vi và đổi mới cá nhân thúc đẩy việc sử dụng AI. Điểm mới của nghiên cứu nằm ở việc tích hợp độ chính xác của thông tin – yếu tố ít được chú ý trước đây – và làm rõ mối liên hệ giữa hành vi cá nhân với sự hài lòng, đóng góp vào lý thuyết AI trong giáo dục. Nghiên cứu còn cung cấp góc nhìn địa phương hóa tại Việt Nam về lĩnh vực này. Các hàm ý khuyến nghị từ nghiên cứu bao gồm: (1) phát triển công cụ AI với dữ liệu chính xác, cập nhật, hỗ trợ kiểm chứng thông tin; (2) thiết kế giao diện thân thiện, tích hợp hướng dẫn sử dụng để tăng ý định hành vi; (3) tổ chức khóa học kỹ năng AI cho sinh viên, tập trung vào ứng dụng thực tiễn để nâng cao thành tích học tập.
Abstract
The advancement of artificial intelligence (AI) has transformed the learning tool usage habits of students in Ho Chi Minh City. This study examines the impact of AI adoption on academic outcomes through behavioral intention, personal innovativeness, academic performance improvement, satisfaction, and information accuracy. Data from 326 AI-using students were analyzed using PLS-SEM on SmartPLS. The research sample was collected from students studying in colleges, universities, and academies, representing multiple fields of study, including natural sciences, social sciences and humanities, economics, engineering and technology, medicine, etc. Results indicate that satisfaction is strongly influenced by improvement in academic performance and information accuracy, while behavioral intention and personal innovativeness enhance the use of AI. The study’s novelty lies in integrating information accuracy – a previously underexplored factor – and elucidating the link between individual behavior and satisfaction, contributing to AI-in-education theory. It also offers a localized perspective from Vietnam. Practical implications include: (1) developing AI tools with accurate, updated data and fact-checking features; (2) designing user-friendly interfaces with integrated usage guides to boost behavioral intention; and (3) organizing AI skills courses for students, focusing on practical applications to enhance academic performance.
Chi tiết bài viết
Lĩnh vực kinh tế (JEL Codes)
O57 - Comparative Studies of Countries - Economywide Country Studies
Tài liệu tham khảo
Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301-314.
Ahmad, M. F., & Ghapar, W. R. G. W. A. (2019). The era of artificial intelligence in Malaysian higher education: Impact and challenges in tangible mixed-reality learning system toward self exploration education (SEE). Procedia Computer Science, 163, 2-10. https://doi.org/10.1016/j.procs.2019.12.079
Al-Emran, M., AlQudah, A. A., Abbasi, G. A., Al-Sharafi, M. A., & Iranmanesh, M. (2024). Determinants of using AI-based chatbots for knowledge sharing: evidence from PLS-SEM and fuzzy sets (fsQCA). IEEE Transactions on Engineering Management, 71, 4985-4999. https://doi.org/10.1109/tem.2023.3237789
Al-Emran M, Arpaci I and Salloum SA (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-Fraihat, D., Joy, M., & Sinclair, J. (2017). Identifying success factors for e-learning in higher education. International Conference on E-Learning, pp. 247–255.
Alkawsi, G., Ali, N., & Baashar, Y. (2021). The moderating role of personal innovativeness and users experience in accepting the smart meter technology. Applied Sciences,11(8), 3297.
Aparicio, M., Bacao, F., & Oliveira, T. (2017). Grit in the path to elearning success. Computers in Human Behavior, 66, 388–399. https://doi.org/10.1016/j.chb.2016.10.009
Assiri, A., Al-Ghamdi, A. A. M., & Brdesee, H. (2020). From traditional to intelligent academic advising: A systematic literature review of e-academic advising. Int. J. Adv. Comput. Sci. Appl, 11(4), 507-517.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370.
Bilquise, G., Ibrahim, S. & Salhieh, S. M. (2023). Investigating student acceptance of an academic advising chatbot in higher education institutions. Education and Information Technologies. https://doi. org/10.1007/s10639-023-12076-x.
Bilquise, G., Ibrahim, S., & Salhieh, S. E. M. (2024). Investigating student acceptance of an academic advising chatbot in higher education institutions. Education and Information Technologies, 29(5), 6357-6382.
Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artifcial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies,25, 3443–3463.
Chumkaew S (2023) The development of chatbot provided registration information services for students in distance learning. ABAC Journal 43(4): 97–112.
Crawford, J., Cowling, M., & Allen, K. A. (2023). Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). Journal of University Teaching & Learning Practice, 20(3), 02.
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Vighio, M. S., Alblehai, F., Soomro, R. B., & Shutaleva, A. (2024). Investigating AI-based academic support acceptance and its impact on students’ performance in Malaysian and Pakistani higher education institutions. Education and Information Technologies, 1-50.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly,13, 319–340.
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
Ellerton, W. (2023). The Human and Machine: OpenAI, ChatGPT, Quillbot, Grammarly, Google, Google Docs, & humans. Visible Language, 57(1), 38-52.
Elliott, K. M., & Shin, D. (2002). Student satisfaction: An alternative approach to assessing this important concept. Journal of Higher Education Policy and Management, 24(2), 197–209. https://doi.org/10.1080/1360080022000013518
Farooq, M. S., Salam, M., Jaafar, N., Fayolle, A., Ayupp, K., Radovic-Markovic, M., & Sajid, A. (2017). Acceptance and use of lecture capture system (LCS) in executive business studies. Interactive Technology and Smart Education, 14(4), 329–348. https://doi.org/10.1108/ITSE-06-2016-0015
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/10.1177/002224378101800104
Foroughi, B., Nhan, P. V., Iranmanesh, M., Ghobakhloo, M., Nilashi, M., & Yadegaridehkordi, E. (2023). Determinants of intention to use autonomous vehicles: Findings from PLS-SEM and ANFIS. Journal of Retailing and Consumer Services, 70, 103158. https://doi.org/10.1016/j.jretconser.2022.103158
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.
Freeze, R. D., Alshare, K. A., Lane, P. L., & Wen, H. J. (2010). IS success model in e-learning context based on students’ perceptions. Journal of Information Systems Education, 21(2), 173–184.
Gill, S. S., Xu, M., Patros, P., Wu, H., Kaur, R., Kaur, K., ... & Buyya, R. (2024). Transformative effects of ChatGPT on modern education: Emerging era 12 IFLA Journal XX(X) of AI chatbots. Internet of Things and Cyber-Physical Systems 4: 19–23.
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS quarterly, 213-236.
Gopal, R., Singh, V., & Aggarwal, A. (2021). Impact of online classes on the satisfaction and performance of students during the pandemic period of Covid-19. Education and Information Technologies, 26(6), 6923-6947. doi:10.1007/s10639-021-10523-1
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook (p. 197). Springer Nature
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
Hallal K, Hamdan R and Tlais S (2023) Exploring the potential of AI-chatbots in organic chemistry: An assessment of ChatGPT and Bard. Computers and Education: Artificial Intelligence 5: Article 100170
Houhamdi, Z., & Athamena, B. (2019). Impacts of information quality on decision-making. Global Business and Economics Review, 21(1), 26–42. https://doi.org/10.1504/GBER.2019.096854
Huang, H., Chen, Y., & Rau, P. L. P. (2022). Exploring acceptance of intelligent tutoring system with pedagogical agent among high school students. Universal Access in the Information Society, 21(2), 381-392.
Johnson, C., Gitay, R., Abdel-Salam, A. S. G., BenSaid, A., Ismail, R., Al-Tameemi, R. A. N., ... & Al Hazaa, K. (2022). Student support in higher education: campus service utilization, impact, and challenges. Heliyon, 8(12).
Khan RA and Qudrat-Ullah H (2021) Technology adoption theories and models. In Adoption of LMS in Higher Educational Institutions of the Middle East. Cham, Zug: Springer International Publishing, 27–48.
Kuhail, M. A., Alturki, N., Alramlawi, S., & Alhejori, K. (2023). Interacting with educational chatbots: A systematic review. Education and Information Technologies 28(1): 973–1018.
Lai Y, Saab N and Admiraal W (2022) University students’ use of mobile technology in self-directed language learning: Using the integrative model of behavior prediction. Computers & Education 179: 104413.
Lee, D., & Yeo, S. (2022). Developing an AI-based chatbot for practicing responsive teaching in mathematics. Computers & Education, 191, 104646.
Li, M., & Xu, H. (2020). AI-driven language apps and their impact on traditional language learning methods. Journal of Computer Assisted Learning, 36(4), 561–574.
McLeay, M., Radia, A., & Thomas, R. (2014). Money creation in the modern economy. Bank of England quarterly bulletin, Q1.
Namahoot, K. S., & Laohavichien, T. (2015). An analysis of behavioral intention to use Thai internet banking with quality management and trust. The Journal of Internet Banking and Commerce, 20(3), 119. https://doi.org/10.4172/1204-5357.1000119
Pillai, R., Sivathanu, B., Metri, B., & Kaushik, N. (2023). Students’ adoption of AI-based teacher-bots (T-bots) for learning in higher education. Information Technology & People, 37(1), 328–355. https://doi.org/10.1108/ITP-02-2021-0152.
Ringle, C., Da Silva, D., & Bido, D. (2015). Structural equation modeling with the SmartPLS. Bido, D., da Silva, D., & Ringle, C.(2014). Structural Equation Modeling with the Smartpls. Brazilian Journal Of Marketing, 13(2).
Sadrzadehrafiei, S., Chofreh, A. G., Hosseini, N. K., & Sulaiman, R. (2013). The benefits of enterprise resource planning (ERP) system implementation in dry food packaging industry. Procedia Technology, 11, 220–226. https:// doi.org/10.1016/j.protcy.2013.12.184
Sakulwichitsintu S (2023) ParichartBOT: A chatbot for automatic answering for postgraduate students of an open university. International Journal of Information Technology 15(3): 1387–1397.
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
Tawafak, R. M., Al-Rahmi, W. M., Almogren, A. S., Al Adwan, M. N., Safori, A., Attar, R. W., & Habes, M. (2023). Analysis of E-learning system use using combined TAM and ECT factors. Sustainability, 15(14), 11100.
Twum, K. K., Ofori, D., Keney, G., & Korang-Yeboah, B. (2022). Using the UTAUT, personal innovativeness and perceived fnancial cost to examine student’s intention to use E-learning. Journal of Science and Technology Policy Management,13(3), 713–737.
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
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.v
Xu, J., Benbasat, I., & Cenfetelli, R. T. (2013). Integrating service quality with system and information quality: An empirical test in the e-service context. MIS quarterly, 777-794.
Yi, M. Y., Fiedler, K. D., & Park, J. S. (2006). Understanding the role of individual innovativeness in the acceptance of IT‐based innovations: Comparative analyses of models and measures. Decision sciences, 37(3), 393-426.
Yuce, A., Abubakar, A. M., & Ilkan, M. (2019). Intelligent tutoring systems and learning performance: Applying task-technology fit and IS success model. Online Information Review, 43(4), 600-616.
Zulfa, S., Dewi, R. S., Hidayat, D. N., Hamid, F., & Defanty, M. (2023). The Use of AI and Technology Tools in Developing Students’ English Academic Writing Skills. International Conference on Education,1(1), 47–63.