Abstract

Teaching effectiveness has been a prominent element in the success of the educational sector around the globe. The present study investigates the impact of adoption, ease of use, and teachers’ experience of artificial intelligence [AI] and the moderating role of students’ interest on the teaching effectiveness of Vietnamese universities. The study collected data from the students and teachers who are doing and teaching electrical and electronic engineering using survey questionnaires. The study also checks the validity and nexus among variables using smart-PLS. The outcomes indicated that the adoption, ease of use, and teachers’ experience of AI have a positive linkage with the teaching effectiveness of Vietnamese universities. The outputs also revealed that student interest significantly moderates this relationship. The study helps the policymakers in developing policies related to enhance teaching effectiveness using effective AI adoption.  

Keywords

References

• Al Rajab, M., Odeh, S., Hazboun, S., & Alheeh, E. (2023). AI-powered smart book: enhancing arabic education in Palestine with augmented reality [Paper presentation]. International Symposium on Ambient Intelligence, Guimaraes, Portugal. https://doi.org/10.1007/978-3-031-43461-7_17
• Allal-Chérif, O., Aránega, A. Y., & Sánchez, R. C. (2021). Intelligent recruitment: How to identify, select, and retain talents from around the world using artificial intelligence. Technological Forecasting and Social Change, 169, 120-822. https://doi.org/10.1016/j.techfore.2021.120822
• Bhutoria, A. (2022). Personalized education and artificial intelligence in the United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 3, 100-168. https://doi.org/10.1016/j.caeai.2022.100068
• Bowden, J. L.-H., Tickle, L., & Naumann, K. (2021). The four pillars of tertiary student engagement and success: a holistic measurement approach. Studies in Higher Education, 46(6), 1207-1224. https://doi.org/10.1080/03075079.2019.1672647
• Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2021). Introduction of human-centric AI assistant to aid radiologists for multimodal breast image classification. International Journal of Human-Computer Studies, 150, 102-607. https://doi.org/10.1016/j.ijhcs.2021.102607
• Choi, S., Jang, Y., & Kim, H. (2023). Influence of pedagogical beliefs and perceived trust on teachers’ acceptance of educational artificial intelligence tools. International Journal of Human–Computer Interaction, 39(4), 910-922. https://doi.org/10.1080/10447318.2022.2049145
• Chu, S. K. W., Reynolds, R. B., Tavares, N. J., Notari, M., & Lee, C. W. Y. (2021). 21st century skills development through inquiry-based learning from theory to practice. Springer.
• Delgado, J. M. D., Oyedele, L., Demian, P., & Beach, T. (2020). A research agenda for augmented and virtual reality in architecture, engineering and construction. Advanced Engineering Informatics, 45, 101-122. https://doi.org/10.1016/j.aei.2020.101122
• Demmans Epp, C., Daniel, B. K., & Muldner, K. (2023). Learning analytics for supporting individualization: data-informed adaptation of learning. Frontiers in Education, 8, 1240377. https://doi.org/10.3389/feduc.2023.1240377
• Dimitriadou, E., & Lanitis, A. (2023). A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms. Smart Learning Environments, 10(1), 12-21. https://doi.org/10.1186/s40561-023-00231-3
• Ebadi, S., & Amini, A. (2022). Examining the roles of social presence and human-likeness on Iranian EFL learners’ motivation using artificial intelligence technology: A case of CSIEC chatbot. Interactive Learning Environments, 32(2), 655-673. https://doi.org/10.1080/10494820.2022.2096638
• Essel, H. B., Vlachopoulos, D., Tachie-Menson, A., Johnson, E. E., & Baah, P. K. (2022). The impact of a virtual teaching assistant (chatbot) on students' learning in Ghanaian higher education. International Journal of Educational Technology in Higher Education, 19(1), 45-57. https://doi.org/10.1186/s41239-022-00362-6
• Fernandez, D., Dastane, O., Omar Zaki, H., & Aman, A. (2024). Robotic process automation: bibliometric reflection and future opportunities. European Journal of Innovation Management, 27(2), 692-712. https://doi.org/10.1108/EJIM-10-2022-0570
• Frick, T. W., Chadha, R., Watson, C., Wang, Y., & Green, P. (2009). College student perceptions of teaching and learning quality. Educational Technology Research and Development, 57, 705-720. https://doi.org/10.1007/s11423-007-9079-9
• Grewal, D., Guha, A., Schweiger, E., Ludwig, S., & Wetzels, M. (2022). How communications by AI-enabled voice assistants impact the customer journey. Journal of Service Management, 33(4/5), 705-720. https://doi.org/10.1108/JOSM-11-2021-0452
• Guan, C., Mou, J., & Jiang, Z. (2020). Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies, 4(4), 134-147. https://doi.org/10.1016/j.ijis.2020.09.001
• Hair Jr, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101-110. https://doi.org/10.1016/j.jbusres.2019.11.069
• Haleem, A., Javaid, M., & Singh, R. P. (2022). An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 2(4), 100-189. https://doi.org/10.1016/j.tbench.2023.100089
• Hernandez-de-Menendez, M., Escobar Díaz, C., & Morales-Menendez, R. (2020). Technologies for the future of learning: state of the art. International Journal on Interactive Design and Manufacturing, 14(2), 683-695. https://doi.org/10.1007/s12008-019-00640-0
• Huang, A. Y., Lu, O. H., & Yang, S. J. (2023). Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, 104-684. https://doi.org/10.1016/j.compedu.2022.104684
• Jiao, P., Ouyang, F., Zhang, Q., & Alavi, A. H. (2022). Artificial intelligence-enabled prediction model of student academic performance in online engineering education. Artificial Intelligence Review, 55(8), 6321-6344. https://doi.org/10.1007/s10462-022-10155-y
• Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, 100-117. https://doi.org/10.1016/j.caeai.2021.100017
• Kaswan, K. S., Dhatterwal, J. S., & Ojha, R. P. (2024). AI in personalized learning. In A. Garg, B. V. Babu, & V. E. Balas (Eds.), Advances in Technological Innovations in Higher Education (pp. 103-117). CRC Press.
• Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S. & Gašević, D. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, 100-124. https://doi.org/10.1016/j.caeai.2022.100074
• Kim, J., Lee, H., & Cho, Y. H. (2022). Learning design to support student-AI collaboration: Perspectives of leading teachers for AI in education. Education and Information Technologies, 27(5), 6069-6104. https://doi.org/10.1007/s10639-021-10831-6
• Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). Exploring generative artificial intelligence preparedness among university language instructors: A case study. Computers and Education: Artificial Intelligence, 5, 100-156. https://doi.org/10.1016/j.caeai.2023.100156
• 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. https://doi.org/10.1007/s10639-022-11177-3
• Larrabee Sønderlund, A., Hughes, E., & Smith, J. (2019). The efficacy of learning analytics interventions in higher education: A systematic review. British Journal of Educational Technology, 50(5), 2594-2618. https://doi.org/10.1111/bjet.12720
• Lazar, I. M., Panisoara, G., & Panisoara, I. O. (2020). Digital technology adoption scale in the blended learning context in higher education: Development, validation and testing of a specific tool. Plos One, 15(7), 1-27. https://doi.org/10.1371/journal.pone.0235957
• Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S. J., Ogata, H., Baltes, J., Guerra, R., Li, P. & Tsai, C.-C. (2020). Challenges and future directions of big data and artificial intelligence in education. Frontiers in Psychology, 11, 580820. https://doi.org/10.3389/fpsyg.2020.580820
• Mazer, J. P. (2012). Development and validation of the student interest and engagement scales. Communication Methods and Measures, 6(2), 99-125. https://doi.org/10.1080/19312458.2012.679244
• Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221-4241. https://doi.org/10.1007/s10639-022-11316-w
• Oduro, S. (2020). Exploring the barriers to SMEs’ open innovation adoption in Ghana: A mixed research approach. International Journal of Innovation Science, 12(1), 21-51. https://doi.org/10.1108/IJIS-11-2018-0119
• Rahiman, H. U., & Kodikal, R. (2024). Revolutionizing education: Artificial intelligence empowered learning in higher education. Cogent Education, 11(1), 229-3431. https://doi.org/10.1080/2331186X.2023.2293431
• Reeve, J., & Cheon, S. H. (2021). Autonomy-supportive teaching: Its malleability, benefits, and potential to improve educational practice. Educational Psychologist, 56(1), 54-77. https://doi.org/10.1080/00461520.2020.1862657
• Rerhaye, L., Altun, D., Krauss, C., & Müller, C. (2021). Evaluation methods for an AI-supported learning management system: quantifying and qualifying added values for teaching and learning. In R. A. Sottilare, & J. Schwarz (Eds.), Adaptive Instructional Systems. Design and Evaluation: Third International Conference Proceedings (pp. 394-411). Springer. https://doi.org/10.1007/978-3-030-77857-6_28
• Srinivasa, K., Kurni, M., & Saritha, K. (2022). Harnessing the power of AI to education. In K. Srinivasa, M. Kurni, & K. Saritha (Eds.), Learning, teaching, and assessment methods for contemporary learners: pedagogy for the digital generation (pp. 311-342). Springer. http://dx.doi.org/10.1007/978-981-19-6734-4_13
• Walkington, C., & Bernacki, M. L. (2019). Personalizing algebra to students’ individual interests in an intelligent tutoring system: Moderators of impact. International Journal of Artificial Intelligence in Education, 29, 58-88. https://doi.org/10.1007/s40593-018-0168-1
• Xu, W., & Ouyang, F. (2022). A systematic review of AI role in the educational system based on a proposed conceptual framework. Education and Information Technologies, 27(3), 4195-4223. https://doi.org/10.1007/s10639-021-10774-y
• Yu, S., & Lu, Y. (2021). An introduction to artificial intelligence in education. Springer. https://doi.org/10.1007/978-981-16-2770-5
• Yu, Z., Gao, M., & Wang, L. (2021). The effect of educational games on learning outcomes, student motivation, engagement and satisfaction. Journal of Educational Computing Research, 59(3), 522-546. https://doi.org/10.1177/0735633120969214

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.