The impacts of Artificial Intelligence (AI) on high school students' academic performance: Empirical evidence from a developing country
Published: July 29, 2026
Abstract
This study explored the impacts of artificial intelligence (AI) on Vietnamese high school students' academic performance. The study used a mixed-method approach with PLS-SEM analysis of 370 responses. The study’s findings showed that perceived usefulness positively influences attitudes and intentions to use AI. The use of AI indirectly positively affects absorptive capacity, indirectly influencing academic performance. Personal innovativeness in IT positively influences AI usage and academic performance. The study provided insights for educators and policymakers on AI's role in education, particularly in Vietnamese high schools, highlighting AI's indirect influence on academic performance through enhanced absorptive capacity.
Keywords
AI usageabsorptive capacityacademic performancepersonal innovativeness
References
1.
Agarwal, R., & Prasad, J. (1998). A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology. Information Systems Research, 9(2), 204–215. https://doi.org/10.1287/isre.9.2.204
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
3.
Al-Adwan, A. S., & Al-Debei, M. M. (2024). The determinants of Gen Z’s metaverse adoption decisions in higher education: Integrating UTAUT2 with personal innovativeness in IT. Education and Information Technologies, 29(6), 7413–7445. https://doi.org/10.1007/s10639-023-12080-1
Álvarez-Marín, A., Velázquez-Iturbide, J. Á., & Castillo-Vergara, M. (2023). The acceptance of augmented reality in engineering education: The role of technology optimism and technology innovativeness. Interactive Learning Environments, 31(6), 3409–3421. https://doi.org/10.1080/10494820.2021.1928710
Banda, H. J., & Nzabahimana, J. (2023). The Impact of Physics Education Technology (PhET) Interactive Simulation-Based Learning on Motivation and Academic Achievement Among Malawian Physics Students. Journal of Science Education and Technology, 32(1), 127–141. https://doi.org/10.1007/s10956-022-10010-3
Boroomand, F., & Chan, Y. E. (2022). Digital absorptive capacity: Developing an instrument. Knowledge Management Research & Practice, 1–12.
7.
Brink, H. W., Loomans, M. G. L. C., Mobach, M. P., & Kort, H. S. M. (2021). Classrooms’ indoor environmental conditions affecting the academic achievement of students and teachers in higher education: A systematic literature review. Indoor Air, 31(2), 405–425. https://doi.org/10.1111/ina.12745
Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. Statistical Strategies for Small Sample Research, 1(1), 307–341.
9.
Chiquet, S., Martarelli, C. S., Weibel, D., & Mast, F. W. (2023). Learning by teaching in immersive virtual reality–Absorption tendency increases learning outcomes. Learning and Instruction, 84, 101716.
10.
Cohen, W. M., & Levinthal, D. A. (1990a). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 128–152.
11.
Cohen, W. M., & Levinthal, D. A. (1990b). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1), 128. https://doi.org/10.2307/2393553
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
Fernández-Mesa, A., Olmos-Penuela, J., García-Granero, A., & Oltra, V. (2022). The pivotal role of students’ absorptive capacity in management learning. The International Journal of Management Education, 20(3), 100687.
14.
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. JOURNAL OF MARKETING RESEARCH, 12.
15.
Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185–214.
16.
Grájeda, A., Burgos, J., Córdova, P., & Sanjinés, A. (2024). Assessing student-perceived impact of using artificial intelligence tools: Construction of a synthetic index of application in higher education. Cogent Education, 11(1), 2287917. https://doi.org/10.1080/2331186X.2023.2287917
Grani#, A., & Maranguni#, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
19.
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.
20.
Jansen, J. J. P., Van Den Bosch, F. A. J., & Volberda, H. W. (2006). Exploratory Innovation, Exploitative Innovation, and Performance: Effects of Organizational Antecedents and Environmental Moderators. Management Science, 52(11), 1661–1674. https://doi.org/10.1287/mnsc.1060.0576
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
Mishra, A. N., & Agarwal, R. (2010). Technological Frames, Organizational Capabilities, and IT Use: An Empirical Investigation of Electronic Procurement. Information Systems Research, 21(2), 249–270. https://doi.org/10.1287/isre.1080.0220
Olugboja, A., & Agbakwuru, E. M. (2024). Bridging Healthcare Disparities in Rural Areas of Developing Countries: Leveraging Artificial Intelligence for Equitable Access. 2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA), 1–6. https://ieeexplore.ieee.org/abstract/document/10467443/
24.
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35.
25.
Teo, T., Huang, F., & Hoi, C. K. W. (2018). Explicating the influences that explain intention to use technology among English teachers in China. Interactive Learning Environments, 26(4), 460–475. https://doi.org/10.1080/10494820.2017.1341940
Uzumcu, O., & Acilmis, H. (2024). Do Innovative Teachers use AI-powered Tools More Interactively? A Study in the Context of Diffusion of Innovation Theory. Technology, Knowledge and Learning, 29(2), 1109–1128. https://doi.org/10.1007/s10758-023-09687-1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.
28.
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.
29.
Whelan, E., Islam, A. N., & Brooks, S. (2020). Applying the SOBC paradigm to explain how social media overload affects academic performance. Computers & Education, 143, 103692.
30.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0