The utilization of artificial intelligence technologies in university education
DOI:
https://doi.org/10.17977/um063.v6.i4.2026.5Keywords:
Artificial Intelligence, University education, Learning effectiveness, Student satisfaction, Technology acceptance modelAbstract
Artificial Intelligence (AI) has become an important component of modern university education, offering significant opportunities to enhance teaching and learning processes, improve educational quality, and support innovative learning environments. Its growing adoption highlights the need to understand its effectiveness, acceptance, benefits, and challenges within higher education institutions. This study examines the role of AI technologies in university education and their impact on learning effectiveness and student satisfaction. A quantitative research approach was adopted, using a structured questionnaire to collect data and evaluate participants’ perceptions of AI utilization in higher education. The collected data were statistically analyzed to assess the relationships among the main study variables and determine the influence of AI technologies on the educational experience. The findings confirm that AI technologies have a profound impact on university education, significantly enhancing learning effectiveness and student satisfaction. They also support and extend the Technology Acceptance Model, demonstrating its relevance in emerging technological contexts. In conclusion, successful AI integration requires addressing technical and organizational challenges while balancing technological innovation with effective pedagogical strategies to achieve sustainable educational transformation and improve overall educational outcomes.
References
Biggs, J., & Tang, C. (2011). Teaching for quality learning at university (4th ed.). Open University Press.
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
IBM Corp. (2021). IBM SPSS Statistics 28 core system user’s guide. IBM.
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 140, 1–55.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.
Miao, F., Holmes, W., Huang, R., & Zhang, H. (2021). AI and education: Guidance for policy-makers. UNESCO. https://doi.org/10.54675/PCSP7350
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill-building approach (7th ed.). John Wiley & Sons.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
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
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, Article 39. https://doi.org/10.1186/s41239-019-0171-0
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
