Digital Self-Efficacy Among Faculty Members and Its Impact on the Adoption of Artificial Intelligence Tools and the Improvement of University Teaching Practices
(A Study Using Structural Equation Modeling)
Keywords:
Digital self-competence, faculty, artificial intelligence, university teaching
Abstract
This study aims to analyze the relationship between digital self-efficacy among faculty members in higher education and their adoption of artificial intelligence tools, while exploring the impact of this adoption on the quality of university teaching practices. The study is based on the theoretical premise that effective digital transformation is not achieved simply by providing technology, but rather requires the availability of psychological, cognitive, and organizational factors that enable users to employ this technology strategically within the educational context. In this framework, digital self-efficacy is a pivotal variable that explains faculty members' willingness to experiment with artificial intelligence tools and integrate them into their teaching strategies. The study combines self-efficacy theory with technology adoption models to construct a causal model that clarifies the direct and indirect relationships between four key variables: digital self-efficacy, institutional support and training, actual use of artificial intelligence tools, and the quality of university teaching practices. A quantitative analytical approach was employed, utilizing a multi-dimensional scientific questionnaire whose validity and reliability were statistically verified. Data were then collected from a sample of faculty members at higher education institutions. To analyze the relationships between variables, Structural Equation Modeling (SEM) was used, as it is one of the most suitable methods for testing complex causal models involving both direct and indirect effects. The results of the analysis demonstrated a statistically significant positive effect of digital self-efficacy on the actual use of artificial intelligence tools. Furthermore, institutional support played a reinforcing role in this use through the provision of appropriate training, infrastructure, and organizational policies. The results also revealed that the actual use of AI is associated with a marked improvement in teaching practices, particularly in the areas of designing educational activities, providing feedback, and stimulating student interaction.References
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2. 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
3. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
4. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
5. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
6. Schunk, D. H. (2012). Learning theories: An educational perspective (6th ed.). Pearson.
7. Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Educational Technology & Society, 14(4), 196–206.
8. UNESCO. (2021). AI and education: Guidance for policy-makers. UNESCO Publishing. https://unesdoc.unesco.org/
9. 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
Published
2026-04-17
How to Cite
Samar Mohammed Al-Harbi. (2026). Digital Self-Efficacy Among Faculty Members and Its Impact on the Adoption of Artificial Intelligence Tools and the Improvement of University Teaching Practices: (A Study Using Structural Equation Modeling). Era Journal for Humanities and Sociology, (22), 246-262. https://doi.org/10.33193/eJHAS.22.2026.438
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