How Does AI Readiness Shape Teacher Performance? The Mediating Roles of Digital Literacy and Digital Competence In Education Management

Enny Kartini; Jubaidi Jubaidi

doi:10.29240/jsmp.v10i2.18077

Authors

Enny Kartini IKIP PGRI Kalimantan Timur, Indonesia https://orcid.org/0009-0007-4597-5214
Jubaidi Universitas Muhammadiyah Kalimantan Timur, Indonesia https://orcid.org/0009-0004-3347-1818

DOI:

https://doi.org/10.29240/jsmp.v10i2.18077

Keywords:

AI Readiness, Digital Literacy, Digital Competence, Teacher Performance, Educational Management

Abstract

The integration of artificial intelligence (AI) in education requires teachers to develop readiness to adopt and use emerging technologies. However, AI readiness does not necessarily improve teacher performance directly. This study examines the effect of AI Readiness on Teacher Performance and the mediating roles of Digital Literacy and Digital Competence. A quantitative survey was conducted involving 188 teachers from Muhammadiyah elementary schools in Samarinda, Indonesia. Data were analyzed using SEM-PLS with SmartPLS 4. The results show that AI Readiness positively and significantly affects Digital Literacy and Digital Competence. Both Digital Literacy and Digital Competence also positively and significantly affect Teacher Performance, while AI Readiness has no significant direct effect. Mediation analysis confirms that Digital Literacy and Digital Competence fully mediate the relationship between AI Readiness and Teacher Performance. These findings indicate that AI readiness alone is insufficient to improve teacher performance and must be supported by adequate digital capabilities. This study contributes to educational management research by highlighting Digital Literacy and Digital Competence as key mechanisms linking AI readiness to teacher performance.

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References

Amemasor, S. K., Oppong, S. O., Ghansah, B., Benuwa, B.-B., & Essel, D. D. (2025). A systematic review on the impact of teacher professional development on digital instructional integration and teaching practices. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1541031

Ayanwale, M. A., Adelana, O. P., Molefi, R. R., Adeeko, O., & Ishola, A. M. (2024). Examining artificial intelligence literacy among pre-service teachers for future classrooms. Computers and Education Open, 6, 100179. https://doi.org/10.1016/j.caeo.2024.100179

Aydin, M. K., Yildirim, T., & Kus, M. (2024). Teachers’ digital competences: a scale construction and validation study. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1356573

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. In Social foundations of thought and action: A social cognitive theory. Prentice-Hall, Inc.

Bandura, A. (1997). Self-efficacy: The exercise of control. In Self-efficacy: The exercise of control. W H Freeman/Times Books/ Henry Holt & Co.

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.

Brownlee, P., McGraw, A., Talbot, D., & Buchanan, J. (2024). Intellectual freedom and teaching performance assessment in Australia. The Australian Educational Researcher, 51(2), 781–797. https://doi.org/10.1007/s13384-023-00623-x

Chiu, T. K. F., Ahmad, Z., & Çoban, M. (2025). Development and validation of teacher artificial intelligence (AI) competence self-efficacy (TAICS) scale. Education and Information Technologies, 30(5), 6667–6685. https://doi.org/10.1007/s10639-024-13094-z

Chiu, T. K. F., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6, 100171. https://doi.org/10.1016/j.caeo.2024.100171

Chiu, T. K. F., Falloon, G., Song, Y., Wong, V. W. L., Zhao, L., & Ismailov, M. (2024). A self-determination theory approach to teacher digital competence development. Computers & Education, 214, 105017. https://doi.org/10.1016/j.compedu.2024.105017

Claro, M., Castro-Grau, C., Ochoa, J. M., Hinostroza, J. E., & Cabello, P. (2024). Systematic review of quantitative research on digital competences of in-service school teachers. Computers & Education, 215, 105030. https://doi.org/10.1016/j.compedu.2024.105030

Damanik, J., & Widodo, W. (2024). Unlocking Teacher Professional Performance: Exploring Teaching Creativity in Transmitting Digital Literacy, Grit, and Instructional Quality. Education Sciences, 14(4), 384. https://doi.org/10.3390/educsci14040384

Delcker, J., Heil, J., & Ifenthaler, D. (2025). Evidence-based development of an instrument for the assessment of teachers’ self-perceptions of their artificial intelligence competence. Educational Technology Research and Development, 73(1), 115–133. https://doi.org/10.1007/s11423-024-10418-1

Ding, A.-C. E., Shi, L., Yang, H., & Choi, I. (2024). Enhancing teacher AI literacy and integration through different types of cases in teacher professional development. Computers and Education Open, 6, 100178. https://doi.org/10.1016/j.caeo.2024.100178

Fundi, M., Sanusi, I. T., Oyelere, S. S., & Ayere, M. (2024). Advancing AI education: Assessing Kenyan in-service teachers’ preparedness for integrating artificial intelligence in competence-based curriculum. Computers in Human Behavior Reports, 14, 100412. https://doi.org/10.1016/j.chbr.2024.100412

Galindo-Domínguez, H., Delgado, N., Campo, L., & Losada, D. (2024). Relationship between teachers’ digital competence and attitudes towards artificial intelligence in education. International Journal of Educational Research, 126, 102381. https://doi.org/10.1016/j.ijer.2024.102381

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd edition). Sage Publication, Inc.

Hair, 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. Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7