The Influence of Curriculum Innovation Readiness and AI Literacy on Teachers’ Readiness to Implement an Adaptive Curriculum
##plugins.themes.bootstrap3.article.main##
Abstract
The rapid development of artificial intelligence (AI) and digital technologies is transforming education and increasing the need for adaptive curricula that respond to technological developments, diverse student needs, and changing competencies. Teachers play a central role in implementing curriculum innovation; therefore, curriculum innovation readiness and AI literacy may influence their readiness to implement an adaptive curriculum. This study aims to examine the influence of curriculum innovation readiness and AI literacy on teachers’ readiness to implement an adaptive curriculum. A quantitative explanatory design was employed involving 300 teachers selected through purposive sampling. Data were collected using a five-point Likert-scale questionnaire and analyzed using PLS-SEM with SmartPLS 4. The results showed that curriculum innovation readiness had a positive and significant influence on teachers’ readiness (β = 0.478; t = 7.214; p < 0.001). AI literacy also had a positive and significant influence (β = 0.391; t = 5.846; p < 0.001). The model produced an R² of 0.602, indicating that both predictors explained 60.2% of the variance in teachers’ readiness. The findings highlight the importance of integrating curriculum innovation training and AI literacy into teacher professional development and adaptive curriculum policies.
##plugins.themes.bootstrap3.article.details##
How to Cite
Sumeyye, L., Erenay, E., & Cihanbey, C. (2025). The Influence of Curriculum Innovation Readiness and AI Literacy on Teachers’ Readiness to Implement an Adaptive Curriculum. Journal of Education Innovation and Curriculum Development, 3(3), 144–156. Retrieved from https://journals.iarn.or.id/index.php/educur/article/view/776
References
Adams Becker, S., Cummins, M., Davis, A., Freeman, A., Hall Giesinger, C., & Ananthanarayanan, V. (2017). NMC Horizon Report: 2017 Higher Education Edition. New Media Consortium.
Ahn, M. J., & Chen, Y.-C. (2022). Digital transformation toward AI-augmented public administration: The perception of government employees and the willingness to use AI in government. Government Information Quarterly, 39(2), 101664.
Alarcón, D., Sánchez, J. A., & De Olavide, U. (2015). Assessing convergent and discriminant validity in the ADHD-R IV rating scale: User-written commands for Average Variance Extracted (AVE), Composite Reliability (CR), and Heterotrait-Monotrait ratio of correlations (HTMT). Spanish STATA Meeting, 39, 1–39.
Alsobhi, M., Sachdev, H. S., Chevidikunnan, M. F., Basuodan, R., KU, D. K., & Khan, F. (2022). Facilitators and barriers of artificial intelligence applications in rehabilitation: a mixed-method approach. International Journal of Environmental Research and Public Health, 19(23), 15919.
Balota, D. A., Yap, M. J., Hutchison, K. A., Cortese, M. J., Kessler, B., Loftis, B., Neely, J. H., Nelson, D. L., Simpson, G. B., & Treiman, R. (2007). The English lexicon project. Behavior Research Methods, 39(3), 445–459.
Byker, E. J., Michael Putman, S., Polly, D., & Handler, L. (2018). Examining elementary education teachers and preservice teachers’ self-efficacy related to technological pedagogical and content knowledge (TPACK). In Self-efficacy in instructional technology contexts (pp. 119–140). Springer.
Chiu, T. K. F., Meng, H., Chai, C.-S., King, I., Wong, S., & Yam, Y. (2021). Creation and evaluation of a pretertiary artificial intelligence (AI) curriculum. IEEE Transactions on Education, 65(1), 30–39.
Chounta, I.-A., Bardone, E., Raudsep, A., & Pedaste, M. (2022). Exploring teachers’ perceptions of artificial intelligence as a tool to support their practice in Estonian K-12 education. International Journal of Artificial Intelligence in Education, 32(3), 725–755.
Davis, K. S. (2003). “Change is hard”: What science teachers are telling us about reform and teacher learning of innovative practices. Science Education, 87(1), 3–30.
Dostál, J., Wang, X., Nuangchalerm, P., Brosch, A., & Steingartner, W. (2017). Researching computing teachers’ attitudes towards changes in the curriculum content—An innovative approach or resistance? 2017 Second International Conference on Informatics and Computing (ICIC), 1–6.
Gouëdard, P., Pont, B., Hyttinen, S., & Huang, P. (2020). Curriculum reform: A literature review to support effective implementation.
Gupta, S. (2017). Ethical issues in designing internet-based research: recommendations for good practice. Journal of Research Practice, 13(2), D1.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.
Hajjar, S. T. (2018). Statistical analysis: Internal-consistency reliability and construct validity. International Journal of Quantitative and Qualitative Research Methods, 6(1), 27–38.
Hoyt, J., Huq, F., & Kreiser, P. (2007). Measuring organizational responsiveness: the development of a validated survey instrument. Management Decision, 45(10), 1573–1594.
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.
Lameras, P., & Arnab, S. (2021). Power to the teachers: An exploratory review on artificial intelligence in education. Information, 13(1), 14.
Meria, L., Prastyani, D., & Dudhat, A. (2022). The role of transformational leadership and self-efficacy on readiness to change through work engagement. Aptisi Transactions on Technopreneurship (ATT), 4(1), 77–88.
Nayak, J. (2008). Factors influencing stress and coping strategies among the degree college teachers of Dharwad City, Karnataka. Master of Home Science in Family Resource Management.
Pearse, N. (2011). Deciding on the scale granularity of response categories of Likert type scales: The case of a 21-point scale. Electronic Journal of Business Research Methods, 9(2), 159–171.
Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development.
Penuel, W. R., Fishman, B. J., Yamaguchi, R., & Gallagher, L. P. (2007). What makes professional development effective? Strategies that foster curriculum implementation. American Educational Research Journal, 44(4), 921–958.
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In Handbook of market research (pp. 587–632). Springer.
Schleicher, A. (2015). Schools for 21st-century learners: Strong leaders, confident teachers, innovative approaches. International Summit on the Teaching Profession.
Seniwoliba, J. A. (2013). Teacher motivation and job satisfaction in senior high schools in the Tamale metropolis of Ghana.
Sing, C. C., Teo, T., Huang, F., Chiu, T. K. F., & Xing Wei, W. (2022). Secondary school students’ intentions to learn AI: Testing moderation effects of readiness, social good and optimism. Educational Technology Research and Development, 70(3), 765–782.
Yusif, S., Hafeez-Baig, A., Soar, J., & Teik, D. O. L. (2020). PLS-SEM path analysis to determine the predictive relevance of e-Health readiness assessment model. Health and Technology, 10(6), 1497–1513.
Zhao, L., Wu, X., & Luo, H. (2022). Developing AI literacy for primary and middle school teachers in China: Based on a structural equation modeling analysis. Sustainability, 14(21), 14549.
Ahn, M. J., & Chen, Y.-C. (2022). Digital transformation toward AI-augmented public administration: The perception of government employees and the willingness to use AI in government. Government Information Quarterly, 39(2), 101664.
Alarcón, D., Sánchez, J. A., & De Olavide, U. (2015). Assessing convergent and discriminant validity in the ADHD-R IV rating scale: User-written commands for Average Variance Extracted (AVE), Composite Reliability (CR), and Heterotrait-Monotrait ratio of correlations (HTMT). Spanish STATA Meeting, 39, 1–39.
Alsobhi, M., Sachdev, H. S., Chevidikunnan, M. F., Basuodan, R., KU, D. K., & Khan, F. (2022). Facilitators and barriers of artificial intelligence applications in rehabilitation: a mixed-method approach. International Journal of Environmental Research and Public Health, 19(23), 15919.
Balota, D. A., Yap, M. J., Hutchison, K. A., Cortese, M. J., Kessler, B., Loftis, B., Neely, J. H., Nelson, D. L., Simpson, G. B., & Treiman, R. (2007). The English lexicon project. Behavior Research Methods, 39(3), 445–459.
Byker, E. J., Michael Putman, S., Polly, D., & Handler, L. (2018). Examining elementary education teachers and preservice teachers’ self-efficacy related to technological pedagogical and content knowledge (TPACK). In Self-efficacy in instructional technology contexts (pp. 119–140). Springer.
Chiu, T. K. F., Meng, H., Chai, C.-S., King, I., Wong, S., & Yam, Y. (2021). Creation and evaluation of a pretertiary artificial intelligence (AI) curriculum. IEEE Transactions on Education, 65(1), 30–39.
Chounta, I.-A., Bardone, E., Raudsep, A., & Pedaste, M. (2022). Exploring teachers’ perceptions of artificial intelligence as a tool to support their practice in Estonian K-12 education. International Journal of Artificial Intelligence in Education, 32(3), 725–755.
Davis, K. S. (2003). “Change is hard”: What science teachers are telling us about reform and teacher learning of innovative practices. Science Education, 87(1), 3–30.
Dostál, J., Wang, X., Nuangchalerm, P., Brosch, A., & Steingartner, W. (2017). Researching computing teachers’ attitudes towards changes in the curriculum content—An innovative approach or resistance? 2017 Second International Conference on Informatics and Computing (ICIC), 1–6.
Gouëdard, P., Pont, B., Hyttinen, S., & Huang, P. (2020). Curriculum reform: A literature review to support effective implementation.
Gupta, S. (2017). Ethical issues in designing internet-based research: recommendations for good practice. Journal of Research Practice, 13(2), D1.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.
Hajjar, S. T. (2018). Statistical analysis: Internal-consistency reliability and construct validity. International Journal of Quantitative and Qualitative Research Methods, 6(1), 27–38.
Hoyt, J., Huq, F., & Kreiser, P. (2007). Measuring organizational responsiveness: the development of a validated survey instrument. Management Decision, 45(10), 1573–1594.
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.
Lameras, P., & Arnab, S. (2021). Power to the teachers: An exploratory review on artificial intelligence in education. Information, 13(1), 14.
Meria, L., Prastyani, D., & Dudhat, A. (2022). The role of transformational leadership and self-efficacy on readiness to change through work engagement. Aptisi Transactions on Technopreneurship (ATT), 4(1), 77–88.
Nayak, J. (2008). Factors influencing stress and coping strategies among the degree college teachers of Dharwad City, Karnataka. Master of Home Science in Family Resource Management.
Pearse, N. (2011). Deciding on the scale granularity of response categories of Likert type scales: The case of a 21-point scale. Electronic Journal of Business Research Methods, 9(2), 159–171.
Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development.
Penuel, W. R., Fishman, B. J., Yamaguchi, R., & Gallagher, L. P. (2007). What makes professional development effective? Strategies that foster curriculum implementation. American Educational Research Journal, 44(4), 921–958.
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In Handbook of market research (pp. 587–632). Springer.
Schleicher, A. (2015). Schools for 21st-century learners: Strong leaders, confident teachers, innovative approaches. International Summit on the Teaching Profession.
Seniwoliba, J. A. (2013). Teacher motivation and job satisfaction in senior high schools in the Tamale metropolis of Ghana.
Sing, C. C., Teo, T., Huang, F., Chiu, T. K. F., & Xing Wei, W. (2022). Secondary school students’ intentions to learn AI: Testing moderation effects of readiness, social good and optimism. Educational Technology Research and Development, 70(3), 765–782.
Yusif, S., Hafeez-Baig, A., Soar, J., & Teik, D. O. L. (2020). PLS-SEM path analysis to determine the predictive relevance of e-Health readiness assessment model. Health and Technology, 10(6), 1497–1513.
Zhao, L., Wu, X., & Luo, H. (2022). Developing AI literacy for primary and middle school teachers in China: Based on a structural equation modeling analysis. Sustainability, 14(21), 14549.
Copyright and Licensing
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.