Fostering AI Integration in Public Education: Leadership Support, Teacher Readiness, and the Development of a Contextualized Support System
DOI:
https://doi.org/10.5281/zenodo.21682485Keywords:
artificial intelligence, leadership support, teacher readiness, public education, digital transformation, support systemAbstract
This study examined the relationship between teachers perceived leadership support and their readiness to integrate artificial intelligence (AI) into instructional practices and used the findings to develop a contextualized support system. A descriptive-correlational, cross-sectional survey design was employed among 317 public secondary school teachers in the Division of Makati during School Year 2025–2026. Stratified random sampling was applied, and data were collected through a researcher-made, expert-validated questionnaire with excellent internal consistency (Cronbach’s α = .91). Weighted means, grand means, and Pearson product-moment correlation were used. Perceived leadership support was Moderate (M = 2.70), with resource allocation and encouragement for innovation obtaining the highest dimension means (M = 2.72 each). Teacher AI readiness was also Moderate (M = 2.70), led by attitude and willingness (M = 2.72). The available source correlation table reported 19 positive and significant pairwise relationships between leadership-support and readiness dimensions (r = .664–.734, p < .001), with professional development and AI literacy showing the strongest association (r = .734). A six-component Leadership Support System for AI-Ready Instruction was proposed, with an estimated annual budget of ₱550,000. The system was rated Highly Acceptable (M = 4.56), and practical applicability received the highest rating (M = 4.62). The findings indicate that teacher AI readiness develops most effectively when school leaders provide coherent vision, enabling policies, job-embedded professional learning, equitable resources, ethical safeguards, and structured opportunities for innovation.
Downloads
References
Bass, B. M., & Riggio, R. E. (2021). Transformational leadership (3rd ed.). Routledge.
Congress of the Philippines. (2012). Republic Act No. 10173: Data Privacy Act of 2012. Official Gazette.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approach (5th ed.). SAGE.
Department of Education (Philippines). (2012). DepEd Order No. 40, s. 2012: Child Protection Policy.
Department of Education (Philippines). (2022). DepEd Order No. 29, s. 2022: Adoption of the Basic Education Development Plan (BEDP) 2030.
Education Endowment Foundation. (2022). Putting evidence to work: A school’s guide to implementation (Updated ed.).
European Commission. (2022). Ethical guidelines on the use of AI and data in teaching and learning for educators. Publications Office of the European Union.
Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). SAGE.
Fowler, F. J. (2014). Survey research methods (5th ed.). SAGE.
Hallinger, P. (2011). Leadership for learning: Lessons from 40 years of empirical research. Journal of Educational Administration, 49(2), 125–142.
Hwang, G.-J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers & Education: Artificial Intelligence, 1, 100001.
International Society for Technology in Education. (2019). ISTE Standards for Education Leaders. ISTE.
Learning Forward. (2022). Standards for professional learning. Learning Forward.
National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). U.S. Department of Commerce.
Ng, W. (2022). Building K–12 AI literacy: A framework for schools and teachers. British Journal of Educational Technology, 53(6), 1585–1603.
Ng, W. (2023). Teacher readiness for AI-enabled learning environments: Opportunities and challenges. Computers & Education, 195, 104695.
Organisation for Economic Co-operation and Development. (2023). Artificial intelligence in education: Challenges, opportunities and policy responses. OECD Publishing.
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763–1768.
Spillane, J. P. (2006). Distributed leadership. Jossey-Bass.
Trust, T., & Prestridge, S. (2022). The role of the edtech coach in teacher technology integration. British Journal of Educational Technology, 53(2), 331–348.
UNESCO. (2021). Recommendation on the ethics of artificial intelligence. UNESCO.
UNESCO. (2023). Guidance for generative AI in education and research. UNESCO.
UNICEF. (2021). Policy guidance on AI for children. UNICEF.
United States Department of Education, Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. U.S. Department of Education.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.
World Bank. (2024). EdTech Readiness Index 2.0: Diagnostics for effective technology use in education. World Bank.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16, 39.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
