E46 Teachers’ Perceptions of AI-Enhanced Data-Driven Decision-Making (DDDM) in Schools: A Systematic Review Findings suggest that context matters. Studies from the U.S. and China emphasize ethical and regulatory issues, while those from Saudi Arabia and Nigeria highlight pedagogical opportunities (Zawacki-Richter et al., 2019). Secondary teachers emphasize autonomy and workload, whereas primary and special education teachers highlight training needs. Leadership practices that foster dialogue and modeling (Marsh & Farrell, 2014) are crucial but must be adopted when AI mediates decision-making. Teachers' engagement with AI is shaped by cultural, institutional, and disciplinary conditions. Across studies, a unifying tension emerges as teachers balance enthusiasm for datainformed improvement with preservation of autonomy and trust. They act not as passive adopters but as interpreters mediating between algorithmic insights and pedagogical values. This perspective bridges AI and DDDM research, positioning teachers' perceptions as the key determinant of whether AI-generated data lead to pedagogical impact. Limitations include the small, uneven corpus of 25 studies and the geographic concentration. Future research should include underrepresented regions, employ longitudinal methods, and explore intersections of ethics, emotion, and discipline. Strengthening professional development and institutional ecosystems remains essential for responsible, teacher-centered integration of AI into DDDM. Conclusions This systematic review concludes that the success of AI-enhanced data-driven decision-making (DDDM) depends less on technological sophistication and more on teachers' professional judgment, trust, and ethical engagement. Strengthening collaborative inquiry, data literacy, and reflective professional development can transform AI-supported DDDM from a technical innovation into a sustainable, teacher-centered practice that empowers evidence-informed decision-making in schools. References References marked with an asterisk (*) indicate studies included in the review. *Abdelazim, A., Al Breiki, M., & Khlaif, Z. N. (2025). AI in education: The mediating role of perceived trust in adoption decisions of school leaders. Education and Information Technologies, 1-33. https://doi.org/10.1007/s10639-025-13596-4 Alonzo, A. C., Cramer, E. D., & Schifter, C. C. (2024). Supporting teachers' data use through ICT-based systems: Challenges and opportunities. Educational Technology Research and Development, 72(3), 455-473. https://doi.org/10.54337/nlc.v7.9210 *Alsudairy, N. A. & Eltantawy, M. M. (2024). Special education teachers' perceptions of using artificial intelligence in educating students with disabilities. Journal of Intellectual Disability-Diagnosis and Treatment, 12(2), 92-102. https://doi.org/10.6000/2292-2598.2024.12.02.5 *Alvarez-Garcia, M., Arenas-Parra, M., & Ibar-Alonso, R. (2024). Uncovering student profiles. An explainable cluster analysis approach to PISA 2022. Computers & Education, 223, 105166. https://doi.org/10.1016/j.compedu.2024.105166 Arantes, J. A. (2023). Teachers' autonomy and the challenges of artificial intelligence in education. Computers & Education, 203, 104875. https://doi.org/10.1016/j.compedu.2023.104875 Biesta, G. (2015). Good education in an age of measurement: Ethics, politics, democracy. Routledge. https://doi.org/10.4324/9781315634319 *Bower, M., Torrington, J., Lai, J. W., Petocz, P., & Alfano, M. (2024). How should we change teaching and assessment in response to increasingly powerful generative artificial intelligence? Outcomes of the ChatGPT teacher survey. Education and Information Technologies, 29(12), 15403-15439. https://doi.org/10.1007/s10639-023-12405-0
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