E38 Teachers’ Perceptions of AI-Enhanced Data-Driven Decision-Making (DDDM) in Schools: A Systematic Review Proceedings of the 21st Chais Conference for the Study of Innovation and Learning Technologies: Learning in the Digital Era I. Blau, A. Caspi, Y. Eshet-Alkalai, N. Geri, Y. Kalman, D. Olenik-Shemesh, Y. Sidi, & N. Brandel (Eds.), Ra'anana, Israel: The Open University of Israel Teachers' Perceptions of AI-Enhanced Data-Driven DecisionMaking (DDDM) in Schools: A Systematic Review Shahaf Rocker Yoel The Open University of Israel rockershahaf@gmail.com Ayelet Becher The Open University of Israel ayeletbe@openu.ac.il תפיסותיהם של מורים את תהליכי קבלת החלטות מבוססת נתונים ) DDDM ( הנתמכים בבינה מלאכותית בבתי הספר : סקירה שיטתית של הספרות איילת בכר האוניברסיטה הפתוחה ayeletbe@openu.ac.il שחף רוקר יואל האוניברסיטה הפתוחה rockershahaf@gmail.com Abstract This systematic review examines teachers' perceptions of data-driven decision-making (DDDM) supported by artificial intelligence (AI) in K-12 schools. Guided by PRISMA standards, it synthesizes 25 empirical studies published between 2020 and 2025, identified through major databases and hand searches of leading journals. The analysis reveals both convergence and variation in how teachers interpret the role of AI in evidence-based practice. Positive perceptions highlight efficiency, personalization, and support for inclusive pedagogy when AI tools align with curricular goals, while negative perceptions focus on transparency, reliability, and threats to professional autonomy, reflecting ongoing ambivalence toward AI adoption. Teachers' experiences are shaped by systemic barriers, such as insufficient training, excessive workload, and infrastructural limitations, alongside enabling factors, including professional development, institutional support, and curricular integration. Ethical and emotional concerns, particularly around privacy, fairness, and professional identity, emerge as cross-cutting influences shaping trust and adoption. Contextual variations across geography, educational levels, and disciplines indicate that perceptions are deeply situated within local policies and cultures. Overall, teachers' perceptions of AIenhanced DDDM are multifaceted and context dependent, shaped by trust, ethics, and professional values. The review consolidates current evidence, notes quality and geographic limitations, and offers guidance for teacher-centered integration of AI into DDDM in schools. Keywords: Artificial Intelligence, Data-Driven Decision-Making, DDDM, Perceptions, Systematic Review, Teachers.
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