ע 281 ש חף רוקר יואל , איילת בכר ספר הכנס העשרים ואחד לחקר חדשנות וטכנולוגיות למידה ע"ש צ'ייס: האדם הלומד בעידן הדיגיטלי א' בלאו, ד' אולניק - שמש, נ' גרי, א' כספי, י' סידי, י' עשת - אלקלעי, י' קלמן ו נ' ברנדל )עורכים(, רעננה: האוניברסיטה הפתוחה הגורמים התומכים והמעכבים בהטמעת כלים מבוססי בינה מלאכותית עבור קבלת החלטות מבוססת נתונים בבתי הספר )פוסטר( איילת בכר האוניברסיטה הפתוחה ayeletbe@openu.ac.il שחף רוקר יואל האוניברסיטה הפתוחה rockershahaf@gmail.com Enabling and Constraining Factors in Implementing AI-Enhanced Data-Driven Decision-Making Tools in Schools (Poster) Shahaf Rocker Yoel The Open University of Israel rockershahaf@gmail.com Ayelet Becher The Open University of Israel ayeletbe@openu.ac.il Abstract Data-Driven Decision Making (DDDM) has become a central component in contemporary educational practice, alongside the growing integration of data-information and Artificial Intelligence (AI)-based tools designed to support teaching, learning, and assessment processes. These tools offer the potential to provide educators with timely insights into students’ learning trajectories, resilience, and self-regulation. However, research continues to show a persistent gap between the potential of these tools and their actual use in schools. The aim of this study is to identify the organizational, cultural, and individual factors that enable or constrain the implementation of AI-based data tools in schools. The study adopted a mixed methods design with an emphasis on qualitative data, conducted across three professional development contexts, using semi-structured interviews, focus groups, classroom and community observations, and teacher and principal surveys. Data analysis combined thematic analysis of qualitative findings with descriptive analysis of survey data. The findings suggest that meaningful implementation of data-driven decision making depends on the interaction between three key conditions: (1) Supportive organizational infrastructure, including leadership, structured routines, and access to unified data tools; (2) Interpretive professional discourse within teacher communities, where data are used as a basis for collaborative inquiry rather than accountability; and (3) Development of teacher agency and professional ownership, which fosters confidence and capacity to translate data into instructional action. The study highlights the need for sustained professional learning and ongoing pedagogical support, rather than relying solely on the provision of technological tools. Keywords: Artificial Intelligence (AI), Data-Based Tools, Data-Driven Decision Making (DDDM), Teacher Communities, Technological Tool Implementation.
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