ע 255 דקלה חן - סער, יעל סידי, עדי ברן 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 השפעות השימוש בבינה מלאכותית יוצרת לצו רכי תכנון הוראה על תהליכים מטה - קוגניטיביים בקרב מורים )פוסטר( עדי ברן האוניברסיטה הפתוחה adibr@openu.ac.il יעל סידי האוניברסיטה הפתוחה yaelsi@openu.ac.il דקלה חן - סער האוניברסיטה הפתוחה diklasaar@gmail.com The Effects of Using Generative Artificial Intelligence for Instructional Planning on Teachers' Metacognitive Processes (Poster) Dikla Chen-Saar The Open University of Israel diklasaar@gmail.com Yael Sidi The Open University of Israel yaelsi@openu.ac.il Adi Brann The Open University of Israel adibr@openu.ac.il Abstract Ill-structured tasks are characterized by uncertainty, multiple possible solutions, and difficulties in evaluating outcomes (Graf-Drasch et al., 2021). In education, one important ill-structured task is lesson planning design. A lesson plan serves as a “road map” that guides teachers in organizing instruction, engaging learners, and assessing outcomes (Heidari et al., 2015), while balancing learners' needs, curriculum standards, teaching methods, and available technologies (Koehler et al., 2014). During lesson planning, teachers assess their progress toward pedagogical and professional goals, engaging metacognitive processes. Metacognition encompasses the monitoring and regulation of cognitive processes (Nelson & Narens, 1990). Accurate monitoring is essential for effective strategic planning. Yet, it is prone to bias, especially when learning takes place in a digital medium, which implies shallow information processing (Salmerón et al., 2024; Sidi et al., 2017). The growing use of generative artificial intelligence (GenAI) for lesson-plan design among teachers (Gonçalves Costa et al., 2024) highlights the need to examine its effects, given evidence that it may influence monitoring processes in similar ways (Fan et al., 2025). Based on existing literature (Fan et al., 2025), we hypothesize that GenAI use will impair monitoring accuracy, leading to overconfidence despite modest performance gains. The current study examines lesson-plan design as an illstructured task and investigates how GenAI use influences monitoring accuracy among 90 teachers. Participants, randomly assigned to GenAI-assisted or control conditions, evaluate a lesson plan using a rubric and rate their confidence. Their performance is assessed by experts and compared with confidence ratings. Initial findings are discussed. Keywords: Generative Artificial Intelligence (GenAI), Ill-structured task, Metacognition, Metacognitive monitoring, Lesson planning.
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