116 ע כאשר לומדים מדברים עם AI : אינטראקטיביות כזרז להבניית ידע )מאמר קצר( 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 כאשר לומדים מדברים עם AI : אינטראקטיביות כזרז להבניית ידע )מאמר קצר( תמר שמיר - ענבל האוניברסיטה הפתוחה tamaris@openu.ac.il אינה בלאו האוניברסיטה הפתוחה inabl@openu.ac.il זיו ארזי האוניברסיטה הפתוחה zivara@gedu.openu.ac.il When Learners Talk with AI: Interactivity as a Catalyst for Knowledge Construction (short paper) Abstract Generative artificial intelligence (GenAI) enables new forms of human-computer interaction, facilitated by online discourse. This development allows online users to use GenAI as a Personal assistant and a 'mediator', utilizing cognitive prompts to engage in highly interactive conversations. This process supports knowledge construction in line with Socio-constructivism, helping learners reach their potential development as defined by Vygotsky's Zone of Proximal Development – ZPD (Vygotsky, 1978). This study compares low interactivity technology levels (e.g., traditional search engines such as Google Search) with high interactivity technology levels (e.g., GenAI tools such as ChatGPT). The aim is to understand the cognitive, emotional, and social changes that occur during knowledge construction using these platforms, focusing on intrinsic motivation, self-efficacy, perceived learning in cognitive, emotional, and social dimensions and learning achievements. The study employs a mixed method approach. Thirty eight participants aged 18-70 are randomly assigned to either the experimental group (ChatGPT) or the control group (Google Search). Both groups will complete identical knowledge construction tasks, followed by a knowledge test based on the task. Afterwards, participants complete self-report questionnaires to assess their levels of intrinsic motivation, self-efficacy, and perceived learning. For the qualitative part, we conducted a thematic analysis of the guided observation data to gain deeper insights into participants' perspectives, feelings, and experiences while constructing knowledge with both technologies. Keywords: Generative Artificial Intelligence – GenAI; Socio-constructivism; Selfefficacy; Intrinsic motivation; Cognitive, Social and Emotional Perceived learning; Learning Achievements. Ziv Arazi The Open University of Israel zivara@gedu.openu.ac.il Ina Blau The Open University of Israel inabl@openu.ac.il Tamar Shamir-Inbal The Open University of Israel tamaris@openu.ac.il
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