ע 101 דפנה אבידוב , אורית עזרא , גיא כהן , ענת כהן , אלה ברונשטיין 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 אינטראקציית אדם - מכונה בפיתוח כלי להערכת ידע של מורים על הכוונה עצמית בלמידה בעת תהליך פתרון בעיות )מאמר קצר( גיא כהן אוניברסיטת תל אביב guycohen@mail.tau.ac.il אורית עזרא אוניברסיטת תל אביב oezra1@gmail.com דפנה אבידוב אוניברסיטת תל אביב dafnavidav@mail.tau.ac.il אלה ברונשטיין אוניברסיטת תל אביב allab@tauex.tau.ac.il ענת כהן אוניברסיטת תל אביב anatco@tauex.tau.ac.il Human-Machine Partnership in Building Assessment Tools for Teachers' Knowledge of Self-Regulated Learning during Problem Solving (Short paper) Dafna Avidov Tel Aviv University dafnavidav@mail.tau.ac.il Orit Ezra Tel Aviv University oezra1@gmail.com Guy Cohen Tel Aviv University guycohen@mail.tau.ac.il Anat Cohen Tel Aviv University anatco@tauex.tau.ac.il Alla Bronshtein Tel Aviv University allab@tauex.tau.ac.il Abstract Educators see great importance in promoting self-regulated learning (SRL) and problem solving (PS) among learners. Teachers are a key factor in promoting SRL-PS in the classroom, therefore diversifying the tools for assessing teachers in this area is needed. Furthermore, the Situational Judgment Test (SJT) is a potential tool for diagnosing teachers' knowledge. However, such a tool is currently lacking in the field of SRL-PS. Human interaction with generative artificial intelligence (GenAI) allows overcoming difficulties and complementing each other. The aim of this article is to present an initial attempt to develop an SJT tool for assessing teachers' knowledge in SRL-PS using the assistance of ChatGPT. This human-machine interaction led to the formulation of 15 difficulty categories and 20 scenarios that form the basis of the SJT tool for SRL-PS. It was found that scenarios created by the researchers can be complemented by those created by ChatGPT. In some cases, the scenarios from both sources are quite similar, while in others, those formulated by ChatGPT expand or present an alternative perspective on the difficulty. Furthermore, ChatGPT suggested new scenarios in some cases. A significant output of the study is a map that allows for analysis of the scenario pool and identification of over – or under-represented difficulty categories. The study advances SRL knowledge evaluation tools through SJT methodology while demonstrating the benefits of human-GenAI collaborative interactions. Keywords: Human-machine partnership, Generative Artificial intelligence, Situational Judgment Test, Self-Regulated Learning, Problem-Solving.
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