Abstract
Learning experiences are usually intimidated by various factors that can impact students’ learning. The factors are associated with the learning environment, including physical, pedagogical, and psychosocial dimensions. Apparently, traditional learning experience design often expected students to adjust and uplift themselves to the expectations of those who design them (i.e., teachers and instructional designers). This approach has led to mismatches, disappointment, and learning gaps among students. Apparently, learning design can be seriously tampered by students’ “Swiss cheese gaps,” motivation, psychological, and other factors. In flipped learning, students may feel less confident and insecure when they are on their journey to learn new concepts alone in pre-classes in their individual space. The current study is set out to examine this notion by asking the following research question: “What is the impact of using an AI model in a flipped learning approach on students’ self-efficacy in their learning?” The study utilized a Quasi-experimental research approach, as 120 students at the Faculty of Educational Sciences, at Hashemite University, Jordan, were selected. Students were divided into two groups: the experimental group, which was taught using flipped learning aided by a Large Language Model (LLM), while the control group was taught using the traditional flipped learning approach, where they were asked to watch short instructional videos before they attended the class. A 12-item self-efficacy scale was developed and used. The results of the study show that students in the experimental group were more efficient and effective in their study than students in the control group.
Presenters
Atef AbuHmaidAssociate Professor, Department of Curricula and Instruction, The Hashemite University, Az Zarqa', Jordan
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
Artificial Intelligence,Large Language Models,self-efficacy,flipped learning
