The Integration of Generative Artificial Intelligence into the REACT Strategy to Enhance Self-Regulated Learning among Elementary School Teacher Candidates
Keywords:
Elementary Teacher Candidates, Generative Artificial Intilegence, REACT, Self-Regulated LearningAbstract
The low level of self-regulated learning among university students requires serious attention. This study aimed to determine the effect of the REACT strategy integrated with generative artificial intelligence (GenAI) on the self-regulated learning of prospective elementary school teachers. An experimental method was employed, specifically a true experimental post-test-only control group design. Random sampling was used to select a total of 45 participants, divided into two groups: an experimental group of 22 students and a control group of 23 students. Data were collected using a questionnaire based on six indicators. The results of the t-test showed a significance value of 0.042 (less than 0.05), indicating a statistically significant difference between the groups; the experimental group outperformed the control group, with a mean score of 75.9 compared to 73.0 (a three-point difference) and a t-value of 2.091. Thus, it can be concluded that there is a significant difference between the experimental and control groups. However, not all indicators of self-regulated learning showed improvement; the highest score was observed in the "learning planning" indicator, while the lowest was in the "metacognitive regulation" indicator. Consequently, while the REACT strategy integrated with GenAI offers a potential solution for enhancing students' self-regulated learning, the findings suggest a need for strengthening metacognitive skills, providing balanced scaffolding, and preventing student over-reliance on AI.
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