Descriptive Analysis of Self-Regulated Learning among Physics Education Students in AI-Assisted Learning Contexts
Keywords:
Artificial intelligence, Descriptive analysis, Physics education students, Self-regulated learningAbstract
Artificial intelligence (AI) is increasingly used in higher education to support learning activities, including information seeking, concept exploration, and problem-solving. Understanding students’ self-regulated learning (SRL) characteristics in AI-assisted learning contexts can provide insights into their reported learning practices and inform pedagogical design. This study aimed to describe SRL characteristics among Physics Education students in AI-assisted learning contexts. A quantitative descriptive survey was conducted involving 28 Physics Education students, using a saturated sampling technique. Data were collected through a 27-item questionnaire using a four-point Likert scale. Content validity was assessed by two experts using Gregory’s formula, yielding a coefficient of 0.90, indicating high content validity. Data were analyzed descriptively by calculating percentage scores across five SRL indicators: self-efficacy, initiative, responsibility, learning resources, and learning strategies. The results showed that all five indicators were classified as high according to the study’s interpretation criteria. Learning resources obtained the highest percentage (81.25%), followed by initiative (80.95%), self-efficacy (79.17%), responsibility (78.57%), and learning strategies (75.74%). These findings indicate relatively positive self-reported SRL characteristics across all five indicators, although differences were observed between them. The comparatively lower percentage for learning strategies suggests an opportunity to strengthen students’ strategy selection, progress monitoring, and reflection on learning processes. Pedagogically, students may benefit from structured activities that encourage critical evaluation of AI-generated information, verification against credible sources, and reflection on problem-solving strategies. However, the findings represent students’ self-reported responses and do not establish that AI use improves SRL. Further research using observational, longitudinal, or experimental designs is needed to examine students’ actual SRL practices and the relationship between AI use and SRL.
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