Self-Regulated Learning in Generative Artificial Intelligence-Assisted Science Learning: A Factor Structure Analysis

Fanny Rahmatina Rahim(1), Ari Widodo(2), Achmad Samsudin(3), Tina Hayati Dahlan(4), Elrisfa Magistarina(5),

DOI: https://doi.org/10.46843/jiecr.v7i4.3142


(1) Science Education Program, Universitas Pendidikan Indonesia, Indonesia; Department of Physics, Universitas Negeri Padang, Indonesia
(2) Science Education Program, Universitas Pendidikan Indonesia, Indonesia
(3) Physics Education Program, Universitas Pendidikan Indonesia, Indonesia
(4) Educational Psychology Program, Universitas Pendidikan Indonesia, Indonesia
(5) School of Psychology, University of Leeds, England; Department of Psychology, Universitas Negeri Padang, Indonesia

Abstract

This study examines students' self-regulated learning (SRL) in Generative Artificial Intelligence (GenAI)-assisted science learning. The purpose of this study was to identify the characteristics of students' SRL and examine the empirically established factor structure of SRL in a GenAI-assisted learning environment. This study employed a quantitative survey design involving 154 students. Data were analyzed using descriptive statistics, Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA). The results showed that students exhibited moderate to high levels of SRL, with the motivation dimension showing stronger performance compared to the metacognitive aspect. EFA identified six initial factors, while CFA confirmed the final five dimensions: metacognitive planning and goal setting, help-seeking and resource management, self-regulation difficulties, intrinsic motivation, and self-monitoring and evaluation. The measurement model demonstrated satisfactory validity, reliability, and model fit. Other findings also showed that science learning can support students' SRL development, but still requires structured support to strengthen metacognitive skills. This study contributes to the development of SRL measurement in GenAI-based science learning.

Keywords

confirmatory factor analysis; factor structure; generative artificial intelligence; science learning; self-regulated learning

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