Integrating Artificial Intelligence into Problem-Based Learning to Enhance Self-Regulated Learning - Nurinda R. Bakri; Abdul Haris Odja; Dewi Diana Paramata; Citron S. Payu; Mursalin Mursalin (Department of Mathematics and Science Education, Faculty of Teacher Training and Education, University of Mataram. Jurnal Pijar MIPA colaborates with Perkumpulan Pendidik IPA Indonesia Wilayah Nusa Tenggara Barat, 2026)
Technological advancements in the digital age have brought significant changes to the learning process; however, students’ self-regulated learning skills in science education still need to be improved, particularly in their ability to plan, monitor, and evaluate the learning process. Therefore, technology-based learning innovations such as Artificial Intelligence (AI) are needed to provide rapid and personalized feedback to support Self-Regulated Learning (SRL). This study aims to determine the effect of integrating Artificial Intelligence (AI) into the Problem-Based Learning (PBL) model on students’ Self-Regulated Learning (SRL) skills in science education. This study employs a quantitative approach with a one-group experimental design, involving 17 students in the experimental class and 10 students in the replication class, selected through purposive sampling. The research instrument consists of a Self-Regulated Learning questionnaire. Data analysis was conducted using descriptive statistics, the Shapiro-Wilk normality test, and a one-sample t-test for hypothesis testing. The results showed that the mean Self-Regulated Learning (SRL) scores of students in the experimental class were 90.12 and 92.40 in the replication class, both falling into the high category, and the data were normally distributed with significance values of 0.842 and 0.589, respectively (p > 0.05). The results of the hypothesis test showed a p-value of 0.000 for the experimental class.