Systematic Literature Review: Urgency of AI-Integrated Chemistry Learning Models to Improve Students’ Self-Regulated Learning - Bintang Ahmad; Eda Lolo Allo; Dewiyanti Fadly (Universitas Lampung, 2026)
Systematic Literature Review: Urgency of AI_Integrated Chemistry Learning Models to Improve Students’s Self Regulated Learning. Objectives: This study aims to analyze the urgency of developing an AI-integrated chemistry learning model as a learning assistant to enhance students’ self-regulated learning. Method: The study employed a Systematic Literature Review (SLR) method based on the PRISMA framework. The search was conducted via Google Scholar for the years 2019–2026, yielding 50 articles, with 12 meeting the inclusion criteria for analysis. Findings: The results indicate that integrating AI into chemistry learning can boost motivation, engagement, and critical thinking, as well as help students grasp abstract chemical concepts. AI also supports more personalized, adaptive, and self-directed learning, thereby contributing to the enhancement of self-regulated learning. However, its implementation still faces challenges related to AI literacy, teacher readiness, and technological infrastructure. Conclusion: Therefore, the development of AI-integrated chemistry learning models is a critical need to support more effective, interactive, and self-directed learning.