Artificial Intelligence-Supported Personalized Learning for Enhancing Students' Critical Thinking Skills: A Systematic Literature Review
DOI:
https://doi.org/10.67467/jliet.v1i1.109Keywords:
Artificial Intelligence, Personalized Learning, Critical Thinking Skills, Systematic Literature Review, Artificial Intelligence in EducationAbstract
The rapid advancement of Artificial Intelligence (AI) has transformed education toward more personalized, adaptive, and learner-centered approaches. Although numerous studies have reported the benefits of AI in improving learning quality, comprehensive evidence explaining the relationship between AI, personalized learning, and students' critical thinking skills remains limited. This study aims to examine the implementation of AI in personalized learning, identify its opportunities and challenges, and propose a conceptual model explaining how AI contributes to the development of students' critical thinking skills. This research employed a Systematic Literature Review (SLR) following the PRISMA 2020 guidelines. Literature was retrieved from five international databases, including Scopus, Web of Science, ERIC, IEEE Xplore, and Google Scholar. Following the identification, screening, and eligibility processes, 42 articles published between 2021 and 2025 were selected and analyzed using a thematic synthesis approach. The findings indicate that AI functions as an enabling technology supporting personalized learning through adaptive learning systems, learning analytics, intelligent tutoring systems, and Generative Artificial Intelligence. The contribution of AI to students' critical thinking is not direct but is mediated by enhanced student engagement, self-regulated learning, and metacognitive awareness. However, the implementation of AI also faces significant challenges related to teachers' pedagogical readiness, AI literacy, digital infrastructure, data privacy, algorithmic bias, and ethical governance. This study proposes a conceptual model integrating Artificial Intelligence, personalized learning, student engagement, self-regulated learning, and critical thinking skills, providing a theoretical contribution to the field of Artificial Intelligence in Education (AIEd) while offering practical implications for educators, policymakers, and future research.
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