Barriers and Strategies for Implementing AI-Based Personalized Learning in Project-Based Learning: A Systematic Literature Review - Hendra Tan; Budi Wibawanta; Khoe Yao Tung (Jurnal Edusci, 2026)
Background. Artificial intelligence (AI), particularly large language models (LLMs), has emerged as a transformative technology in education, offering new possibilities for student-centered pedagogies such as project-based learning (PBL). Methods. This systematic literature review examines the integration of AI in PBL across K‑12 and higher education contexts, focusing on its benefits, challenges, and implementation strategies. Following the PRISMA 2020 guidelines, 23 empirical studies published between 2020 and 2026 were analyzed from the Scopus and Web of Science databases. Result. Key findings include: (a) AI enhances PBL through personalized learning pathways, automated scaffolding, real-time feedback, and collaborative prompting; (b) challenges persist in data privacy, over-dependence on AI, output accuracy, and teacher readiness; and (c) successful strategies emphasize co-design, ethical frameworks, and gradual personalization. Conclusion. This review provides actionable insights for educators, policymakers, and technology developers to design effective and responsible AI‑supported PBL environments.