GENERATIVE AI AND METACOGNITION-BASED LEARNING STRATEGIES IN ISLAMIC EDUCATION TO ENHANCE STUDENTS' CRITICAL THINKING AND SELF-DIRECTED LEARNING

Objective: The utilization of generative artificial intelligence (GenAI) in Islamic Education presents opportunities to provide feedback, offer varied explanations, and stimulate dialogue. However, it concurrently poses risks of dependency, misinformation, and a reduction in students' cognitive effort. This article aims to formulate Islamic Education learning strategies that integrate GenAI and metacognition to foster critical thinking and self-directed learning. Method: The study employs a qualitative approach through library research, examining relevant books, journal articles, policy reports, and empirical findings. Data were analyzed through source identification, thematic coding, cross-source comparison, and conceptual synthesis. Results: The synthesis yielded four primary findings. First, GenAI is most appropriately positioned as a dialogic partner and comparative resource, rather than as a religious authority or a substitute for educators. Second, metacognitive cycles comprising planning, monitoring, and evaluation must be embedded before, during, and after interactions with AI. Third, critical thinking can be cultivated through source verification, rational analysis, perspective comparison, and argumentation grounded in theological evidence and context. Fourth, self-directed learning emerges when students establish goals, select strategies, document their learning processes, and reflect on outcomes responsibly. Novelty: The article concludes that the integration of GenAI and metacognition must be founded upon pedagogical objectives, AI literacy, digital ethics, the validation of Islamic sources, and authentic assessment. Its implementation necessitates institutional guidelines, teacher facilitation, data protection, and periodic evaluation.