<![CDATA[The rapid development of generative artificial intelligence (GenAI) has created new opportunities and challenges for Islamic Religious Education (IRE) at the senior secondary school level. This study aimed to analyze the development of GenAI in education, map its applications in the planning, implementation, and assessment of IRE learning, identify its pedagogical, epistemic-religious, and ethical risks, and formulate principles for responsible use. An integrative literature review was conducted by searching Google Scholar, GARUDA, the Directory of Open Access Journals, Scopus, and ERIC in June–July 2025. Publications issued between 2020 and 2025 were selected based on their relevance, full-text availability, bibliographic clarity, and discussion of the uses, benefits, risks, or governance of AI in education. A total of 24 sources were included, comprising 16 journal articles as the primary corpus and eight academic books or policy and theoretical documents as supporting sources. Data were extracted into an analytical matrix and synthesized thematically by distinguishing direct evidence from IRE learning in senior secondary schools from indirect evidence derived from other educational contexts. The findings indicate that GenAI is primarily used to support instructional material development, lesson planning, alternative explanations, adaptive learning, question generation, rubric development, and preliminary feedback. However, direct empirical evidence demonstrating improvements in IRE learning outcomes remains limited. Major risks include religious misinformation and hallucination, algorithmic bias, learner dependency, academic integrity violations, privacy concerns, unequal access, and reduced independent thinking. The study concludes that GenAI should function as a teacher-supervised instructional aid rather than a substitute for pedagogical authority and religious guidance. These findings imply that schools should develop AI literacy, religious-source verification protocols, authentic assessment practices, data-protection measures, and transparent institutional guidelines to ensure that GenAI supports meaningful, ethical, and value-oriented IRE learning.]]>
