Reconstructing the Affective Assessment Model in Islamic Religious Education in the Era of Generative Artificial Intelligence

Abstract

                                             Abstract

The rapid development of Generative Artificial Intelligence (AI) has transformed educational practices, including learning assessment in Islamic Religious Education. While AI offers opportunities to improve instructional efficiency and assessment design, its widespread use also raises concerns regarding the authenticity of affective assessment and the maintenance of academic integrity. This study aims to reconstruct an affective assessment model for Islamic Religious Education that is responsive to the challenges posed by Generative AI while preserving authentic character assessment. This study employed a qualitative research design using a content analysis approach. Data were derived from scholarly journal articles, books, policy documents, and other relevant academic literature, which were systematically analyzed through data reduction, thematic categorization, interpretation, and conceptual synthesis. The findings indicate that an effective affective assessment model should integrate AI-assisted assessment with authentic teacher-based assessment through direct observation, reflective discussions, open-ended questioning, self-assessment, and peer assessment. Based on these findings, this study proposes a hybrid affective assessment model in which Generative AI functions as an assistive technology for developing assessment instruments and managing data, whereas teachers remain the primary evaluators responsible for validating, interpreting, and providing character-based feedback. This model contributes conceptually to the development of affective assessment in Islamic Religious Education by balancing technological innovation with the principles of authenticity and academic integrity in the era of Generative AI.