Beyond Translation: Can Human-AI Co-Regulation Improve Qira’ah al-Kutub Instruction?

Qira’ah al-Kutub instruction in higher education has long been dominated by translation-oriented practices that emphasize grammatical decoding rather than interpretive understanding, limiting students’ critical engagement with classical Arabic texts. In the context of increasing AI integration in education, it is important to develop pedagogical models that strengthen, rather than replace, human interpretive judgment. This study therefore aims to investigate and analyze how and to what extent human–AI co-regulation can enhance students’ comprehension and interpretation of classical Arabic texts in Qira’ah al-Kutub learning. Employing a research and development approach, the study implemented a co-regulated instructional design that integrates AI-assisted linguistic analysis with instructor mediation, peer negotiation, and reflective learning activities. Participants were 56 (fifty-six) undergraduate students enrolled in a Qira’ah al-Kutub course in an Islamic Education Program of Universitas Islam Indonesia. Data were collected through reading comprehension assessments, student response questionnaires, reflective writings, and analyzed using research and developments methods. The findings indicated that human–AI co-regulation facilitates a shift from literal translation strategies toward interpretive, contextual, and reflective reading practices. Rather than fostering cognitive dependency, AI functioned as a provisional analytical partner that enhanced students’ critical engagement when guided by pedagogical mediation. This study contributes to the field by demonstrating that the educational value of artificial intelligence in classical text learning lies in instructional design rather than technological capability alone. The proposed model offers a principled framework for responsible AI integration in humanities-based language education and provides practical implications for Qira’ah al-Kutub instruction in higher education.