Data-Driven Constructivism: A Pedagogical Framework for Integrating Deep Learning into Arabic Second Language Acquisition - Mohammad Makinuddin; Muhammad A'inul Haq; Muhammad Farih; Abdul Ghani Md Din (Izdihar : Journal of Arabic Language Teaching, Linguistics, and Literature, 2025)
This study aimed to develop and validate a Data-Driven Constructivist Framework for integrating Deep Learning (DL) into Arabic Second Language Acquisition. Using a phenomenological approach, the research explored the lived experiences of 12 stakeholders at Islamic Junior and Senior high school Mambaus Sholihin Gresik, Indonesia. Data were collected through interviews, observations, and reflective documentation. The findings validated a five-stage pedagogical model (Orientation, Identification, Discussion, Decision, and Implementation) and showed that DL-enabled scaffolding transformed Project-Based Learning into a reflective, iterative process. The integration significantly enhanced learner autonomy and intrinsic motivation by fulfilling needs for competence and relatedness, supporting Self-Determination Theory in Semitic language education. However, sustainability depended on addressing the digital divide and improving teacher readiness through Professional Learning Communities. In conclusion, aligning adaptive DL algorithms with Islamic cultural values offers a scalable precision-education model that reconciles AI integration with linguistic and cultural preservation.