Adaptive Learning Systems for Classical Arabic: Personalised Pathways to Proficiency

Abstract

This research paper focuses on the creation and deployment of an AI-powered adaptive learning framework designed to facilitate the acquisition of Classical Arabic. Although Classical Arabic is a language of religious and cultural heritage, learning it is not an easy task due to its rich morphology and syntax, as well as the limited opportunities for immersion in the country for those who know it as non-native speakers. Generic pedagogical practices commonly take a generalized but non-individualistic direction as they disregard the divergence and fluctuation of learners in time pace, cognitive inclination, and proficiency level in the usage of languages. This study suggests a machine learning-enabled adaptive learning system to overcome these problems by dynamically tailoring the content delivery according to the real-time evaluation of the performance of the learners. The technology analyzes the inputs of learners using natural language processing (NLR) methods, and it uses reinforcement learning algorithms to optimize specialized learning pathways. These characteristics comprise: (1) diagnostic testing to seed learner profiles, (2) fine-grained tracking of morphological and syntactic skills, and (3) adaptive feedback systems that automatically adjust the challenge level of exercises and instructional approaches (e.g., visual emphasis or auditory emphasis). An intermediate level group (n=120) of students is used to carry out a controlled experiment; proficiency outcomes over 6 months between the adaptive system and the normal classroom instruction method are compared. Statistically significant gains in grammatical accuracy (p < 0.01) and vocabulary retention (p < 0.05) will be shown in the adaptive learning cohort, especially among the learners who had low metacognitive awareness. Qualitative feedback indicates the effectiveness of the system in alleviating the anxiety of the learners by means of scaffolded challenges.
The research makes a contribution to computational linguistics and Islamic pedagogy in the following manner: D elivering how adaptive algorithms are the effective means to facilitate the structural rigour in Classical Arabic and support various learning paths. Offers a guide to ethical inclusion of AI in sacred language learning that deals with cultural commodification issues. Providing scalable outcomes of madrasas and online platforms that experience instructor deficiencies.