Using Artificial Intelligence for Personalized Arabic Language Learning: A Case Study of the Federal College of Education, Katsina (Journal of Natural Language and Linguistics, 2025)
This paper is seeks to examine how Artificial Intelligence (AI) powered personalized learning platform can influences Arabic language proficiency among non-native speakers. The paper used a quasi-experimental design by selecting fifty students from Arabic Department of Federal College of Education, Katsina randomly, the students were divided into two groups, in which one group received instruction in an Arabic course through traditional teaching method, while the other engaged with Duolingo an adaptive AI platform that offer personalized learning over a period of 12-week. To assess their progress, pre and post - test were administer to both groups to evaluate their reading, writing, listening, and speaking skills in Arabic Language. The results were as follows: the experimental group’s average proficiency score jumped from 41.2 to 68.7 which signifying a gain of 27.5 points (p < 0.001) far outpacing the control group’s 10.4 point improvement. The result of the experiment also shows strong participation from the experimental group, where 92% of modules was completed and the students spends an average of 4.6 hours per week on the platform. Based on these findings we can say that Ai driven personalized learning can significantly improve Arabic language acquisition among non - native speakers, it also foster deeper student engagement, and promote learner independence. Based on this evidence the study recommends that Nigerian tertiary institutions consider integrating AI-based tools into Arabic language instruction to address the challenges faced by non-native learners of Arabic Language.