The Effectiveness of an AI-Supported Neurolinguistic Strategy in Developing Functional Vocabulary and Reducing Digital Fatigue Among Non-Native Learners of Arabic

Abstract

When teaching Arabic to speakers of other languages, novice learners face challenges in acquiring practical vocabulary and in dealing with pressures associated with digital learning environments. Therefore, this study aimed to examine the effectiveness of an AI-supported neurolinguistic approach in enhancing functional vocabulary and decreasing digital fatigue among these learners. The research employed a quasi-experimental design involving two groups of learners: an experimental group and a control group, each comprising 30 learners. The strategy incorporated elements of multi-sensory contextual learning, mental activation through auditory and visual stimuli, collaborative communicative tasks, and immediate feedback via ChatGPT integrated into Google Sites. Data were collected and analyzed to compare the performance of both groups across all dimensions of functional vocabulary, including recognition, comprehension, and production, as well as levels of digital fatigue (physical, cognitive, emotional, and social-sensory). The results showed that learners in the experimental group significantly outperformed the control group in all aspects of functional vocabulary while experiencing less digital fatigue. These findings show that integrating neurolinguistic principles with AI-supported interactive learning to develop functional vocabulary and enhance learner motivation within digital contexts. Based on these results, we recommend incorporating AI-supported neurolinguistic strategies into Arabic language teaching for non-native speakers to create more effective and supportive learning environments.