A Conceptual Framework for Learning Arabic Morphology Assisted By AI Chatbots: Integrating Three Theoretical Perspectives

Arabic morphology poses persistent challenges for second language learners due to its non-concatenative, root-and-pattern system, which imposes a high cognitive load. This complexity limits opportunities for active and interactive learning while demanding timely, individualised feedback that is difficult to provide in conventional classroom settings. While AI chatbots have emerged as promising tools for language learning, their application to Arabic morphology instruction remains underexplored and often lacks a coherent theoretical grounding. This conceptual paper proposes an integrated theoretical framework for Arabic morphology learning through AI chatbots by synthesising three complementary perspectives: Cognitive Load Theory, Social Constructivism, and Corrective Feedback Theory. Cognitive Load Theory informs how complex morphological content is structured into manageable units; Social Constructivism underpins the pedagogical approach in which learners actively construct knowledge through interaction with the chatbot; and Corrective Feedback Theory explains the mechanism through which the chatbot detects errors and delivers feedback that promotes self-correction. The paper discusses how these three perspectives complement one another and converge to support effective morphological acquisition. The proposed framework offers a theoretical foundation for designing AI chatbot-assisted modules and contributes to the limited literature on technology-enhanced Arabic language learning.

Arabic morphology, AI chatbot, Cognitive Load Theory, Social Constructivism, Corrective Feedback Theory

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