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Abstract
Abstract. Vocabulary knowledge is a decisive factor in the academic success of university students who study English as a foreign language (EFL), yet classroom instruction alone rarely provides the volume, distribution, and depth of lexical practice that acquisition research shows to be necessary. This conceptual article proposes the AI-Driven Mobile Vocabulary (AIMV) Framework, an integrated design model for mobile vocabulary learning environments powered by artificial intelligence. Drawing on vocabulary acquisition theory, cognitive psychology, and two decades of research on mobile-assisted language learning (MALL), the article first derives eight design principles concerning lexical selection, distributed retrieval, depth of processing, the balance of learning conditions, adaptive scaffolding, motivation, teacher orchestration, and ethical use of AI. It then presents a four-layer functional architecture — a learner model layer, a lexical content layer, an adaptive delivery engine, and an assessment–analytics layer — supported by two cross-cutting strands addressing engagement and governance. A weekly learning cycle integrating the mobile application with classroom instruction is described, with illustrations from English for Specific Purposes (ESP) courses for economics students. The article concludes with implementation considerations for higher education institutions and an agenda for the empirical validation of the framework. The AIMV Framework is intended to guide application developers, curriculum designers, teachers, and researchers who seek to move beyond generic vocabulary applications toward theoretically grounded, personalized, and pedagogically embedded mobile learning.
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