A Multimodal Human–AI Instructional Framework for Productive Vocabulary Development: A Classroom Evaluation of a Coordinated LLM–ASR System

This study examined the implementation and instructional effectiveness of a multimodal AI-supported instructional framework integrating a generative AI assistant (Microsoft Copilot) with a speech-recognition-based mobile learning application (Mondly) to support productive vocabulary development in EFL higher education. Unlike studies focusing on single AI tools, this study evaluates a coordinated dual-module instructional configuration combining LLM-based lexical support with ASR-based spoken retrieval practice within a structured classroom routine. The proposed framework can be viewed as a lightweight socio-technical instructional arrangement in which learners engage with complementary AI components through guided feedback and repeated practice. A quasi-experimental pretest–post-test control group design was conducted over an eleven-week semester with 64 first-year EFL students at an Iraqi university. Productive vocabulary knowledge was measured using the Productive Vocabulary Levels Test (PVLT), and data were analyzed using mixed-design ANOVA. Results revealed a statistically significant Time × Group interaction with a large effect size, indicating greater productive vocabulary gains in the AI-supported condition compared with traditional instruction. Qualitative findings further suggested perceived improvements in lexical retrieval, sentence construction, pronunciation accuracy, and learner engagement. From an instructional perspective, the findings suggest that learning gains were associated with the coordinated use of complementary AI tools within a structured classroom workflow. This study provides a practical instructional model that may be adaptable to comparable resource-constrained higher-education contexts.