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Description
This study investigates the application of multimodal discourse analysis (MDA) based on artificial intelligence (AI) to enhance university English teaching, particularly in Academic English writing and digital multimodal composing courses. The purpose is to examine how AI-powered tools—such as text-to-video generators, voice-based chatbots, and AI feedback systems—reshape semiotic decision-making, meaning-making processes, and student engagement in higher education English contexts. Employing a mixed-methods design, the research involves 200 EFL university students enrolled in an Academic English course integrating AI-enhanced multimodal activities. Data collection includes AI-generated video compositions, student written reflections, classroom interaction recordings (voice, text, facial expressions, body movements), and post-task questionnaires measuring self-efficacy, enjoyment, and continuance intention. The methodology combines multimodal critical discourse analysis with cross-modal feature dynamic fusion modeling to analyze semantic associations and emotional states in teacher-student-AI interactions. Findings reveal that AI significantly enhances students' technical fluency, design experimentation, and efficiency in mode-switching. Students demonstrate high self-efficacy and enjoyment when using AI text-to-video tools, with strong continuance intention. Quantitative results indicate improved teacher response delay (2.19 seconds), increased student interaction density (14 times per minute), and stable high emotion scores (3.4–4.3). However, challenges persist in critical AI evaluation, ethical attribution, and preservation of authorial voice. The implications underscore the necessity of integrating AI literacy into university English curricula, emphasizing AI as a collaborative scaffold rather than a substitute for human creativity. This research contributes theoretical insights into AI as semiotic agents in multimodal meaning-making and offers practical strategies for pedagogical reform in the age of AI, supporting the transition from single-modality to multimodal teaching paradigms in English education.
