INTEGRATING MULTIMODAL AI INTO TBLT: A FRAMEWORK FOR ENHANCING STRATEGIC AND INTERACTIONAL COMPETENCE IN HIGHER EDUCATION

dc.contributor.authorSuvankulova Aziza Nuriddin qizi
dc.date.accessioned2025-12-29T09:32:27Z
dc.date.issued2025-12-12
dc.description.abstractThis article presents a new theoretical framework that demonstrates how multimodal Artificial Intelligence (AI), specifically GPT-4o and the Whisper speech-recognition model, can assist in the formation of strategic and interactional communicative competence among B1-B2 non-philological students in higher education. While previous research has focused mostly on AI’s function in enhancing language correctness, vocabulary retention, and individualized grammar assistance, the discourse-level components of communicative competence remain insufficiently addressed. These include learners’ abilities to negotiate meaning, regulate turn-taking, correct misunderstandings, and maintain coherent interaction in spontaneous communication. Building on new developments in multimodal AI, the paper proposes that tools capable of processing audible, visual, and textual input can better simulate real communicative situations than traditional teaching methods. GPT-4o can simulate adaptive, dynamic conversational contexts, whereas Whisper generates accurate transcriptions of learners’ speech, allowing for extensive review of disfluencies, hesitation markers, repair patterns, and interactional timing. When combined with a Task-Based Language Teaching (TBLT) cycle, these technologies provide more possibilities for pre-task preparation, performance-based scaffolding, and post-task reflection. The paper introduces the “AI-TBLT Strategic and Interactional Competence Framework”, which describes how learners can enhance discourse-level skills through task design, multimodal input, data-driven feedback, and teacher mediation. The model prioritizes pedagogical and ethical factors, ensuring that AI functions as a supplement rather than a substitute for human interaction. In all, the study emphasizes the potential of incorporating multimodal AI into communicative pedagogy to support non-philological students in developing the strategic and interactional flexibility necessary for academic and professional communication in multilingual, technologically advanced environments.
dc.formatapplication/pdf
dc.identifier.urihttps://americanjournal.org/index.php/ajper/article/view/3223
dc.identifier.urihttps://asianeducationindex.com/handle/123456789/16110
dc.language.isoeng
dc.publisherAmerican Journals
dc.relationhttps://americanjournal.org/index.php/ajper/article/view/3223/3075
dc.rightshttps://creativecommons.org/licenses/by-nc/4.0
dc.sourceAmerican Journal of Pedagogical and Educational Research; Vol. 43 (2025); 39-44
dc.source2832-9791
dc.subjectArtificial Intelligence, communicative competence, strategic competence, interactional competence, Task-based language teaching, multimodal AI, non-philological students, AI-pedagogy.
dc.titleINTEGRATING MULTIMODAL AI INTO TBLT: A FRAMEWORK FOR ENHANCING STRATEGIC AND INTERACTIONAL COMPETENCE IN HIGHER EDUCATION
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typePeer-reviewed Article

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