Multimodal Linguistic Technologies for Solving Spoken Interaction Problems in EFL Classrooms

Abstract

Spoken interaction is a core component of communicative competence in English as a Foreign Language (EFL) learning; however, many learners experience persistent difficulties in oral communication due to limited exposure, anxiety, pronunciation problems, and insufficient interactional feedback. Recent developments in multimodal linguistic technologies—integrating speech recognition, visual analytics, gesture modeling, and automated feedback—offer innovative solutions to these challenges. This study investigates the effectiveness of multimodal linguistic technologies in solving spoken interaction problems in EFL classrooms through an experimental research design. A mixed-methods approach was employed, involving pre- and post-speaking assessments, classroom observations, system-generated interaction data, and learner interviews. Forty EFL learners were divided into experimental and control groups, with the experimental group receiving multimodal technology-assisted instruction over an eight-week period. The findings reveal that learners exposed to multimodal technologies demonstrated significant improvement in pronunciation accuracy, fluency, turn-taking skills, and pragmatic appropriateness compared to those receiving traditional instruction. Qualitative data further indicate increased learner confidence, reduced speaking anxiety, and higher engagement levels. The study concludes that multimodal linguistic technologies provide effective pedagogical support for enhancing spoken interaction by offering immediate, multimodal feedback and authentic communicative practice. These results highlight the potential of frontier linguistic technologies to address real-world linguistic problems in EFL classrooms and contribute to the advancement of technology-enhanced language education.