TASK-BASED INSTRUCTION AND THE DEVELOPMENT OF COMMUNICATIVE COMPETENCE AMONG ESL PHILOLOGY STUDENTS IN UZBEKISTAN: AN AI- ENHANCED APPROACH

In response to higher education reforms in Uzbekistan, English Philology programs increasingly prioritize communicative competence aligned with international TESOL standards. However, instruction often remains structurally oriented, limiting students’ interactional fluency and pragmatic effectiveness. This study investigates the impact of AI-enhanced Task-Based Language Teaching (TBLT) on the development of communicative competence among ESL Philology students in Uzbek higher education. A quasi-experimental pre-test–post-test design was employed with 72 second-year Philology students divided into experimental and control groups over a 12-week semester. The experimental group engaged in structured TBLT cycles (pre-task, task performance, post-task reflection) supported by AI tools, including automated speech recognition feedback and corpus-based lexical analysis. The control group followed a conventional communicative syllabus without AI integration. Data were collected through standardized speaking tests, analytic rubrics measuring pragmatic competence, discourse analysis of recorded tasks, and learner perception surveys. Statistical analyses were conducted using independent and paired samples t-tests.The experimental group demonstrated statistically significant gains (p < .05) in fluency, lexical diversity, discourse management, and pragmatic appropriateness compared to the control group. Effect size calculations indicated large practical impact. AI-mediated feedback enhanced self-monitoring and reduced speaking anxiety. Findings suggest that AI-integrated TBLT significantly strengthens communicative competence and supports curriculum modernization in Uzbek higher education.

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