MODELLING COLLEGE STUDENTS INTENTION TO USE GENERATIVE AI-BASED ENGLISH LANGUAGE LEARNING APPLICATIONS: AN INTEGRATED TAM-TPB APPROACH

Abstract

The progression of Generative Artificial Intelligence (GAI) has revolutionized English language learning. It has ensured personalized, interactive, and accessible learning experiences. This study examines the factors influencing college students' intention to use generative AI based English language learning applications. The present study integrates the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). The study explores the effects of perceived usefulness, perceived ease of use, attitude, subjective norm, and perceived behavioral control on students behavioural intention. A cross sectional descriptive research design was applied. The primary data were collected through a structured questionnaire circulated to undergraduate and postgraduate students in various Arts and Science Colleges. Structural Equation Modeling (SEM) was employed to test the proposed conceptual model utilizing 267 responses. It is found that Perceived usefulness and perceived ease of use significantly and positively influenced students attitudes toward using Generative AI based English language applications. The study also shows that attitude, subjective norm, and perceived behavioral control significantly influenced behavioural intention of students. The structural model demonstrated 69.5% of the variance in attitude and 56.9% of the variance in behavioural intention. The findings show that the integrated TAM and TPB framework provides a rigorous explanation of college students acceptance of generative AI based English language learning applications.