From Acceptance to Satisfaction: Understanding AI-Driven Chatbots on English Learning Through Technology Acceptance Model (TAM) and Self-Determination Theory (SDT)

Abstract

Adopting AI-driven chatbots in English as foreign language (EFL) learning has significantly gained scholarly attention. However, studies primarily adopted technology acceptance model (TAM) and self-determination theory (SDT) separately. An integration of both theoretical models is crucial to develop a comprehensive understanding, specifically the roles of technological perceptions and psychological needs. A cross-sectional online survey was conducted among 552 EFL learners who were at least aged 18 and undergraduate students using AI chatbots at least once a week for over 10 weeks. Covariance-based structural equation modeling (CB-SEM) was implemented to test the hypothesised relationships. Findings revealed that perceived ease of use (PEOU) is a significant predictor of perceived usefulness (PU) but not a significant predictor of attitude (AT). PU predicts AT significantly. Subsequently, AT serves as a significant predictor of behavioural intention (BI), while PU does not. For mediating effect, BI is a significant mediator for the relationship between AT and actual use (AU). Need satisfaction and need frustration are the significant outcomes of AU. AI developers are encouraged to design chatbots that can enhance EFL learners’ engagement, autonomy, competence, and relatedness for sustainable adoption and effective learning outcomes.