What Drives Learners to Keep Using Chatbots? An Extended TCT Modeling in Higher Education

Abstract

The rapid integration of conversational AI tools in higher education necessitates a deeper understanding of learners’ sustained engagement with chatbot systems. This study investigated the cognitive, affective, and value-based determinants of higher education learners’ intention to continue using AI chatbot assistance with the extended Technology Continuance Theory (TCT). Data were collected from 600 Indian higher education learners and analyzed using Covariance-based structural equation modeling (CB-SEM). The results demonstrate that perceived usefulness (PU) and perceived ease of use (PEU) play central roles in shaping learners’ attitudes (ATT), satisfaction (SAT), and continuance intention (CI). Confirmation (CON) emerged as a critical post-adoption factor, significantly influencing PU, SAT, and affective support (AS). Notably, AS and academic value (AV) contributed uniquely to learners’ CI, highlighting the AI chatbot’s socio-emotional and instrumental dimensions. ATT and SAT were also identified as strong predictors of sustained usage, and AI Self-Efficacy (AISE) positively influenced PEU. The model explained substantial variance in key endogenous constructs with CI ( R 2 = 0.740), thereby underpinning the model’s strong explanatory power. The findings extend TCT by integrating affective and academic value dimensions within chatbot-mediated learning contexts. Implications emphasize responsible pedagogical integration, balancing efficiency and emotional support with critical engagement, transparency, and meaningful human interaction in higher education.