Artificial Intelligence Driven Personalized E-Learning Languages and Its Effects on User Engagement and Continuance Intention

Abstract This study explores the impacts of AI-based chatbots in personalized digital marketing in e-Learning languages platforms. Building on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the study examines the effects of performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit on users’ continued use of AI chatbots. Satisfaction and engagement are also incorporated into the model to explain how technology-related perceptions translate into sustained interaction with AI-enabled learning services. A mixed-methods approach was adopted. Quantitative data were collected from 642 users and analyzed using PLS-SEM, including tests of reliability, validity, and mediation. The quantitative findings were complemented by semi-structured interviews with 20 practitioners and system usage data, which were analyzed thematically. The findings indicate that personalized and real-time chatbot interactions are associated with higher levels of engagement, satisfaction, and continuous usage intention. Satisfaction and habit have the strongest relationships with continuance, whereas engagement provides a weaker mediating pathway. The findings also suggest that cognitive and social acceptance factors do not consistently translate into continued use, while concerns about trust and privacy remain important considerations for sustained adoption. The study contributes to the literature by extending UTAUT2 beyond technology acceptance to include experiential and behavioral outcomes in AI-supported language learning. It also offers practical implications for developing chatbot-based marketing and learning services that are personalized, trustworthy, transparent, and responsive to users’ needs.