Unlocking Continuous Engagement in AI-Driven Education: An Integrated Model of Flow Experience, Academic Performance, and Technology Acceptance

This study examines the factors influencing distance education and online English learning in AI-driven education by integrating flow experience theory with the Technology Acceptance Model. Using a quantitative research approach, data was collected from 289 learners using AI-assisted platforms and analyzed through structural equation modeling (SEM). The findings revealed that flow was significantly associated with continuous intention. All the antecedents, including intrinsic motivation, immediacy, and feedback, significantly impacted flow, except telepresence and competition. Furthermore, flow significantly impacted online engagement, perceived ease of use, academic performance, and perceived usefulness. Lastly, online engagement, perceived usefulness, academic performance, and perceived ease of use significantly impacted CI. These findings offer theoretical insights into digital learning engagement and provide practical implications for designing AI-enhanced educational platforms that foster sustained learner commitment through optimized user experiences, personalized feedback mechanisms, and instructional strategies that promote flow.