Decoding flow experience in AI-assisted learning: a comprehensive model linking flow antecedents to continuous intention

Purpose The increasing integration of technology has garnered significant attention across both educational and industrial domains, particularly concerning the development of engaging experiences for online users. This study aims to bridge several notable gaps in existing literature. Firstly, it examines the influence of key antecedents of flow—namely perceived enjoyment, perceived visual appeal, interactivity, and intrinsic motivation in artificial intelligence (AI)-assisted learning—on the flow experience. Secondly, it evaluates the impact of intrinsic motivation in AI-assisted learning, flow experience, and educational effectiveness on continuous intention. Thirdly, it investigates the relationship between flow experience and educational effectiveness. Methods To achieve these objectives, the study employed an online survey methodology, collecting data from a sample of 302 users of AI-assisted learning platforms. Results The findings indicate that all examined antecedents—perceived enjoyment, perceived visual appeal, interactivity, and intrinsic motivation in AI-assisted learning—positively and significantly influenced flow experience. Additionally, continuous intention was significantly predicted by intrinsic motivation in AI-assisted learning and educational effectiveness, whereas flow experience did not directly predict continuous intention. Finally, flow experience emerged as a significant determinant of educational effectiveness. Conclusion This research makes a meaningful contribution to the theoretical advancement of flow experience theory in the context of online education.