Students’ Flow Experience of Using AI-Powered Online English Learning Platforms - Chang-Hong Wu; Wei-Shang Fan (HighTech and Innovation Journal, 2024)
Objectives: This research aims to explain the impact of flow’s antecedents on flow experience. Furthermore, this research explores the intention of students to continue using online AI-powered English learning platforms. Methodology: This study gathered data from 300 online students enrolled in AI-powered English learning platforms in Taiwan, with data collection facilitated by a research company in the country. Findings: According to the findings, flow was significantly associated with continuous intention. In terms of antecedents of flow, information quality, service support quality, and intrinsic motivation were significant, whereas confirmation, service quality, and instructor quality were not significant. Flow was found to have significant associations with perceived usefulness and satisfaction. Furthermore, confirmation significantly impacted perceived usefulness and satisfaction. Moreover, perceived usefulness was significantly associated with satisfaction but had no association with continuous intention. Lastly, both intrinsic motivation and satisfaction were associated with continuous intention. Novelty/Improvement:This research delves into the dynamic interplay between students' experiences and the adoption of AI-powered online English learning platforms. The study employed a comprehensive framework, including flow, a technology acceptance model, motivation, and an expectation confirmation model.