Han Jining, Liu Geping, Liu Xinmiao, Yang Yuying, Quan Wenying, Chen Yongfu
Faculty of Education, Southwest University, Chongqing, China.
Yibin Municipal Education and Sports Bureau, Yibin, Sichuan, China.
Heliyon. 2024 Jun 19;10(12):e33251. doi: 10.1016/j.heliyon.2024.e33251. eCollection 2024 Jun 30.
This paper investigates the factors influencing the continuous use intention of AI-powered adaptive learning systems among rural middle school students in China. Employing a mixed-method approach, this study integrates Technology Acceptance Model 3 with empirical data collected from rural middle schools in western China. The main contributions of this study include identifying key determinants of usage intention, such as computer self-efficacy, perceived enjoyment, system quality, and the perception of feedback. The findings provide insights into enhancing rural education through AI and suggest strategies for developing more effective and engaging adaptive learning systems. This research not only fills a significant gap in the understanding of AI in education but also offers practical implications for educators and policymakers aiming to improve learning outcomes in rural settings.
本文研究了影响中国农村中学生对人工智能驱动的自适应学习系统持续使用意愿的因素。本研究采用混合研究方法，将技术接受模型3与从中国西部农村中学收集的实证数据相结合。本研究的主要贡献包括确定使用意愿的关键决定因素，如计算机自我效能感、感知乐趣、系统质量和对反馈的感知。研究结果为通过人工智能加强农村教育提供了见解，并为开发更有效、更具吸引力的自适应学习系统提出了策略。这项研究不仅填补了教育领域对人工智能理解的重大空白，也为旨在改善农村地区学习成果的教育工作者和政策制定者提供了实际参考。
