文献类型：期刊文献

英文题名：AI performance assessment in blended learning: mechanisms and effects on students' continuous learning motivation

作者：Ji, Hao[1];Suo, Lingling[1];Chen, Hua[1]

通讯作者：Ji, H[1]

机构：[1]Beijing Union Univ, Coll Management, Beijing, Peoples R China

第一机构：北京联合大学管理学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Management, Beijing, Peoples R China.|[1141755]北京联合大学管理学院;[11417]北京联合大学;

年份：2024

卷号：15

外文期刊名：FRONTIERS IN PSYCHOLOGY

收录：;Scopus(收录号：2-s2.0-85213678691);WOS:【SSCI(收录号:WOS:001390012600001)】；

基金：The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by the Educational Science Research Project at Beijing Union University, titled "Optimization Path and Effect Study on the Evaluation of Core Competencies of Undergraduate Accounting Talents Empowered by Digital Intelligence" (No. JK202407).

语种：英文

外文关键词：AI performance assessment; blended learning; continuous learning motivation; expectation confirmation model (ECM); educational technology

摘要：Introduction: Blended learning combines the strengths of online and offline teaching and has become a popular approach in higher education. Despite its advantages, maintaining and enhancing students' continuous learning motivation in this mode remains a significant challenge. Methods: This study utilizes questionnaire surveys and structural equation modeling to examine the role of AI performance assessment in influencing students' continuous learning motivation in a blended learning environment. Results: The results indicate that AI performance assessment positively influences students' continuous learning motivation indirectly through expectation confirmation, perceived usefulness, and learning satisfaction. However, AI performance assessment alone does not have a direct impact on continuous learning motivation. Discussion: To address these findings, this study suggests measures to improve the effectiveness of AI performance assessment systems in blended learning. These include providing diverse evaluation metrics, recommending personalized learning paths, offering timely and detailed feedback, fostering teacher-student interactions, improving system quality and usability, and visualizing learning behaviors for better tracking.