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ORIGINAL RESEARCH article

Front. Psychol.
Sec. Educational Psychology
Volume 15 - 2024 | doi: 10.3389/fpsyg.2024.1447680
This article is part of the Research Topic Along the Path to Recovery: Supporting Student Learning Motivation, Engagement and Development in Post-Pandemic Higher Education View all 8 articles

AI Performance Assessment in Blended Learning: Mechanisms and Effects on Students' Continuous Learning Motivation

Provisionally accepted
Hao Ji Hao Ji *Lingling Suo Lingling Suo Hua Chen Hua Chen
  • Beijing Union University, Beijing, China

The final, formatted version of the article will be published soon.

    Blended learning, which integrates the advantages of both online and offline teaching, has been widely adopted in higher education. However, effectively enhancing students' continuous learning motivation within this teaching mode remains a challenge.Based on questionnaire surveys and structural equation modeling, we investigate the impact of AI performance assessment on students' continuous learning motivation in blended learning. The results show that AI performance assessment enhances students' continuous learning motivation through expectation confirmation, perceived usefulness, and learning satisfaction. However, AI performance assessment alone does not directly lead to continuous learning motivation in a blended learning environment. Based on these findings, this paper proposes measures to optimize the effectiveness of AI performance assessment systems in blended learning environments, including, but not limited to providing diverse evaluation metrics, personalized learning path recommendations, timely and detailed performance feedback, enhancing teacher-student interaction, improving system quality and usability, and tracking and visualizing learning behaviors.

    Keywords: AI performance assessment, blended learning, continuous learning motivation, Expectation confirmation model (ECM), Educational Technology

    Received: 12 Jun 2024; Accepted: 28 Nov 2024.

    Copyright: © 2024 Ji, Suo and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Hao Ji, Beijing Union University, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.