课程调剂如何降低学习投入：AI归因调剂干预与自我效能的中介作用

ORIGINAL RESEARCH article

Front. Psychol.

Sec. Educational Psychology

• 1. Huzhou Normal University, Huzhou, China

• 2. Fudan University, Shanghai, China

Abstract

Abstract Introduction: Course reassignment is a common practice in university registration systems, yet reassigned students often exhibit lower learning engagement than those in self-selected courses, and the underlying psychological mechanism remains unclear. This study examined the impact of course reassignment on learning engagement, the mediating role of self-efficacy, and the buffering effect of AI-basedAI-attributed course reassignment. Methods: Two scenario-based online experiments were conducted with university students randomly assigned to conditions. Experiment 1 (N = 393) used a single-factor between-subjects design comparing reassignment versus self-selection. Experiment 2 (N = 293) added an AI-basedAI-attributed reassignment condition in which an AI engine matched students' interests, traits, and abilities with the most suitable available courses (participants in the AI condition read a simulated AI-generated learning analysis report.). Participants completed a learning task and reported learning engagement and self-efficacy. Mediation analyses were conducted using bootstrapping. Results: Experiment 1 showed that course reassignment was associated with lower learning engagement course reassignment significantly reduced learning engagement compared with self-selection, and this effect was mediated by self-efficacy (indirect effect B = 0.198, SE = 0.051, 95%CI [0.096, 0.299]). Experiment 2 demonstrated that AI-basedAI-attributed reassignment attenuated this negative effect. Learning engagement in the AI-basedAI-attributed condition did not differ from self-selection and was significantly higher than in random reassignment. Moreover, the indirect effect through self-efficacy became nonsignificant (B = 0.140, SE = 0.113, 95% CI [-0.077, 0.362]B = 0.198, SE = 0.051, 95%CI [0.096, 0.299]). Discussion: These findings identify self-efficacy as a key psychological mechanism through which course reassignment impairs learning engagement, and demonstrate that AI-basedAI-attributed reassignment can restore both self-efficacy and engagement by signaling person-course fit. This study extends self-efficacy theory to the course selection context and offers a feasible, technology-driven intervention for mitigating the negative consequences of course reassignment in higher education.

Keywords

AI-basedAI-attributed reassignment, course reassignment, Course selection, Learning engagement, self-efficacy

Received

03 August 2026

Accepted

28 September 2026

Copyright

© 2026 Li and Tang. 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:

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