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Course reassignment and learning engagement: the intervention strategy of AI-attributed reassignment.

Created on 28 Oct 2026

Authors

Han Li, Yuner Zhu, Yifan Tang

Published in

Frontiers in psychology. Volume 17. Pages 1957576. Epub Oct 13, 2026.

Abstract

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-attributed course reassignment.
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 vs. self-selection. Experiment 2 (N = 293) added an AI-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.
Experiment 1 showed that course reassignment was associated with lower 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-attributed reassignment attenuated this negative effect. Learning engagement in the AI-attributed condition did not differ from self-selection and was significantly higher than in random reassignment. Moreover, the indirect effect through self-efficacy became non-significant (B = 0.140, SE = 0.113, 95% CI [-0.077, 0.362]).
These findings identify self-efficacy as a key psychological mechanism through which course reassignment impairs learning engagement, and demonstrate that AI-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.

PMID:
42947885
Bibliographic data and abstract were imported from PubMed on 28 Oct 2026.

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