Perceived procedural fairness in dynamic educational resource allocation: a scenario-based factorial experiment
Year: 2026
Type: article
Abstract: Introduction Dynamic algorithmic allocation can improve the responsiveness of educational resource distribution, but frequent and opaque adjustments may also affect students' perceptions of procedural fairness. This study examines how allocation adjustment frequency, information feedback, and explanatory transparency shape perceived procedural fairness in an algorithm-mediated educational resource-allocation context. Methods A 2 × 2 × 2 between-subjects experiment was conducted with 396 undergraduate students. Reinforcement learning was used as the conceptual technological context rather than as an empirically trained or evaluated policy. The allocation trajectories presented to participants were experimentally controlled stimuli designed to manipulate observable characteristics of dynamic allocation. Results and discussion Explanatory transparency increased procedural fairness, and its effect was strengthened when detailed learning-state feedback was provided. Detailed feedback alone did not improve fairness perceptions in the absence of explanation. Procedural fairness was strongly associated with overall perceived fairness and remained a significant predictor after controlling for distributive fairness. Algorithm trust further moderated the effect of explanation, with the incremental benefit of explanatory transparency being greater among participants with lower baseline algorithm trust. These findings suggest that students evaluate not only the resources presented to them but also the temporal stability and intelligibility of the allocation procedure. The results provide a scenario-based indication that predictable adjustment rhythms and decision-level explanations may support perceived procedural fairness, while further research is needed to determine whether these effects extend to real-world adaptive allocation systems.
Source: Frontiers in Psychology
Authors: Kexuan Huang, Ziquan Feng
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7225827812