Technology-enhanced learning frequency and perceived academic acceleration in AI-mediated digital learning environments: self-efficacy as an indirect pathway and learning motivation as a boundary condition

Xuezhen Chen, Zexuan Qi, Yifan Ou

AI-mediated digital learning environments have transformed how students communicate with knowledge, access learning resources, and receive algorithmically generated feedback, thereby reshaping how they perceive the pace of academic progress.This study examines perceived academic acceleration as a learning outcome distinct from general achievement, focusing on students’ self-reported sense that they move through academic tasks and course materials more quickly when supported by digital tools. A cross-sectional survey model was used to test whether technology-enhanced learning frequency was associated with perceived academic acceleration directly and indirectly through self-efficacy, and whether learning motivation conditioned the technology-acceleration association. Item-level data from 420 undergraduate students were analyzed using reliability analysis, correlation analysis, measurement-model diagnostics, ordinary least squares regression, bootstrap indirect-effect estimation, moderation analysis, and collinearity diagnostics. Results indicated that technology-enhanced learning frequency, self-efficacy, and learning motivation were positively associated with perceived academic acceleration. The indirect association through self-efficacy was supported by bootstrap confidence intervals, suggesting that students who reported more frequent technology-enhanced learning within AI-mediated learning environments also reported stronger efficacy beliefs, which in turn were associated with stronger perceived academic acceleration. The interaction between technology-enhanced learning frequency and learning motivation was positive but did not reach conventional significance. The findings should therefore be interpreted as evidence of associations among students’ technology use, efficacy beliefs, motivation, and perceived acceleration, not as causal evidence that technology use produces faster academic progress.These findings contribute to the emerging literature on AI-mediated learning and digital communication by demonstrating how technology-supported learning experiences are associated with students’ perceptions of academic progress through psychological mechanisms.The study clarifies the conceptual boundary of perceived academic acceleration and highlights the need for longitudinal and behavioral validation in future research.