Generative AI-assisted learning and perceived academic performance in higher education: the indirect role of self-efficacy and the moderating role of AI literacy.
- 1Department of Business, Gachon University, Seongnam, Republic of Korea.
- 2Department of Business, College of Business, Gachon University, Seongnam, Republic of Korea.
Abstract
Introduction
Generative AI is increasingly used to support learning in higher education, yet the association between generative AI-assisted learning use and students' perceived academic performance, as well as the conditions under which this association varies, remains unclear. Drawing on social cognitive theory and AI literacy research, this study tested whether generative AI-assisted learning use was indirectly associated with perceived academic performance through self-efficacy and whether AI literacy conditioned the association between generative AI-assisted learning use and self-efficacy.Methods
Cross-sectional questionnaire data were collected from 376 undergraduates at universities in China. An item-level latent interaction structural equation was estimated using a matched-pair product-indicator approach.Results
The model showed positive associations between generative AI-assisted learning use and self-efficacy (β = 0.207, p < 0.001) and between self-efficacy and perceived academic performance (β = 0.199, p < 0.001). The latent interaction between generative AI-assisted learning use and AI literacy was positive (B = 0.068, 95% bias-corrected bootstrap confidence interval [0.012, 0.123]), indicating that the association between generative AI-assisted learning use and self-efficacy was stronger at higher levels of AI literacy. The conditional indirect association was not statistically significant at low AI literacy but was positive and statistically significant at the mean and high levels. The index of moderated mediation was 0.011 (95% bias-corrected bootstrap confidence interval [0.002, 0.026]), indicating that the indirect association varied across levels of AI literacy.Discussion
These findings describe a theory-consistent conditional pattern of cross-sectional associations but do not establish temporal or causal ordering.
Related
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BibTeX
@article{shen2026generative,
title = {{Generative AI-assisted learning and perceived academic performance in higher education: the indirect role of self-efficacy and the moderating role of AI literacy.}},
author = {Xiaotong Shen and Geumchul Um},
journal = {Frontiers in psychology},
year = {2026},
month = oct,
doi = {10.3389/fpsyg.2026.1949060},
url = {https://europepmc.org/article/MED/42903429},
note = {CC BY},
}
RIS
TY - JOUR
TI - Generative AI-assisted learning and perceived academic performance in higher education: the indirect role of self-efficacy and the moderating role of AI literacy.
AU - Xiaotong Shen
AU - Geumchul Um
PY - 2026
DA - 2026/10/06
JO - Frontiers in psychology
DO - 10.3389/fpsyg.2026.1949060
UR - https://europepmc.org/article/MED/42903429
AB - Introduction Generative AI is increasingly used to support learning in higher education, yet the association between generative AI-assisted learning use and students' perceived academic performance, as well as the conditions under which this association varies, remains unclear. Drawing on social cognitive theory and AI literacy research, this study tested whether generative AI-assisted learning use was indirectly associated with perceived academic performance through self-efficacy and whether AI literacy conditioned the association between generative AI-assisted learning use and self-efficacy.Methods Cross-sectional questionnaire data were collected from 376 undergraduates at universities in China. An item-level latent interaction structural equation was estimated using a matched-pair product-indicator approach.Results The model showed positive associations between generative AI-assisted learning use and self-efficacy (β = 0.207, p < 0.001) and between self-efficacy and perceived academic performance (β = 0.199, p < 0.001). The latent interaction between generative AI-assisted learning use and AI literacy was positive (B = 0.068, 95% bias-corrected bootstrap confidence interval [0.012, 0.123]), indicating that the association between generative AI-assisted learning use and self-efficacy was stronger at higher levels of AI literacy. The conditional indirect association was not statistically significant at low AI literacy but was positive and statistically significant at the mean and high levels. The index of moderated mediation was 0.011 (95% bias-corrected bootstrap confidence interval [0.002, 0.026]), indicating that the indirect association varied across levels of AI literacy.Discussion These findings describe a theory-consistent conditional pattern of cross-sectional associations but do not establish temporal or causal ordering.
SN - 1664-1078
ER -