Predicting perceived learning effectiveness from generative artificial intelligence in higher education: a hierarchical regression analysis of psychological, behavioral, and cognitive factors.
- DOI
- 10.1007/s10339-026-01389-7
- Published
- 2026-08-18
- Container
- Cogn Process
- Publisher
- Not recorded
- Open access
- no
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limited evidence Score 43/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1007/s10339-026-01389-7,
title = {Predicting perceived learning effectiveness from generative artificial intelligence in higher education: a hierarchical regression analysis of psychological, behavioral, and cognitive factors.},
author = {Alamri H.},
year = {2026},
journal = {Cogn Process},
doi = {10.1007/s10339-026-01389-7},
url = {https://doi.org/10.1007/s10339-026-01389-7}
}RIS
TY - JOUR TI - Predicting perceived learning effectiveness from generative artificial intelligence in higher education: a hierarchical regression analysis of psychological, behavioral, and cognitive factors. AU - Alamri H. PY - 2026 JO - Cogn Process DO - 10.1007/s10339-026-01389-7 UR - https://doi.org/10.1007/s10339-026-01389-7 ER -
APA
H., A. (2026). Predicting perceived learning effectiveness from generative artificial intelligence in higher education: a hierarchical regression analysis of psychological, behavioral, and cognitive factors.. Cogn Process. https://doi.org/10.1007/s10339-026-01389-7
Source records
- europe-pmc · retrieved 2026-09-25T06:43:29.773Z