Questionable mediated effect of artificial intelligence anxiety on academic burnout: A comment on Long et al. (2026)
Abstract
Drawing on the covariance matrix reported by Long et al., I reassessed the empirical basis of their mediation model linking artificial intelligence anxiety to academic burnout. I demonstrate that the observed covariance structure can be reproduced by a theoretically different latent-variable model that does not rely on the directional mechanism proposed in the original article. In this alternative account, self-reported artificial intelligence anxiety, perceived stress, rumination, and academic burnout are conceptualized as negative indicators of a broader construct, core self-evaluation. The results illustrate a broader inferential problem in psychological research: multiple theoretically plausible models may provide equally adequate explanations of the same correlational pattern. Consequently, observational associations alone offer limited grounds for preferring one explanation over competing alternatives. Researchers should therefore routinely evaluate alternative models when drawing inferences about psychological mechanisms from nonexperimental data.
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BibTeX
@misc{sorjonen2026questionable,
title = {{Questionable mediated effect of artificial intelligence anxiety on academic burnout: A comment on Long et al. (2026)}},
author = {Kimmo Sorjonen},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/6bae8_v1},
url = {https://osf.io/preprints/psyarxiv/6bae8_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Questionable mediated effect of artificial intelligence anxiety on academic burnout: A comment on Long et al. (2026)
AU - Kimmo Sorjonen
PY - 2026
DA - 2026/10/07
PB - PsyArXiv
DO - 10.31234/osf.io/6bae8_v1
UR - https://osf.io/preprints/psyarxiv/6bae8_v1/
AB - Drawing on the covariance matrix reported by Long et al., I reassessed the empirical basis of their mediation model linking artificial intelligence anxiety to academic burnout. I demonstrate that the observed covariance structure can be reproduced by a theoretically different latent-variable model that does not rely on the directional mechanism proposed in the original article. In this alternative account, self-reported artificial intelligence anxiety, perceived stress, rumination, and academic burnout are conceptualized as negative indicators of a broader construct, core self-evaluation. The results illustrate a broader inferential problem in psychological research: multiple theoretically plausible models may provide equally adequate explanations of the same correlational pattern. Consequently, observational associations alone offer limited grounds for preferring one explanation over competing alternatives. Researchers should therefore routinely evaluate alternative models when drawing inferences about psychological mechanisms from nonexperimental data.
ER -