AI pedagogical orientation strongly predicts self-reported AI use across research, teaching, and other professional domains¶
Claim · All claims
Evidence · 1 study · associationalr2·q2·i2medium
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i2 AI pedagogical orientation factor scores statistically significantly predict binary AI use in research, teaching, and other professional tasks, with odds ratios from approximately 2.8 to 5.0. → T. J. Atherton 2026
Evidence¶
T. J. Atherton 2026¶
T. J. Atherton, I. Descamps, M. Webel, T. R. Holmes, C. L. Vizcarra, N. Sui, J. J. Foley IV. (2026). Faculty Orientations Shape Adoption of AI in Research and Teaching. https://arxiv.org/abs/2605.18140 doi:10.48550/arxiv.2605.18140
q2 · i2 · associational · r2
Logistic regression analyses of self-reported AI use with the factor score as primary predictor, in a survey of 90 Cottrell STEM faculty. The article reports a "statistically significant pre- dictor of AI use across all three domains (allp < .001)" with printed odds ratios OR = 2.8 to 5.0 ("odd-ratios ranging from approx- imately2.8to5.0").
"AI pedagogical orientation was a statistically significant pre- dictor of AI use across all three domains (allp < .001). Log-odds estimates and associated odds ratios show sub- stantial effect sizes, with odd-ratios ranging from approx- imately2.8to5.0, with the strongest for research and teaching."
Discussion¶
Related Claims¶
- In joint analysis, Practice codes correlate most strongly with AI pedagogical orientation, while Epistemic codes show a weaker positive association — related
- Faculty with negative versus positive AI pedagogical orientation differ in how they interpret AI's role in thinking: cautious peripheral use versus integrated use with managed concerns — related
- Self-efficacy predicts cluster membership, with the article concluding self-efficacy may play a foundational role in the cluster difference — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
ai-pedagogical-orientation-predicts-ai-use |
title |
AI pedagogical orientation strongly predicts self-reported AI use across research, teaching, and other professional domains |
description |
AI pedagogical orientation strongly predicts self-reported AI use across research, teaching, and other professional domains |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — t-j-atherton-2026 |
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