Higher AI agency is associated with lower intention to use AI in education, while transparency, explainability, and privacy do not directly predict intention to use¶
Claim · All claims
Evidence · 1 study · causalr2·q3
Currency · newest study 2025 · 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.
q3 i? In a multiple regression across the full sample, agency significantly negatively predicted intention to use (β = -0.2519, p < 0.001), whereas transparency, explainability, and privacy were not significant direct predictors, and the model accounted for ~1.5% of variance. → Alexander John Karran 2025
Evidence¶
Alexander John Karran 2025¶
Alexander John Karran, Patrick Charland, Joé Trempe-Martineau, Ana Ortiz de Guinea Lopez de Arana, Anne-Marie Lesage, Sylvain Sénécal & Pierre-Majorique Léger. (2025). Multi-stakeholder perspective on responsible artificial intelligence and acceptability in education. npj Science of Learning 10:44. https://doi.org/10.1038/s41539-025-00333-2
q3 · i? · causal · r2
Multiple regression on the full sample (final sample of 1198 participants) examined direct effects of the four manipulated vignette variables on intention to use. The model significantly predicted intention to use, F(4, 1207) = 4.683, (p < 0.001), and "accounted for ~1.5% of the variance in intention to use (R² = 0.015, Adjusted R² = 0.012)". No effect size beyond R² is printed.
"Agency was shown to be a significant predictor, with a higher level of Agency associated with a lower intention to use (β = -0.2519,p < 0.001). However, Transparency (β =0 . 0 1 6 8 ,p =0 . 7 9 9 ) , Explainability (β = 0.1189, p = 0.0071), and Privacy (β = 0.0718, p = 0.275) were not significant predictors of intention to use"
Discussion¶
Related Claims¶
- Teacher communion is a stronger predictor of student emotions than teacher agency — related
- Focal paths are robust to background controls, which did not directly predict AI integration intention — related
- AI literacy relates to AI integration intention through a serial indirect pathway via AI-TPACK and science teaching self-efficacy — related
- For students, AI agency reduced intention to use Correct-AI indirectly through lower perceived global utility and justice, with no direct effect — related
- For parents, higher AI agency lowered confidence, which indirectly reduced intention to allow AI to correct student work (Correct-AI) — related
- For teachers, higher privacy levels lowered confidence regarding an AI supporting student emotional health, indirectly reducing intention to use (Emotion-AI) — related
- For students, privacy (Answer-AI) and explainability (Emotion-AI) each positively influenced intention to use indirectly through perceived global utility — related
- For teachers, explainability (Correct-AI) and both agency and explainability (Answer-AI) influenced intention to use indirectly through global utility and perceived usefulness — related
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by process:wiki-ingest on 2026-10-26 |
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