Domain-driven explanations significantly increase teachers' understandability, trust, and acceptance compared to data-driven explanations¶
Claim · All claims
Evidence · 1 study · causalr1·q2
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.
q2 i? One-tailed Wilcoxon signed-rank tests showed statistically significant increases in understandability (W = 80.5, p = 0.005), learned trust (W = 52, p = 0.002), and acceptance (W = 22.5, p = 0.003) after domain-driven explanations following data-driven ones. → Yael Feldman-Maggor 2025
Evidence¶
Yael Feldman-Maggor 2025¶
Yael Feldman-Maggor, Mutlu Cukurova, Carmel Kent, Giora Alexandron. (2025). The Impact of Explainable AI on Teachers' Trust and Acceptance of AI EdTech Recommendations: The Power of Domain-specific Explanations. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-025-00486-6
q2 · i? · causal · r1
Within-subject staged experiment with 41 in-service chemistry teachers using GrouPer; one-tailed Wilcoxon signed-rank tests compared each construct after data-driven versus domain-driven explanations. The article reports "statistically significant results for all the variables" with the printed W and p values; no effect size was printed.
"The test yielded statistically significant results for all the variables: Understandability—W = 80.5, p = 0.005; Learned trust—W = 52, p = 0.002; Acceptance—W = 22.5, p = 0.003**."
Discussion¶
Related Claims¶
- Teachers report domain-driven explanations are more influential for building trust than data-driven explanations — related
- Teachers' understandability, trust, and acceptance of AI-EdTech recommendations are positively and significantly correlated — related
- The correlations among understandability, trust, and acceptance strengthen during teacher–system interaction, indicating trust is dynamic — related
- User acceptance of AI-driven decisions depends on explanation type, with sensitivity-analysis and certification explanations increasing acceptance — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
domain-driven-explanations-increase-understandability-trust-acceptance |
title |
Domain-driven explanations significantly increase teachers' understandability, trust, and acceptance compared to data-driven explanations |
description |
Domain-driven explanations significantly increase teachers' understandability, trust, and acceptance compared to data-driven explanations |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2025 |
generated |
by process:wiki-ingest on 2026-10-12 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — yael-feldman-maggor-2025 |
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