Teachers' understandability, trust, and acceptance of AI-EdTech recommendations are positively and significantly correlated¶

Claim · All claims
Evidence · 1 study · causal r1 · q2 · i3 large
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i3 Spearman correlations between understandability, trust, and acceptance were high (above 0.5) except one moderate correlation of r = 0.439, all statistically significant. → 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 · i3 · causal · r1

One-tailed Spearman correlation analysis of survey responses from 41 in-service chemistry teachers after each XAI stage, with Bonferroni-corrected α of 0.025. The article reports "all correlations were high, above 0.5, except one moderate correlation with a value of 0.439" (r = 0.439 for that one correlation).

"As we can see in Fig. 5A and B, all correlations were high, above 0.5, except one moderate correlation with a value of 0.439. The strong positive correlations indicate a significant relationship between understandability, trust, and acceptance."

Discussion¶

Related Claims¶

• Domain-driven explanations significantly increase teachers' understandability, trust, and acceptance compared to data-driven explanations — related
• The correlations among understandability, trust, and acceptance strengthen during teacher–system interaction, indicating trust is dynamic — related