The correlations among understandability, trust, and acceptance strengthen during teacher–system interaction, indicating trust is dynamic¶

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

Subclaims¶

q2 i? Correlations between the three variables were stronger following the domain-driven explanation, suggesting these factors vary during teachers' interactions with the system. → 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

Comparison of Spearman correlation matrices (Fig. 5A data-driven, Fig. 5B domain-driven) from the same 41-teacher survey. The article reports the correlations "were stronger following the Domain-driven explanation"; no effect size for the change was printed.

"Notably, Fig. 5A and B demonstrate that the correlations between the variables were stronger following the Domain-driven explanation, suggesting that these factors vary during the teachers' interactions with the system."

Discussion¶

Related Claims¶

• Domain-driven explanations significantly increase teachers' understandability, trust, and acceptance compared to data-driven explanations — related
• Teachers' understandability, trust, and acceptance of AI-EdTech recommendations are positively and significantly correlated — related