The Challenge scale's three-factor structure was confirmed for students but did not reach satisfactory fit for faculty¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Student challenge-scale CFA showed good fit for the three-factor solution (Ethics and Privacy Concerns, Negative Educational Impact, Accuracy and Sensitivity), while the faculty version failed fit thresholds. → Stella Y. Kim 2026

Evidence¶

Stella Y. Kim 2026¶

Stella Y. Kim, Ji Yae Bong, Daniel Maxwell, Beth Oyarzun. (2026). Development and validation of Generative Artificial Intelligence Readiness and Perception (GenAI-RP) Scale for faculty and students in higher education. Education Tech Research Dev. https://doi.org/10.1007/s11423-026-10713-z

q2 · i? · design · r2

CFA of the challenge scale: the student version (N=450) showed good fit (CFI 0.91, RMSEA 0.066) with all factor loadings exceeding .40; the faculty version (N=128) showed poor fit (CFI 0.87, RMSEA 0.110, SRMR 0.091), and no modification-index suggestion was theoretically justifiable.

"Unlike the student version, the faculty version of the challenge scale did not yield a satisfactory model fit, as shown in Table 7. The CFI and TLI values did not meet the 0.90 threshold, and both RMSEA and SRMR were higher than .08, indicating poor fit."

Discussion¶

Related Claims¶

• The Benefit scale's two factors (Effectiveness and Empowerment) fit satisfactorily for both groups, but their near-1.00 correlation suggests a potential unidimensional structure — related
• Ethics, privacy, bias, over-reliance, accuracy, usability and cost are key challenges AI use poses for learner satisfaction and engagement — related
• The faculty readiness scale required two model modifications (a cross-loading item and two residual correlations) to reach adequate fit — related