SR–OB profile patterns differ by prior AI literacy experience: overestimation appears only among experienced teachers and a low–low profile only among inexperienced teachers¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · no dated study recorded · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? In subgroup LPAs, overestimation (high self-reports with lower performance) appeared only among teachers with prior AI literacy experience (n = 186), whereas a low–low profile appeared only among teachers without such experience (n = 102). → lpa-sub

Evidence¶

lpa-sub¶

Shan Zhang, Ruiwei Xiao, Anthony F. Botelho, Guanze Liao, Thomas K. F. Chiu, John Stamper, and Kenneth R. Koedinger. 2026. How to Assess AI Literacy: Misalignment Between Self-Reported and Objective-Based Measures. In LAK26: 16th International Learning Analytics and Knowledge Conference (LAK 2026). ACM, New York, NY, USA. https://doi.org/10.1145/3785022.3785088

q2 · i? · associational · r2

Subgroup latent profile analyses compared teachers with prior AI literacy experience (N = 186; three profiles) and without (N = 102; three profiles). The comparison showed "overestimation ... appeared only among teachers with prior AI literacy experience, whereas underestimation emerged in both groups"; the low–low profile (n = 30) was unique to the inexperienced group.

"When comparing the two subgroups, overestimation (high self-reports paired with lower performance) appeared only among teachers with prior AI literacy experience, whereas underestimation emerged in both groups"

Discussion¶

Related Claims¶

• Latent profile analysis of combined SR and OB factors identifies six distinct teacher profiles spanning overestimation, underestimation, and alignment — related
• Teachers' self-reported AI literacy correlates only weakly with their objectively measured performance (r = 0.07–0.24) — related
• Adding a cross-factor composite from a teacher study yields r = .079, still with an interval including zero, and latent profiles reveal over- and underestimation of competence — related
• Students' self-reported AI literacy was moderately negatively correlated with objectively measured AI competency, suggesting potential overestimation of AI abilities — related
• Experienced teachers diagnose underestimating, overestimating, and uninterested student profiles more accurately than novices, with partial eye-tracking support — related
• A two-dimensional profiling method based on initial artifact quality and criterion-aligned revision categorizes student groups into four analytic profiles in an AI-supported RQ/hypothesis revision activity — related
• Perceived and objectively demonstrated AI literacy show only limited alignment in the same samples (pooled r = .055, interval including zero) — related
• Misalignment profiles predict growth-oriented disengagement: students can endorse self-change while withdrawing when routes are perceived as infeasible — related