Claims

Self-reported instruments measure AI self-efficacy (perceptions) rather than AI literacy abilities, so both should be included in comprehensive assessment

Claim · All claims
Evidence · 1 study · design r3 · q1
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person · recheck by 2028: tool and model accuracy changes quickly

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.

q1 i? The authors argue that a self-reported approach can only examine perceptions of using and evaluating AI (self-efficacy), not abilities and skills (literacy), which require tests or examinations. → Thomas K. F. Chiu 2025

Evidence

Thomas K. F. Chiu 2025

Thomas K. F. Chiu, Murat Çoban, Ismaila Temitayo Sanusi, Musa Adekunle Ayanwale. (2025). Validating student AI competency self-efficacy (SAICS) scale and its framework. Education Tech Research Dev. https://doi.org/10.1007/s11423-025-10512-y

q1 · i? · design · r3

A conceptual argument in the literature-review section, not a tested result. The authors reason that self-reported items capture feelings and thinking, and conclude that "both AI self-efficacy and AI literacy should be included in a more comprehensive assessment of student learning."

"However, a self-reported approach can only examine participants' perceptions of using and evaluating AI (i.e., AI self-efficacy) but not their abilities and skills (i.e., AI literacy). AI literacy should be measured by tests or examinations similar to international literacy assessment programs"

Discussion

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