Claims

Unscaffolded GenAI access produced no significant advantage in pre-service teachers' AI readiness compared with traditional learning

Claim · All claims
Evidence · 1 study · causal r2 · q3 · i0 negligible
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

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.

q3 i0 After controlling for pretest scores, adjusted posttest AI readiness did not differ significantly between the experimental and control groups, with a negligible effect size. → Zhang Jun 2026

Evidence

Zhang Jun 2026

Zhang Jun, Peng Yuting, Deng Xinyue, Zeng Qin, Wang Kai. (2026). The Impact of Unscaffolded GenAI Use on Pre-Service Teachers’ AI Readiness, Self-Regulated Learning, Critical Thinking, and Instructional Design Performance: A Quasi-Experimental Study. Behavioral Sciences. https://doi.org/10.3390/bs16071114

q3 · i0 · causal · r2

One-way ANCOVA on AI readiness (18-item scale, four dimensions) with pretest as covariate in the quasi-experiment of 52 pre-service chemistry teachers; adjusted means were 4.07 (experimental) and 4.11 (control). The article reports "F = 0.198, p = 0.658, η2 = 0.004, with a negligible effect size".

"The ANCOVA analysis showed no significant difference between the experimental and control groups, F = 0.198, p = 0.658, η2 = 0.004, with a negligible effect size."

Discussion

Page metadata
Field Value
type claim
id unscaffolded-genai-no-ai-readiness-advantage
title Unscaffolded GenAI access produced no significant advantage in pre-service teachers' AI readiness compared with traditional learning
description Unscaffolded GenAI access produced no significant advantage in pre-service teachers' AI readiness compared with traditional learning
status review
evidence_strength moderate
newest_evidence 2026
generated by process:wiki-ingest on 2026-10-12
trust unverified — no one has confirmed this page's content
sources 1 — zhang-jun-2026

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