The four-predictor model explains approximately 55.4% of variance in teachers' AI integration ability¶
Claim · All claims
Evidence · 1 study · associationalr1·q3
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: 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.
q3 i? The combination of AI Knowledge, Attitude Toward AI in ESD, ESD Teaching Material, and Use of AI explained R² = 0.554 of the variance in AI Integration Ability. → Riandi Riandi 2026
Evidence¶
Riandi Riandi 2026¶
Riandi Riandi, Ismail Ismail, Ida Kaniawati, Wahyu Sopandi, Diana Ayu Rostikawati, Defrizal Hamka & Suhendar Suhendar. (2026). Teacher involvement in developing sustainable education materials for AI integration in green energy education. Scientific Reports. https://doi.org/10.1038/s41598-025-34422-4
q3 · i? · associational · r1
Structural model evaluation in the PLS-SEM survey of 122 teachers reported R² = 0.554 (adjusted 0.539), which the authors characterise as moderate predictive power. R² is not a standardised effect size, so no impact magnitude is coded.
"An R2 value of 0.554 indicates that approximately 55.4% of the variance in AI Integration Ability is explained by the combination of four exogenous constructs: (1) AI Knowledge; (2) Attitude Toward AI in ESD; (3) ESD Teaching Material; and (4) Use of AI in Science Green Energy."
Discussion¶
Related Claims¶
- AI knowledge and attitudes toward AI did not significantly predict teachers' AI integration ability — related
- Teachers' practical use of AI in science and green energy learning significantly predicts their AI integration ability (β = 0.391, p < 0.01, f² = 0.180) — related
- Teacher involvement in developing ESD-based teaching materials significantly predicts AI integration ability (β = 0.236, p < 0.05, f² = 0.127) — related
- The model explains 62.50% of variance in AI teaching innovation behavior, with moderate predictive relevance and good fit — related
- Review reports that teachers' beliefs, attitudes and TPACK—not AI knowledge alone—mediate AI integration — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
plssem-model-explains-55-percent-variance-ai-integration |
title |
The four-predictor model explains approximately 55.4% of variance in teachers' AI integration ability |
description |
The four-predictor model explains approximately 55.4% of variance in teachers' AI integration ability |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — riandi-riandi-2026 |
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