Overall demand–AI-use alignment varies modestly across education systems, but task-specific association profiles show clear residual heterogeneity¶
Claim · All claims
Evidence · 1 study · associationalr2·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2031: baselines and conditions change
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.
q2 i? The configuration of slopes across tasks shows residual heterogeneity (QE(135) = 229.57, p < .001), with heterogeneity largest for assessment/marking (I² = 48.7%) and special education (I² = 39.4%) and least for planning (I² = 11.8%). → Holster 2026
Evidence¶
Holster 2026¶
Holster, J. (2026). Where Artificial Intelligence Enters Teacher Work. https://www.oecd.org/en/data/datasets/talis-2024-database.html doi:10.35542/osf.io/a39rp_v1
q2 · i? · associational · r2
Multivariate random-effects model retaining within-system covariance among the three task slopes; task-specific I² values were 11.8% (planning), 48.7% (marking), and 39.4% (special education), and leave-one-system-out ranges were narrow.
"More importantly, the configuration of the association across tasks showed clear residual heterogeneity, QE(135) = 229.57, p < .001."
Discussion¶
Related Claims¶
- The within-teacher demand–AI-use association differs sharply across tasks, with only a modest positive planning gradient — related
- Special-education support/adaptation shows the strongest demand–AI-use alignment, with a 16.32-percentage-point increase in predicted use across a two-point relative-demand contrast — related
- Assessment/marking shows an inverse demand–AI-use association in the primary system-average analysis, with predicted use falling 3.47 percentage points across a two-point relative-demand contrast — related
- Teachers experiencing higher average demand across lesson planning, assessment/marking, and special-education adaptation report higher odds of AI adoption — related
- The prespecified task–demand mapping produces the strongest within-teacher alignment of all six possible one-to-one assignments, supporting task-specific rather than arbitrary demand alignment — related
- Among AI-using teachers, tasks experienced as relatively more demanding are more likely to be AI-supported overall — related
- AI-related professional learning does not clearly moderate the within-teacher demand–AI allocation association — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
cross-system-heterogeneity-task-specific-ai-profiles |
title |
Overall demand–AI-use alignment varies modestly across education systems, but task-specific association profiles show clear residual heterogeneity |
description |
Overall demand–AI-use alignment varies modestly across education systems, but task-specific association profiles show clear residual heterogeneity |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
recheck_by |
2031 |
generated |
by process:wiki-ingest on 2026-10-12 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — holster-2026 |
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