Current ChatGPT users were overrepresented among text reconstructors and underrepresented among text integrators (Cramér's V = 0.26)¶
Claim · All claims
Evidence · 1 study · associationalr1·q3·i2medium
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 i2 ChatGPT use across clusters showed a significant association, Χ²(2, N = 121) = 7.98, p = 0.019, with standardized residuals z = ±2.81 for reconstructors vs. integrators. → Teresa Luther · Kate Belcher · Joachim Kimmerle · Detmar Meurers · Ulrike Cress 2026
Evidence¶
Teresa Luther · Kate Belcher · Joachim Kimmerle · Detmar Meurers · Ulrike Cress 2026¶
Teresa Luther · Kate Belcher · Joachim Kimmerle · Detmar Meurers · Ulrike Cress (2026). Collaborative knowledge construction with generative AI: Exploring argumentative co-writing processes through n-gram and cluster analysis. Intern. J. Comput.-Support. Collab. Learn. https://doi.org/10.1007/s11412-026-09470-1
q3 · i2 · associational · r1
Chi-square test of ChatGPT use categories across clusters (prior/current/never) in the n=131 sample, testing only Clusters 2 and 3; a "significant effect" with Cramér's V = 0.26. Current users were significantly overrepresented in text reconstructors (z = 2.81).
"Our analysis on the use of ChatGPT across text integrators and text reconstructors revealed a significant effect: Χ 2 (2, N = 121) = 7.98, p = 0.019, with a moderate to large effect size (Cramér's V = 0.26)."
Discussion¶
Related Claims¶
- Affinity for technology interaction differed across clusters, with text reproducers highest and integrators scoring higher than reconstructors (d = 0.59) — related
- AI-supported argumentative writing yields three distinct uptake clusters (text reproducers, text integrators, text reconstructors) based on recurring n-gram overlap — a broader claim this one bears on
- Clusters did not differ significantly in the number of prompts sent to ChatGPT — related
- Text reproducers copied nearly all of their final text from ChatGPT with long verbatim sequences, while text integrators showed fragment-level micro-uptake — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
current-chatgpt-use-associated-with-reconstructors |
title |
Current ChatGPT users were overrepresented among text reconstructors and underrepresented among text integrators (Cramér's V = 0.26) |
description |
Current ChatGPT users were overrepresented among text reconstructors and underrepresented among text integrators (Cramér's V = 0.26) |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
generated |
by process:wiki-ingest on 2026-10-18 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — teresa-luther-kate-belcher-joachim-kimmerle-detmar-meurers-ulrike-cress-2026 |
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