Affinity for technology interaction differed across clusters, with text reproducers highest and integrators scoring higher than reconstructors (d = 0.59)¶
Claim · All claims
Evidence · 1 study · associationalr2·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 ATI scores were significantly higher for text integrators (M = 3.88, SD = 0.96) than text reconstructors (M = 3.36, SD = 0.80), t(119) = −3.21, p = 0.002, d = 0.59; reproducers had the highest ATI (M = 4.08). → 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 · r2
Comparison of Affinity for Technology Interaction scores (German ATI scale) across clusters in the n=131 study; "significantly higher for text integrators" with d = 0.59. Text reproducers had the highest ATI score, measured descriptively.
"An independent samples t-test showed that ATI scores were significantly higher for text integrators (M = 3.88, SD 0.96) than for text reconstructors (M = 3.36, SD = 0.80) (t(119) = − 3.21, p = 0.002, 95% CI [− 0.83, − 0.20], d = 0.59)."
Discussion¶
Related Claims¶
- LLM learners are younger, more likely male, and score higher on technology affinity than non-learners — related
- AI-supported argumentative writing yields three distinct uptake clusters (text reproducers, text integrators, text reconstructors) based on recurring n-gram overlap — related
- Current ChatGPT users were overrepresented among text reconstructors and underrepresented among text integrators (Cramér's V = 0.26) — related
- Affinity propagation clustering of gameplay attempts yielded an interpretable six-cluster solution (part.6)
- Text reproducers copied nearly all of their final text from ChatGPT with long verbatim sequences, while text integrators showed fragment-level micro-uptake — related
- Clusters did not differ significantly in the number of prompts sent to ChatGPT — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
ati-differs-across-uptake-clusters |
title |
Affinity for technology interaction differed across clusters, with text reproducers highest and integrators scoring higher than reconstructors (d = 0.59) |
description |
Affinity for technology interaction differed across clusters, with text reproducers highest and integrators scoring higher than reconstructors (d = 0.59) |
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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