Condition-specific mediation estimates showed a literacy–performance association in the reactive condition but not the proactive condition, with indirect effects not significant¶

Claim · All claims
Evidence · 1 study · causal r2 · q1
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? Exploratory condition-specific mediation found a literacy–performance association in the reactive condition but not the proactive condition; indirect effects and literacy-by-design interactions were not significant. → Yueqiao Jin 2025

Evidence¶

Yueqiao Jin 2025¶

Yueqiao Jin, Kaixun Yang, Roberto Martinez-Maldonado, Dragan Gašević and Lixiang Yan. (2025). The Agency Gap in AI-Supported Writing: How Reactive and Proactive Agent Designs Shape Multimodal Reasoning. https://arxiv.org/abs/2507.04398 doi:10.48550/arxiv.2507.04398

q1 · i? · causal · r2

Exploratory mediation analyses with 39 (reactive) and 40 (proactive) participants, using 5,000-resample bootstrap confidence intervals; the article treats the results as exploratory associational estimates and notes the pattern is consistent with smaller literacy-related differences under proactive scaffolding but does not establish a compensatory causal effect.

"Condition-specific mediationestimatesshowedaliteracy–performanceassociationinthereactiveconditionbutnot intheproactivecondition;however,theindirecteffectsandliteracy-by-designinteractionswere not significant."

Discussion¶

Related Claims¶

• No significant GenAI-literacy-by-agent-condition interaction emerged, so the formal models did not support different literacy slopes across designs — possibly the same claim (merge candidate)
• Proactive AI agent dialogues showed stronger conceptual–adequate reasoning links while reactive dialogues showed stronger factual/off-task associations — related