Learners show stronger short-term syntactic priming with the AI partner, significant across all three recency-window operationalizations including one equalizing input volume¶

Claim · All claims
Evidence · 1 study · causal r2 · q3 · i3 large
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i3 Recency-windowed syntactic uptake was significantly higher with the AI across turn-based (β=3.18), time-based (β=3.04), and template-count (β=2.84) windows, including the window explicitly equalizing input volume; descriptive cumulative uptake rate was much higher with the AI (d=0.93). → Russell Scheinberg 2026

Evidence¶

Russell Scheinberg 2026¶

Russell Scheinberg, Ameeta Agrawal, Tetyana Sydorenko, Kalab Kahsay, Nina Vyatkina, Griet Boone. (2026). What Changes When the Interlocutor Is an AI? Interactional Fluency and Linguistic Uptake in L2 Spoken Dialogue. https://arxiv.org/abs/2606.22225 doi:10.48550/arxiv.2606.22225

q3 · i3 · causal · r2

Mixed-effects regression on 156 participant-level dialogue records (78 AI, 78 human-partner from 39 dyads). Descriptive cumulative uptake rate was higher with the AI (M=.16 vs. .08, d=0.93) but cumulative uptake after controls was only a nonsignificant trend (β=1.55, p=.090).

"the AI advantage was significant across all three operationalizations—turn-based (β= 3.18,p < .001), time-based (β= 3.04, p < .001), and template-count (β= 2.84,p=.001)—including the window that explicitly equalizes input volume."

Discussion¶

Related Claims¶

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