What question did this study set out to answer?

This paper aims to explore how user corrections impact the depth of responses from conversational AI systems.

February 9, 2026Open Access

Conditional Engagement, Authority Compression, and Response Contraction in Conversational AI Systems

Puntos clave

This paper aims to explore how user corrections impact the depth of responses from conversational AI systems.
Document real-world interaction with an AI system
Analyze behavioral changes post-user correction
Identify psychological mechanisms and consequences of engagement dynamics
Conversational AI exhibited reduced response depth after user corrections
Engagement levels declined significantly following pushback
Identified authority compression and negative punishment effects on user experience

Resumen

This white paper documents a real interaction in which a conversational AI system reduced response depth and engagement following user correction. The behavioral contraction introduced conditional engagement dynamics, authority compression, and functional negative punishment effects. The paper identifies the psychological mechanisms involved and accounts for the asymmetric human costs associated with response contraction after pushback.

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