The Adaptation Paradox: Agency vs. Mimicry in Companion Chatbots
This research addresses the problem of designing effective companion chatbots for users, offering practical insights to improve connection, though it is incremental in refining existing design principles.
The study investigated how companion chatbots can foster genuine connection by comparing user authorship (avatar generation) and covert mimicry (adaptive language style matching). It found that user-generated avatars increased rapport, while adaptive mimicry paradoxically reduced personalization and satisfaction, termed the Adaptation Paradox.
Generative AI powers a growing wave of companion chatbots, yet principles for fostering genuine connection remain unsettled. We test two routes: visible user authorship versus covert language-style mimicry. In a preregistered 3x2 experiment (N = 162), we manipulated user-controlled avatar generation (none, premade, user-generated) and Language Style Matching (LSM) (static vs. adaptive). Generating an avatar boosted rapport ($ω^2$ = .040, p = .013), whereas adaptive LSM underperformed static style on personalization and satisfaction (d = 0.35, p = .009) and was paradoxically judged less adaptive (t = 3.07, p = .003, d = 0.48). We term this an Adaptation Paradox: synchrony erodes connection when perceived as incoherent, destabilizing persona. To explain, we propose a stability-and-legibility account: visible authorship fosters natural interaction, while covert mimicry risks incoherence. Our findings suggest designers should prioritize legible, user-driven personalization and limit stylistic shifts rather than rely on opaque mimicry.