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A paradox of AI fluency

arXiv cs.CL / 4/29/2026

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Key Points

  • The study examines how user proficiency with AI affects the quality and nature of outputs, using 27K annotated transcripts from WildChat-4.8M.
  • Fluent users tend to tackle more complex tasks and use an active, collaborative interaction style that iteratively refines goals and critically evaluates responses.
  • This produces a paradox: fluent users experience more visible failures, yet they are more likely to partially recover and achieve greater success on complex tasks.
  • In contrast, novices are more prone to “invisible failures,” where conversations look successful but actually miss the intended outcome.
  • The findings suggest that success depends not only on the model but also on encouraging active user engagement, and that AI builders should design for user behavior, not just friction-free delivery.

Abstract

How much does a user's skill with AI shape what AI actually delivers for them? This question is critical for users, AI product builders, and society at large, but it remains underexplored. Using a richly annotated sample of 27K transcripts from WildChat-4.8M, we show that fluent users take on more complex tasks than novices and adopt a fundamentally different interactional mode: they iterate collaboratively with the AI, refining goals and critically assessing outputs, whereas novices take a passive stance. These differences lead to a paradox of AI fluency: fluent users experience more failures than novices -- but their failures tend to be visible (a direct consequence of their engagement), they are more likely to lead to partial recovery, and they occur alongside greater success on complex tasks. Novices, by contrast, more often experience invisible failures: conversations that appear to end successfully but in fact miss the mark. Taken together, these results reframe what success with AI depends on. Individuals should adopt a stance of active engagement rather than passive acceptance. AI product builders should recognize that they are designing not just model behavior but user behavior; encouraging deep engagement, rather than friction-free experiences, will lead to more success overall. Our code and data are available at https://github.com/bigspinai/bigspin-fluency-outcomes

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