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arXiv · 2601.04461

Users Mispredict Their Own Preferences for AI Writing Assistance

Vivian Lai·Zana Buçinca·Nil-Jana Akpinar·Mo Houtti·Hyeonsu B. Kang·Kevin Chian·Namjoon Suh·Alex C. Williams
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Abstract

Proactive AI writing assistants need to predict when users want drafting help, yet we lack empirical understanding of what drives preferences. Through a factorial vignette study with 50 participants making 750 pairwise comparisons, we find compositional effort dominates decisions ($ρ= 0.597$) while urgency shows no predictive power ($ρ\approx 0$). More critically, users exhibit a striking perception-behavior gap: they rank urgency first in self-reports despite it being the weakest behavioral driver, representing a complete preference inversion. This misalignment has measurable consequences. Systems designed from users' stated preferences achieve only 57.7\% accuracy, underperforming even naive baselines, while systems using behavioral patterns reach significantly higher 61.3\% ($p < 0.05$). These findings demonstrate that relying on user introspection for system design actively misleads optimization, with direct implications for proactive natural language generation (NLG) systems.

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Vivian Lai, Zana Buçinca, Nil-Jana Akpinar, Mo Houtti, Hyeonsu B. Kang, Kevin Chian, Namjoon Suh, Alex C. Williams. 2026-01-08. Users Mispredict Their Own Preferences for AI Writing Assistance. https://arxiv.org/abs/2601.04461

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