Beyond the Hype: Mapping Uncertainty and Gratification in AI Assistant Use

TL;DR

This study explores user experiences with AI assistants, identifying uncertainties and unmet expectations that impact trust and satisfaction, and offers design and policy recommendations for improvement.

Contribution

It introduces a framework linking user uncertainties to gratification disruptions and proposes practical guidelines for transparent, user-controlled AI assistant design.

Findings

Three core types of user uncertainty identified: functional, interactional, social.

Unmet expectations often lead to user frustration or abandonment.

Transparency and user control are crucial for trustworthy AI assistants.

Abstract

This paper examines the gap between the promises and real-world performance of emerging AI personal assistants. Drawing on interviews with early adopters of devices like Rabbit R1 and Humane AI Pin, as well as services like Ohai and Docus, we map user experiences through the lens of Uses and Gratifications and Uncertainty Reduction Theory. We identify three core types of user uncertainty, functional, interactional, and social, and explore how each disrupts different user gratifications. We show that while marketing hype fuels initial adoption, unmet expectations often result in frustration or abandonment. Our findings highlight the importance of transparency, task-specific design, and user control over contextual memory and personalization. We provide design and policy recommendations, including user-facing explainability tools and calls for regulatory benchmarks such as CI Bench, to guide ethical and interpretable AI integration. Our study offers actionable insights for creating more usable, trustworthy, and socially aligned AI assistants.