Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents
High-quality feedback is essential for effective human-AI interaction. It bridges knowledge gaps, corrects digressions, and shapes system behavior; both during interaction and throughout model development. Yet despite its importance, human feedback to AI is often infrequent and low quality. This gap motivates a critical examination of human feedback during interactions with AIs. To understand and overcome the challenges preventing users from giving high-quality feedback, we conducted two studies examining feedback dynamics between humans and conversational agents (CAs). Our formative study, through the lens of Grice's maxims, identified four Feedback Barriers -- Common Ground, Verifiability, Communication, and Informativeness -- that prevent high-quality feedback by users. Building on these findings, we derive three design desiderata and show that systems incorporating scaffolds aligned with these desiderata enabled users to provide higher-quality feedback. Finally, we detail a call for action to the broader AI community for advances in Large Language Models capabilities to overcome Feedback Barriers.
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Nikhil Sharma
claimed_verified
2026-02-04T19:03:26.520Z
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Nikhil Sharma
nikhilsk
user
nikhilsk
69836c8637bca54cd0587d8a
Zheng Zhang
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2026-02-04T19:06:59.012Z
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Zheng Zhang
roryzhang95
user
roryzhang95
69836c8637bca54cd0587d8b
Daniel Lee
69836c8637bca54cd0587d8c
Namita Krishnan
69836c8637bca54cd0587d8d
Guang-Jie Ren
69836c8637bca54cd0587d8e
Ziang Xiao
69836c8637bca54cd0587d8f
Yunyao Li
Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents
High-quality feedback is essential for effective human-AI interaction. It bridges knowledge gaps, corrects digressions, and shapes system behavior; both during interaction and throughout model development. Yet despite its importance, human feedback to AI is often infrequent and low quality. This gap motivates a critical examination of human feedback during interactions with AIs. To understand and overcome the challenges preventing users from giving high-quality feedback, we conducted two studies examining feedback dynamics between humans and conversational agents (CAs). Our formative study, through the lens of Grice's maxims, identified four Feedback Barriers -- Common Ground, Verifiability, Communication, and Informativeness -- that prevent high-quality feedback by users. Building on these findings, we derive three design desiderata and show that systems incorporating scaffolds aligned with these desiderata enabled users to provide higher-quality feedback. Finally, we detail a call for action to the broader AI community for advances in Large Language Models capabilities to overcome Feedback Barriers.