Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918416141516800 |
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| author | Sharma, Nikhil Zhang, Zheng Lee, Daniel Krishnan, Namita Ren, Guang-Jie Xiao, Ziang Li, Yunyao |
| author_facet | Sharma, Nikhil Zhang, Zheng Lee, Daniel Krishnan, Namita Ren, Guang-Jie Xiao, Ziang Li, Yunyao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01405 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents Sharma, Nikhil Zhang, Zheng Lee, Daniel Krishnan, Namita Ren, Guang-Jie Xiao, Ziang Li, Yunyao Human-Computer Interaction 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. |
| title | Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2602.01405 |