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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2506.03052 |
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| _version_ | 1866908391683653632 |
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| author | Nguyen, Hannah Vy Yen, Yu-Chun Grace Shakir, Omar Huynh, Hang Gutierrez, Sebastian Smith, June A. Jimenez, Sheila Abdelgelil, Salma MacNeil, Stephen |
| author_facet | Nguyen, Hannah Vy Yen, Yu-Chun Grace Shakir, Omar Huynh, Hang Gutierrez, Sebastian Smith, June A. Jimenez, Sheila Abdelgelil, Salma MacNeil, Stephen |
| contents | Many conversational user interfaces facilitate linear conversations with turn-based dialogue, similar to face-to-face conversations between people. However, digital conversations can afford more than simple back-and-forth; they can be layered with interaction techniques and structured representations that scaffold exploration, reflection, and shared understanding between users and AI systems. We introduce Feedstack, a speculative interface that augments feedback conversations with layered affordances for organizing, navigating, and externalizing feedback. These layered structures serve as a shared representation of the conversation that can surface user intent and reveal underlying design principles. This work represents an early exploration of this vision using a research-through-design approach. We describe system features and design rationale, and present insights from two formative (n=8, n=8) studies to examine how novice designers engage with these layered supports. Rather than presenting a conclusive evaluation, we reflect on Feedstack as a design probe that opens up new directions for conversational feedback systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03052 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Feedstack: Layering Structured Representations over Unstructured Feedback to Scaffold Human AI Conversation Nguyen, Hannah Vy Yen, Yu-Chun Grace Shakir, Omar Huynh, Hang Gutierrez, Sebastian Smith, June A. Jimenez, Sheila Abdelgelil, Salma MacNeil, Stephen Human-Computer Interaction Many conversational user interfaces facilitate linear conversations with turn-based dialogue, similar to face-to-face conversations between people. However, digital conversations can afford more than simple back-and-forth; they can be layered with interaction techniques and structured representations that scaffold exploration, reflection, and shared understanding between users and AI systems. We introduce Feedstack, a speculative interface that augments feedback conversations with layered affordances for organizing, navigating, and externalizing feedback. These layered structures serve as a shared representation of the conversation that can surface user intent and reveal underlying design principles. This work represents an early exploration of this vision using a research-through-design approach. We describe system features and design rationale, and present insights from two formative (n=8, n=8) studies to examine how novice designers engage with these layered supports. Rather than presenting a conclusive evaluation, we reflect on Feedstack as a design probe that opens up new directions for conversational feedback systems. |
| title | Feedstack: Layering Structured Representations over Unstructured Feedback to Scaffold Human AI Conversation |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2506.03052 |