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Feedforward in Generative AI: Opportunities for a Design Space

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arxiv 2502.14229 v2 pith:74MUXDPJ submitted 2025-02-20 cs.HC

classification cs.HC
keywords genaifeedforwardacrossdesignsystemsuserscontextsdesigns
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Generative AI (GenAI) models have become more capable than ever at augmenting productivity and cognition across diverse contexts. However, a fundamental challenge remains as users struggle to anticipate what AI will generate. As a result, they must engage in excessive turn-taking with the AI's feedback to clarify their intent, leading to significant cognitive load and time investment. Our goal is to advance the perspective that in order for users to seamlessly leverage the full potential of GenAI systems across various contexts, we must design GenAI systems that not only provide informative feedback but also informative feedforward -- designs that tell users what AI will generate before the user submits their prompt. To spark discussion on feedforward in GenAI, we designed diverse instantiations of feedforward across four GenAI applications: conversational UIs, document editors, malleable interfaces, and automation agents, and discussed how these designs can contribute to a more rigorous investigation of a design space and a set of guidelines for feedforward in all GenAI systems.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. REVA: Supporting LLM-Generated Programming Feedback Validation at Scale Through User Attention-based Adaptation

    cs.HC 2025-07 conditional novelty 6.0 of 10

    REVA uses instructors' highlighting and edits to reorder AI-generated feedback reviews and propagate revisions, and a 12-instructor lab study reports higher feedback precision and recall than a baseline without these ...

  2. Interaction as Intelligence: Deep Research With Human-AI Partnership

    cs.CL 2025-07 reject novelty 5.0 of 10

    A human-in-the-loop deep research system with transparent, interruptible interaction is claimed to outperform commercial baselines, but the evidence is weakened by small samples and biased instructions.

  3. Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop

    cs.HC 2025-08 conditional novelty 4.0 of 10

    A synthesis of the CHI 2025 workshop maps research and design opportunities for understanding, protecting, and augmenting human cognition with generative AI.

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