Feedstack: Layering Structured Representations over Unstructured Feedback to Scaffold Human AI Conversation

2025-06-03

AI Summary

Current human–AI dialogue feedback systems lack mechanisms to support deep reflection and shared understanding between users and AI. Method: This study proposes a hierarchical interaction architecture that overlays structured representations onto unstructured dialogue streams, enabling users to organize, navigate, and externalize feedback. It innovatively integrates the design probe paradigm to enhance exploratory and reflective capabilities, and combines research-driven design, hierarchical interface modeling, and feedback structural encoding in two formative user studies (n = 16). Contribution/Results: The approach significantly improves novice designers’ accuracy in articulating design intent and their ability to identify core design principles. Findings provide both a novel conceptual pathway and empirical grounding for developing next-generation dialogue feedback systems that are explainable, traceable, and supportive of collaborative cognition.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Enhancing feedback conversations with structured layered representations
• Scaffolding exploration and shared understanding in human-AI dialogue
• Investigating interface design for organizing and externalizing feedback

Innovation

• Layered structured representations over feedback
• Augments conversations with navigation affordances
• Research-through-design approach for feedback systems