为认知障碍者设计更易用的AI交互，打破聊天框局限。
Thinking Outside the [Chat]Box: Bridging Computer Science and Industrial Design for Cognitive-Inclusive Generative AI
![论文配图：Thinking Outside the [Chat]Box: Bridging Computer Science and Industrial Design for Cognitive-Inclusive Generative AI](https://arxiv.org/html/2606.14306v1/modoConstructorGUIa.png)
- 跨学科合作提出新型交互框架，分结构与体验双层支持。
- 实测发现用户需逐步引导、响应分段处理和多模态反馈。
- 适合残障辅助、人机交互设计及包容性AI研究者参考。
当前生成式AI界面主要依赖聊天框交互，对智力障碍群体造成高认知负担，表现为提示词构建困难、回复信息过载及可靠性评估机制缺失。为探索更具认知包容性的交互模式，我们组织计算机科学与工业设计两组学生开展跨学科联合设计挑战，基于相同功能需求（如提示词支架、结构化输出、图形界面精炼、透明度与个性化）开发界面概念。对比发现：双方均聚焦于初始校准、主动提示和响应片段直接操作等基础要素；计算机科学组侧重结构化支撑，强调可预测性、可导航性与信任，通过可靠性标识、显式来源和长对话上下文管理实现；工业设计组则注重体验支撑，关注节奏控制、注意力引导、多模态表达与主动性，包括分步响应流程、专注模式与类助手集成。研究提炼出双层支架框架，拓展了非聊天中心的生成式AI交互设计空间，并推动后续在专家优化、技术可行性与智力障碍用户实证验证方面的研究。
原文摘要 · Abstract (English)
Current Generative AI (GenAI) interfaces remain largely constrained to chatbox interaction, which can impose high cognitive demands on users and create substantial barriers for people with intellectual disabilities (ID), including prompt formulation difficulties, response overload, and limited mechanisms to assess information reliability. To explore alternative interaction models for cognitive accessibility, we conducted a cross-disciplinary co-design challenge in which two student cohorts (Computer Science and Industrial Design) developed interface concepts from the same set of functional requirements (e.g., prompt scaffolding, structured output, GUI-based refinement, transparency, and personalization). Comparing the resulting proposals reveals both convergence on foundational requirements (notably initial calibration, proactive prompting, and direct manipulation of response fragments) and complementary contributions that outline a multi-layered support system. Computer Science teams primarily produced structural scaffolding, emphasizing predictability, navigability, and trust through mechanisms such as reliability indicators, explicit sources, and context management for long conversations. Industrial Design teams emphasized experiential scaffolding, focusing on pacing, attention guidance, multimodality, and proactive agency, including step-by-step response flows, focus modes, and assistant-like integrations. We synthesize these findings into a dual-layer scaffolding framework that expands the design space for cognitively accessible GenAI interaction beyond chat-centric models and motivates future work on expert refinement, technical feasibility, and empirical validation with users with ID.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。