跳出聊天框：融合计算机科学与工业设计的认知包容性生成式人工智能
Thinking Outside the [Chat]Box: Bridging Computer Science and Industrial Design for Cognitive-Inclusive Generative AI
AI总结：
针对当前GenAI界面认知门槛高、对智力障碍者不友好等问题，通过跨学科设计挑战，提出融合计算机科学的结构化支架与工业设计的体验式支架的双层框架，以扩展认知包容性交互设计空间。
摘要：
当前的生成式人工智能（GenAI）界面仍然主要局限于聊天框交互，这给用户带来了高认知负担，并对智力障碍（ID）人群造成了重大障碍，包括提示词表述困难、响应过载以及评估信息可靠性的机制有限。为了探索认知无障碍的替代交互模型，我们进行了一项跨学科协同设计挑战，其中两个学生群体（计算机科学和工业设计）从相同的功能需求集（例如，提示词支架、结构化输出、基于GUI的细化、透明度和个性化）出发，开发界面概念。比较最终提案揭示了在基础需求上的趋同（特别是初始校准、主动提示和响应片段的直接操作）以及互补性贡献，勾勒出一个多层次支持系统。计算机科学团队主要产生结构支架，强调通过可靠性指标、明确来源和长对话上下文管理等机制实现可预测性、可导航性和信任。工业设计团队强调体验支架，侧重于节奏、注意力引导、多模态和主动代理，包括逐步响应流程、专注模式和类似助手的集成。我们将这些发现综合为一个双层支架框架，该框架将认知无障碍GenAI交互的设计空间扩展到以聊天为中心的模式之外，并激励未来在专家细化、技术可行性和与ID用户进行实证验证方面的工作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
