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.

翻译：当前生成式人工智能（GenAI）界面仍主要局限于聊天框交互模式，这对用户造成较高认知负荷，尤其对智力障碍群体形成显著使用障碍，具体包括提示词构建困难、信息响应超载以及缺乏评估信息可靠性的有效机制。为探索面向认知可及性的替代交互模型，我们开展了一项跨学科协同设计挑战，由计算机科学与工业设计两个专业的学生团队基于相同功能需求集（如提示词脚手架、结构化输出、基于图形界面的精炼机制、透明度与个性化）分别开发界面概念。对比分析最终方案表明，双方在基础需求（特别是初始校准、主动提示与响应片段直接操作）上存在趋同性，同时各自提出互补性贡献，共同勾勒出多层支持系统框架。计算机科学团队主要产出结构化脚手架，通过可靠性指标、显式信源标注及长对话上下文管理等机制，强调结果可预测性、导航清晰性与用户信任建立。工业设计团队则聚焦体验型脚手架，着重于节奏控制、注意力引导、多模态交互与主动代理能力，包括分步响应流程、专注模式及类助手集成方案。我们将研究结果综合为双层脚手架框架，该框架将认知可及性GenAI交互的设计空间扩展至超越聊天中心模型，并为后续专家精炼、技术可行性验证及针对智力障碍群体的实证研究奠定基础。