Thinking Outside the [Chat]Box: Bridging Computer Science and Industrial Design for Cognitive-Inclusive Generative AI

2026-06-12

AI Summary

This study addresses the limitations of current generative AI systems, which rely heavily on chat-based interactions and impose high cognitive demands that exclude individuals with intellectual disabilities—particularly in prompt formulation, information processing, and credibility assessment. To bridge this gap, the authors propose a dual-layer interaction framework integrating structural scaffolds (e.g., reliability indicators, context management) and experiential scaffolds (e.g., pacing control, multimodal guidance). Developed through a collaborative design approach combining computer science and industrial design, this framework expands the design space for cognitively inclusive AI. The work identifies core mechanisms—including initial calibration, proactive prompting, and direct manipulation of response segments—to formulate an interaction paradigm tailored to cognitively diverse users, thereby laying the groundwork for expert refinement and empirical validation.

Abstract

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.

Problem

• Generative AI
• cognitive accessibility
• intellectual disabilities
• user interface
• chatbox interaction

Innovation

• cognitive accessibility
• generative AI
• co-design
• scaffolding framework
• inclusive interaction