用AI辅助设计更易用的适配认知障碍者的移动学习系统
Developing a UXR Point of View for Cognitive Accessibility in Mobile Learning with Generative AI
- 结合用户体验研究与大模型分析，构建可落地的需求框架
- 发现多数问题源于需求模糊，而非界面设计本身
- 产出9张实用卡片，助力跨学科团队高效协作
本研究探讨如何将用户体验研究（UXR）原则与大语言模型（LLM）支持的分析相结合，以提升面向认知障碍学习者的移动学习系统的需求质量。基于UXR观点（PoV）金字塔方法论，研究历经四个阶段：心理、行为与设计层的基础构建；采用DeLone和McLean信息系统成功模型及质量功能展开（QFD）进行结构化验证；通过开发九张认知无障碍UXR游戏卡整合洞察；以及为利益相关方制定特定观点以促进跨学科沟通。在人工监督下，引入LLM支持的主题聚类、需求优化与假设生成。结果表明，移动学习中的可用性与参与度问题多由需求不明确或描述不足引起，而非仅界面设计所致。通过将认知无障碍原则融入可测量、可追溯的技术需求，所提出的认知无障碍UXR手册为理论、系统架构与利益相关方战略提供了结构化对齐路径。
原文摘要 · Abstract (English)
This study investigates how UX research (UXR) principles, combined with Large Language Model (LLM)-supported analysis, can be used to improve the quality of requirements for mobile learning systems designed for learners with cognitive disabilities. Using the UXR Point-of-View (PoV) pyramid as a methodological framework, the study progressed through four stages: foundational structuring of psychological, behavioral, and design layers; structured validation using the DeLone and McLean Information Systems Success Model and Quality Function Deployment (QFD); insight consolidation through the development of nine Cognitive Accessibility UXR Play Cards; and stakeholder-specific PoV articulation to support interdisciplinary communication. LLM-supported synthesis was integrated to assist in theme clustering, requirement refinement, and hypothesis formulation under human oversight. Findings suggest that many usability and engagement challenges in mobile learning originate from ambiguous or under-specified requirements rather than interface design alone. By embedding cognitive accessibility principles into measurable and technically traceable requirements, the proposed Cognitive Accessibility UXR Playbook provides a structured pathway for aligning theory, system architecture, and stakeholder strategy.
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