用生成式AI增强用户体验研究，为注意力缺陷多动症人群设计更包容的情绪调节工具。
UXR PoV for Neuroinclusive Emotion Regulation
- 结合心理理论与生成式AI，构建四阶段用户研究流程。
- 产出10张基于理论的可操作设计卡片，指导神经多样性友好系统设计。
- 适合数字心理健康、人机交互及神经多样性研究者参考。
注意力缺陷/多动症（ADHD）是一种精神障碍，表现为注意力不集中、多动和冲动行为，以及决策和情绪调节困难。尽管数字化和基于AI的干预措施扩大了情绪调节支持的可及性，但现有系统仍受限于理论整合不足、未能充分考虑神经多样性差异，以及缺乏结构化的用户体验研究（UXR）方法，难以连接心理学洞见与设计实践。本文提出一种基于UXR观点（PoV）手册的生成式AI增强型用户体验研究方法，旨在支持为成年ADHD患者设计具有情感智能且神经包容性的数字情绪调节干预。该方法融合实证证据与既有的心理学框架——辩证行为疗法（DBT）、自我决定理论（SDT）和COM-B行为模型，并利用生成式AI作为共分析工具，辅助假设生成、综合分析与设计表达。通过四个阶段的用户研究流程——AI支持的假设生成、基础规划、通过建构模块生成洞察，以及构建利益相关者特定的视角叙事——最终形成10张理论驱动的UXR Play Cards，将心理机制与实证发现转化为具体的设计指导。本研究的核心贡献在于建立了一个可复现、具备偏见意识的生成式AI与用户体验研究融合框架，推动以人为本且神经包容的数字心理健康设计发展。
原文摘要 · Abstract (English)
Attention-deficit/hyperactivity disorder (ADHD) is a psychiatric disorder which presents itself in individuals through patterns of developmentally inappropriate levels of inattentiveness, hyperactivity, and impulsivity, with difficulties in decision making and emotional regulation (ER). Although digital and AI-based interventions have expanded access to ER support, many existing systems remain limited by weak theoretical integration, insufficient accommodation of neurodiversity, and a lack of structured user experience research (UXR) methodologies, that bridge psychological insight with design practice. This paper introduces a Generative AI-augmented UXR methodology, grounded in the UXR Point of View (PoV) Playbook, to support the design of emotionally intelligent and Neuroinclusive digital ER interventions for adults with ADHD. The approach integrates empirical evidence with established psychological frameworks Dialectical Behaviour Therapy (DBT), Self-Determination Theory (SDT), and the COM-B behavioural model and leverages Generative AI as a co-analytic tool to support synthesis, hypothesis formation, and design articulation. The methodology is operationalized through a four-stage UXR process encompassing AI-supported hypothesis generation, foundational planning, insight generation via Building Blocks, and the construction of stakeholder-specific PoV narratives. This process results in a set of ten theory informed UXR Play Cards that translate psychological mechanisms and empirical findings into actionable design guidance. The primary contribution of this work is a replicable, bias-aware framework for integrating Generative AI into UXR practice, advancing human-centred and Neuroinclusive approaches to digital mental health design.
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