用AI助手引导用户反思健康数据，避免焦虑和比较。
Designing KRIYA: An AI Companion for Wellbeing Self-Reflection
- 设计AI陪伴系统，通过共情式对话引导自我反思。
- 用户将数据互动视为理解而非绩效考核，减轻压力。
- 适合关注心理健康与自我成长的用户使用。
大多数个人健康应用仅提供健康与运动数据的总结性仪表盘，但许多用户难以从中获得有意义的理解。这些应用通常通过目标、提醒和结构化指标来促进参与，反而可能加剧比较、评判和表现焦虑。为探索一种更注重自我反思的替代方案，我们设计了KRIYA——一个支持用户共同解读个人健康数据的AI心理陪伴系统。KRIYA通过‘舒适区’、‘侦探模式’和‘如果规划’等功能，协助用户探讨问题、解释现象并设想未来情境。我们对18名大学生进行了半结构化访谈，使用假设数据测试原型。结果表明：用户将与数据的互动看作解释而非表现；情绪基调决定了反思是支持性还是压迫性；透明性有助于建立信任。研究讨论了支持好奇心、自我关怀与反思性认知的AI陪伴设计启示。
原文摘要 · Abstract (English)
Most personal wellbeing apps present summative dashboards of health and physical activity metrics, yet many users struggle to translate this information into meaningful understanding. These apps commonly support engagement through goals, reminders, and structured targets, which can reinforce comparison, judgment, and performance anxiety. To explore a complementary approach that prioritizes self-reflection, we design KRIYA, an AI wellbeing companion that supports co-interpretive engagement with personal wellbeing data. KRIYA aims to collaborate with users to explore questions, explanations, and future scenarios through features such as Comfort Zone, Detective Mode, and What-If Planning. We conducted semi-structured interviews with 18 college students interacting with a KRIYA prototype using hypothetical data. Our findings show that through KRIYA interaction, users framed engaging with wellbeing data as interpretation rather than performance, experienced reflection as supportive or pressuring depending on emotional framing, and developed trust through transparency. We discuss design implications for AI companions that support curiosity, self-compassion, and reflective sensemaking of personal health data.
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