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Preprint
Designing KRIYA: An AI Companion for Wellbeing Self-Reflection
ArXiv.org
21 Jan 2026
Abstract
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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Details
- Title
- Designing KRIYA: An AI Companion for Wellbeing Self-Reflection
- Creators
- Shanshan Zhu - University of Illinois at Urbana ChampaignWenxuan Song - University of Illinois at Urbana ChampaignJiayue Melissa Shi - University of Illinois at Urbana ChampaignDong Whi Yoo - Indiana University IndianapolisKarthik S Bhat - Drexel UniversityKoustuv Saha - University of Illinois at Urbana Champaign
- Publication Details
- ArXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Other Identifier
- 991022155549504721