Designing KRIYA: An AI Companion for Wellbeing Self-Reflection

TL;DR

KRIYA is an AI wellbeing companion designed to foster meaningful self-reflection on personal health data, shifting focus from performance metrics to interpretive understanding and emotional support.

Contribution

The paper introduces KRIYA, a novel AI tool that promotes interpretive engagement and emotional reflection in wellbeing self-assessment, contrasting with traditional goal-oriented apps.

Findings

Users viewed wellbeing data as interpretive rather than performance-based.

Interaction with KRIYA fostered emotional framing of reflection as supportive or pressuring.

Transparency in KRIYA built user trust and facilitated self-compassion.

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.