Designing KRIYA: An AI Companion for Wellbeing Self-Reflection
This addresses the issue of performance anxiety in wellbeing apps for users by offering a complementary, reflective approach, though it is incremental as it builds on existing AI companion concepts.
The researchers tackled the problem of users struggling to derive meaningful insights from summative wellbeing dashboards by designing KRIYA, an AI companion that promotes self-reflection through co-interpretive engagement, finding that it helped users reframe data interaction as interpretation rather than performance in a study with 18 college students.
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.