DAIMON: Designing AI-Augmented Research Dashboards to Enable Novel Human-AI Collaborative Workflows in Longitudinal Sensing Studies

Researchers conduct longitudinal passive sensing studies in in-the-wild settings, often spanning months or years, to uncover naturalistic behavioral patterns. These studies are not “set-and-forget” deployments; they require continuous monitoring as technical failures and declining participant compliance can lead to substantial missing data, undermining study validity and downstream models. Thus, conducting these studies involves multiple detail-oriented, cognitively demanding, and time-consuming tasks, making it a burdensome and stressful process. Existing research dashboards, the primary tools for data monitoring, offer limited support in easing this burden. Leveraging recent advances in AI for passive sensing data, we explore the design of human-AI collaborative workflows enabled through research dashboards to improve the effectiveness and efficiency of monitoring and associated tasks. We begin with a co-design study with 13 researchers involved in longitudinal sensing studies to identify desired AI capabilities and interactions through semi-structured interviews, brainstorming, and sketching activities. We operationalize novel human-AI workflows our participants envisioned by implementing an AI-augmented dashboard prototype DAIMON , and use it as a research probe in two studies: a task-based study and a deployment within an ongoing real-world sensing study. Our findings demonstrate the promise of AI-augmented dashboards in supporting researchers' day-to-day data monitoring and decision-making tasks. It also surfaces concerns around transparency and expectations with AI systems. Consolidating insights across all three studies, we present design guidelines for AI-augmented dashboards for longitudinal passive sensing research and discuss directions for future work.