Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies· 2026Q1
DAIMON: Designing AI-Augmented Research Dashboards to Enable Novel Human-AI Collaborative Workflows in Longitudinal Sensing Studies
- 2citations
- Q1SCImago
- 2026year
Short summary
DAIMON is a novel AI-augmented dashboard prototype designed to streamline monitoring and data management in longitudinal passive sensing studies, reducing researcher burden.
AI-generated from the title and abstract; the full text is not read.
Key points
- Longitudinal passive sensing studies are prone to missing data, undermining validity and requiring intensive researcher effort.
- DAIMON is an AI-augmented dashboard prototype developed through co-design with researchers to improve monitoring and data management.
- The prototype operationalizes human-AI collaborative workflows envisioned by researchers.
- Evaluations show DAIMON's potential to support researchers' tasks but also reveal concerns about AI transparency and expectations.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Computer Science Applications
Computer Science ApplicationsComputer Science