In this UbiComp 2026 workshop, we are hoping to bring together researchers, clinicians, practitioners, and industry professionals to critically examine how artificial intelligence can responsibly augment ubiquitous computing systems for mental health, and to discuss the opportunities and risks this introduces for research, clinical practice, and real-world deployment.
Find Out MoreUbiquitous computing technologies (UbiComp) have long served as crucial tools for collecting behavioral, physiological, social, and environmental data to enable early symptom detection, deliver preventative interventions, and support ongoing symptom management. With over a decade of success in demonstrating the feasibility of using UbiComp technologies to support well-being and mental health in both general and clinical populations, the field is now witnessing a rapid integration of artificial intelligence (AI) and generative AI in particular into the sensing, inference, and intervention pipelines it has spent years developing. This convergence introduces new opportunities, such as personalized and context-sensitive interventions and scalable clinical decision support. However, it also introduces risks that the field has not yet systematically addressed, including model brittleness across populations and deployment contexts, hallucination in AI-generated clinical interpretations, and insufficient transparency for general and clinical accountability.
This workshop aims to bring together researchers, clinicians, practitioners, and industry professionals to collaboratively examine this convergence, identify shared research priorities, and develop community standards for responsible innovation at the intersection of UbiComp and AI in mental health. We are calling for papers that address these challenges from technical, clinical, or ethical perspectives. Building on ten years of success, we continue to support the UbiComp community in navigating the opportunities and risks that AI integration introduces into research, clinical practice, and real-world deployment.
We are introducing a special call for workshop papers that not only explore innovative AI-enabled approaches but also critically reflect on their implications, risks, and responsible deployment in real-world contexts. We encourage submissions that present early-stage findings or exploratory ideas that may not yet be fully developed for archival publication but are valuable to the community.
Relevant topics may include, but are not limited to:
Clinical validation, real-world deployment, and scalable implementation of AI-integrated UbiComp interventions.
Datasets, community benchmarks, and evaluation standards for UbiComp mental health systems across diverse populations and clinical contexts.
Foundation models and AI-integrated UbiComp pipelines for mental health and well-being support (e.g., mental health inference, emotional support, clinical decision-making).
Longitudinal personalization and context-aware adaptation in UbiComp mental health and well-being systems.
Long-term integration and sustainable adoption of UbiComp mental health technologies in healthcare systems.
Multimodal and longitudinal sensing data collection, integration, and quality for AI-driven mental health contexts.
Novel sensing modalities and hardware innovations for mental health monitoring beyond conventional smartphone and wearable platforms.
Responsible and trustworthy UbiComp mental health and well-being systems (e.g., transparency, interpretability, fairness, and accountability).
Submission deadline: 25 June 2026 5 July 2026 (extended and final)
Decisions to authors: 17 July 2026 22 July 2026
Camera-ready deadline: 15 July 2026 28 July 2026 20 August 2026
Workshop: 11 October 2026
All items due 11:59 PM AoE
We are soliciting six types of contributions (see below). Papers should be submitted using the UbiComp/ISWC 2026 proceedings format. Papers should be in PDF format and not anonymized.
To submit, please use the below details: https://new.precisionconference.com/
Society: SIGCHI
Conference: UbiComp/ISWC 2026,
Track: UbiComp/ISWC 2026 Mental Health
We are soliciting six types of contributions for the workshop as follow:
Scientific papers describing novel technologies, approaches, datasets, or empirical findings at the intersection of ubiquitous sensing and AI for mental health. We encourage these submissions to focus on learnings that are beneficial for the community and not finished contributions.
Challenge papers, in which authors articulate a specific challenge to be pitched and discussed at the workshop. These papers often lead to a lively discussion during the workshop and to new directions for future work.
Experience reports detailing the deployment of AI-integrated UbiComp systems in real-world mental health contexts, including systematic documentation of implementation barriers, user engagement outcomes, clinical workflow integration, and lessons learned.
Critical reflections on methodological assumptions, evaluation norms, or ethical frameworks at the intersection of ubiquitous computing, AI, and mental healthcare. We expect critical reflection papers to contribute towards better research practices in the community.
Dataset papers, in which authors document new datasets from clinical or underrepresented populations, including data collection methodology, participant characteristics, known limitations, and guidance for responsible community reuse.
Demonstrations of working systems accompanied by a short paper describing the sensing architecture, evaluation approach, and preliminary findings.
Submissions may be up to 6 pages in length, including figures and references. Shorter papers (e.g., 3-page submissions) are also welcome.
All submissions should be formatted using the 2-column ACM proceedings template.
This workshop uses a single-blind review process; all submissions must include the names and affiliations of all authors.
All submitted papers will be reviewed and judged on originality, technical correctness, relevance, and quality of presentation. We explicitly invite submissions of papers that describe preliminary results or work-in-progress, including early translational experiences.
The accepted papers will appear in the UbiComp supplemental proceedings and in the ACM Digital Library (DL). Authors of accepted papers will be invited to present their work in person and receive feedback from attendees. We plan to have a fully in-person workshop in Shanghai, China.
Please also see this general guideline if you plan to submit your accepted paper archived in ACM DL to other peer-reviewed venues, like IMWUT. — IMWUT, by default, UbiComp workshop papers are not considered for publication in IMWUT. The authors are also allowed to "re-use and re-submit the content to other peer-reviewed venues”. The new manuscript would require at least 25% of new material (conceptually, not just text) per ACM guidelines, and, in this case, it would be prudent to include the previous submission together with the new one.
You can also “choose not to" archive your accepted paper in ACM DL. In this case, please notify the organizers once your paper has been accepted.
| Time (Local to Shanghai, China) | Event |
|---|---|
| 9:00 AM - 9:30 AM | Opening remarks |
| 9:30 AM - 10:30 AM | Keynote speaker 1: Uichin Lee (KAIST) — Beyond Prediction: Building Reliable and Responsible AI for Digital Mental Health |
| 10:30 AM – 11:00 AM | Speed networking and coffee break |
| 11:00 AM – 12:30 PM | Workshop paper feedback sessions
To be announced after notification (July 2026). |
| 12:30 PM - 2:00 PM | Networking lunch with workshop attendees |
| 2:00 PM – 4:00 PM | Group discussion and brainstorming with attendees:
Potential topics:
|
| 4:00 PM – 5:00 PM | Keynote speaker 2: TBD |
| 5:00 PM - 5:15 PM | Coffee break (with the main conference itself) |
| 5:15 PM – 5:30 PM | Closing remarks and best paper award |
| 6:00 PM – 8:00 PM | Networking dinner with workshop attendees |
We will invite two keynote speakers representing complementary perspectives: one from clinical or translational research with experience deploying UbiComp systems in real-world mental health settings, and one from the AI or sensing research community addressing the opportunities and risks of integrating foundation models and generative AI into mental health pipelines. We aim to include a speaker from the Asia-Pacific region to reflect the geographic diversity of the workshop community.
Abstract:
The convergence of digital phenotyping and AI offers new opportunities to understand and support mental health in everyday life. Mobile phones, wearable devices, and Internet of Things technologies can continuously capture behavioral, physiological, social, and contextual signals outside clinical settings. Drawing on research in mobile and wearable sensing for mental healthcare, this talk will examine how digital phenotyping can move beyond passive observation toward actionable and adaptive mental health support. It will discuss emerging opportunities, including multimodal and longitudinal modeling, personalized AI, causal analysis of behavior and context, and proactive interventions that learn from individual responses over time. The talk will also critically address the challenges that may limit translation into practice. These include noisy and incomplete real-world data, uncertainty in inferred mental states, limited reproducibility and generalizability, differences across individuals and populations, privacy and data governance, and the risk of providing inappropriate or overly confident recommendations. Finally, the talk will outline research directions for developing digital mental health systems that are reliable, interpretable, ethically grounded, and deployable in real-world care and self-management settings.
Dr. Uichin Lee is a Professor in the School of Computing and AI Computing Department at the Korea Advanced Institute of Science and Technology (KAIST), leading the Interactive Computing Lab, whose mission is to study intelligent positive computing systems that can intervene in threats to human health and digital wellbeing. He received a Ph.D. degree in computer science from UCLA in 2008. He worked for Alcatel-Lucent Bell Labs as a member of the technical staff before joining KAIST in 2010. He has joint affiliations with the Department of Industrial and Systems Engineering, the Graduate School of Data Science at KAIST, and the KAIST Health Science Institute. In 2023, he was inducted as a member of the SIGCHI Academy, an honorary group of individuals who have made substantial contributions to the field of human-computer interaction (HCI). He served as a program committee member of the key HCI conferences and journals, such as ACM CHI, CSCW, and Ubicomp, and as an editor for PACM HCI (CSCW) and IMWUT (Ubicomp). He received the best paper awards at ACM Ubicomp’24 (IMWUT), ACM CHI’16, AAAI ICWSM’13, IEEE CCGrid’11, and IEEE PerCom’07, and an impact award from IEEE IoT Forum’19.
Abstract:
To be announced.
To be announced.