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AffAdapt: AFFect-driven ADAPTive AI Personas for Seamless Conversations

UIST Adjunct 2026AI-generated personas are being increasingly used for support, training and simulations. While generative AI models possess abilities to generate affect-aware responses, their embodiment into visual personas is an active area of investigation.

Designing for College Mental Health: We Have Resources; We Lack the Reach

ACM IH 2026Community-engaged dissemination and implementation research focuses on the implementation of evidence-based interventions within clinical or community-based settings. It uses community-engaged processes or partnerships.

GenAI and Synthetic Data in Healthcare: Exploring the Design and Use of AI-Generated Data for Interactive Health Systems

ACM IH 2026Designing effective interactive health systems requires rich, realistic data to inform user-centered design activities. However, accessing authentic health data introduces major barriers due to privacy rules (HIPAA, IRBs, data use agreements) governing protected health information.

SocialLM: Social Signal Processing of Patient-Provider Communication using LLMs and Contextual Aggregation

CHIL 2026 Effective patient-provider communication is difficult to assess at scale. This work examines whether large language models can track 20 social behaviors from clinical transcripts without fine-tuning, and introduces an agreement-weighted ensemble to stabilise performance that otherwise varies by patient race and visit segment.

The Campus Mental Health Problem Isn't Capacity - It's Access!

CHI EA 2026The prevailing response to the student mental health crisis has been to expand clinical capacity: hire more therapists, reduce wait times, increase funding. We challenge this assumption.

Wellness Prescribed: Activating Holistic Care through EHR-embedded SmartPhrases

ACM IH 2026Universities have expanded counseling capacity in response to rising student mental health needs, yet limited access, fragmented discovery, and weak follow-through remain persistent barriers. Institutions offer non-clinical wellness support, but student awareness and use remain low.

DriveSimQuest: A VR Driving Simulator and Research Platform on Meta Quest with Unity

UIST Adjunct 2025Using head-mounted Virtual Reality (VR) displays to simulate driving is critical to study driving behavior and design driver assistance systems. But existing VR driving simulators are often limited to tracking only eye movements.

Holo-Stroke-CTA: Stroke Hologram Teleportation for CTA Large Vessel Occlusion Assessments

Stroke 2025Augmented reality enables visualization of and interaction with both physical and virtual environments. Holograms can allow 3‐dimensional image transmission to distant sites, allowing patients to interact with providers as if in the same space. Our prior publication resulted in high satisfaction/immersion for patients interacting with Holo‐Stroke providers. Our aim here was to determine if providers assessing computed tomographic angiographies (CTAs) for large vessel occlusion would result in reliability and satisfaction.

Interactions Beyond the Pandemic: Lessons Learned from Large-scale Emergency Remote Teaching in Higher Education

CHI 2025 Emergency Remote Teaching (ERT) during COVID-19 offered a unique chance to study online higher education at scale, beyond traditional lab settings. Through a review of 22 empirical studies, we analyzed how online classrooms addressed different types of interaction. Our findings highlight the need for future research that centers Learner-Content interaction as a way to balance flexibility with structure—especially as ERT may continue to shape education going forward.

Predicting trust in autonomous vehicles: Modeling young adult psychosocial traits, risk-benefit attitudes, and driving factors with machine learning

CHI 2025 Trust in autonomous vehicles varies widely among individuals, and this study uses machine learning to identify the key factors influencing young adults’ trust. Surveying over 1,400 participants, the analysis reveals that perceptions of AV risks and benefits, usability attitudes, institutional trust, prior experience, and mental models are the strongest predictors of trust—while psychosocial traits and driving styles play a lesser role. These findings underscore the need to account for individual differences when designing trustworthy AV systems.