Gabriella Strudler | HXI - Human-centered eXtended Intelligence

Simulated Patients for Clinical Communication Training

Mon, 14 Sep 2026 00:00:00 +0000

Overview

How a clinician talks to a patient is part of the care, not a skill layered on top of it. Communication quality determines whether trust is built, whether a patient discloses what matters, and whether bias is enacted in the room. While that much is established, the opportunity to practice a difficult conversation, and to learn afterwards how it went, is scarce for clinicians and trainees alike.

Our UnBIASED work established that these dynamics are measurable: dominance, warmth, engagement, interactivity, and turn-taking can be extracted from real clinical conversations and modeled against how the interaction was experienced. This project turns that measurement into a training platform. Large language models and expressive speech synthesis drive simulated patients that behave like people rather than assistants: they hold information back, and their affect shifts in response to how they are treated, so trust has to be earned before a patient’s underlying needs surface. Each rehearsed conversation is analyzed with the UnBIASED pipeline and returned through ConverSense feedback, pairing time-aligned visualizations of the interaction with prompts that ask the learner to interpret their own behavior.

ConversHIVe applies the platform to HIV care teams, where implicit bias linked to sexual orientation and race affects access to care for people who have already faced stigma, and where clinics and community-based organizations work under limited time, staffing shortages, and turnover. Its second phase, ConversHIVe-Live, embeds the training in routine HIV service delivery.

EmpathIQ applies it to pre-clinical medical students, where empathy declines during training precisely as students meet the emotional load of clinical work, and where standardized patients and faculty-led workshops are effective but too resource-intensive to scale. EmpathIQ identifies the markers that distinguish empathic communication, varies patient affect within a single encounter, and evaluates whether the resulting feedback changes measured empathy and compassion.

Funding and External Collaborations

ConversHIVe is funded by the California HIV/AIDS Research Program (CHRP) under its Low Barrier Technology Interventions for HIV Prevention and Care program, with a second phase supporting real-world implementation. It is a collaboration between the HXI Lab and the Owen Clinic at UC San Diego, the AntiViral Research Center (AVRC) Community Advisory Board, and San Diego community-based organizations including Christie’s Place, San Ysidro Health Center, and Father Joe’s Villages.

EmpathIQ is supported by a Sanford Research Fellowship from the T. Denny Sanford Institute for Empathy and Compassion at UC San Diego, awarded to Manas Bedmutha, and is conducted with Drs. Lisa Eyler and Federica Klaus of the Sanford Institute and with Dr. Charles Goldberg, Associate Dean for Graduate Medical Education.

UNDERSTAND: Uplifting the New generation through DBT Education and Resilience for Social Triggers, Anxiety, Negativity, and Depression

Fri, 11 Jul 2025 00:00:00 +0000

Overview

Many college students experience depression and anxiety together, which compounds both symptom severity and functional impairment. Treatments like CBT and DBT work, but they are delivered on a schedule. The skill a student needs is taught in a session on Tuesday and required at 2am on Saturday, and the gap between those two moments is where the intervention fails.

UNDERSTAND closes that gap with three components.

Sensing. Wearables and phones carry signals that precede and accompany distress: physiological arousal, sleep and activity disruption, changes in social behavior and interaction patterns. We instrument these continuously and in daily life, so that the system is reading the situation as it develops rather than reconstructing it afterwards from self-report.

AI. Machine learning models turn those raw streams into an estimate of when someone is struggling and when they are receptive, which is the harder of the two questions. Large language models then generate the support itself, and our work constrains that generation so the model stays inside the skills and principles of DBT rather than producing generic encouragement that sounds therapeutic without being it.

Just-in-time adaptive intervention. The decision of what to deliver, when, and whether to deliver anything at all is the intervention. JITAI makes that decision continuously from the sensed context, offering a DBT skill at the moment it applies, and staying silent when an interruption would do more harm than good.

By targeting transdiagnostic mechanisms such as emotional dysregulation, interpersonal difficulty, and cognitive distortion, the approach is designed to generalize across populations with comorbid mood and anxiety disorders rather than to a single diagnosis.

Funding and External Collaborations

UNDERSTAND is funded by the National Institute of Mental Health (NIMH) at NIH, with the funded project starting 1 August 2026. It is a collaboration between the HXI Lab and the UC San Diego Department of Psychiatry and the Herbert Wertheim School of Public Health, bringing together expertise in DBT, clinical psychology, digital mental health, and ubiquitous sensing.

Designing Smart and Autonomous Vehicles

Thu, 25 May 2023 00:00:00 +0000

Overview

Adoption of autonomous vehicles is held back less by the driving than by the passenger’s judgment of it. Trust has to be calibrated: too little and the technology goes unused, too much and it is relied on where it should not be. We study that calibration at the level of the individual, using virtual reality, biometric measurement, human-centered design, and data science, rather than treating drivers as a single population.

Trust is personal. A survey of 1,457 young adults, modeled with machine learning and SHAP, showed that perceptions of AV risks and benefits, attitudes toward feasibility and usability, institutional trust, prior experience, and mental models predict trust, while psychosocial traits and driving style contribute far less.

Explanations carry that trust, and they can fail. In a simulated driving study with 232 participants, errors in an AV’s explanations reduced comfort in relying on the vehicle, confidence in its ability, and explanation satisfaction, even though the driving itself was identical. Perceived harm and driving difficulty amplified the damage, so the contexts where an explanation matters most are where getting it wrong costs most.

Studying any of this requires observing the driver. DriveSimQuest is a VR driving simulator and research platform on the Meta Quest Pro, capturing gaze, facial expression, hand activity, and full-body gesture in real time, so that studying a driver’s affective state is a matter of designing the study rather than building the rig.

The same setting looks different when the driver is a person. Fleet drivers are involved in collisions that impose severe financial costs and endanger lives, and fleet companies rely on one-to-one coaching to prepare them. We characterize that coaching from both sides, surveying coaches and interviewing drivers, and find that manager-led coaching outperforms self-coaching across experiential outcomes.

Funding and External Collaborations

The autonomous vehicle trust work is a collaboration between the UCSD departments of Cognitive Science, Computer Science and Engineering, and external industry partners. The fleet driver coaching research is conducted with Lytx.

Comparative Effectiveness of Coaching Modalities in Commercial Fleet Operations

Thu, 06 Nov 2025 00:00:00 +0000