Presented by
University of Louisiana at Lafayette, Ochsner Health, Tulane University, Georgia Tech, and the University of Florida
Our next seminar is scheduled for April 17 at 2:30 pm – 3:30 pm CST.
NSF AHeAD invites you to participate in the AI4HealthOutcomes Seminar Series, a multi-institutional forum bringing together leading experts in artificial intelligence, public health, and medicine to help advance evidence-based health innovation using AI.
This forum brings together AI researchers, data scientists, and public health professionals to explore AI applications in healthcare delivery and population health – with emphasis on implementation strategies, model validation, and data integration challenges.
Goals
The goal of the series is to foster collaboration, spark innovation, and gather feedback from an interdisciplinary community of researchers and practitioners.
Who can attend?
The seminar series is open to researchers, faculty, industry, doctors, healthcare professionals, and students!
Is there a cost?
The series is complementary. The seminar is open to everyone, so please share it with your colleagues, faculty, students, or staff.
Register Now!
► Register here – https://forms.office.com/r/w4Be170ByL
► If you’d like to present, attend, or learn more about the series, please send a note to raju@louisiana.edu.
Meeting Format and Schedule
- When: 3rd Friday of each month
- Time: 2:30 pm – 3:30 pm CST
- Duration: 30 – 40-minute presentation + 15 minutes for Q&A
- Format: Webinar (Registered participants will receive the Zoom link the day before the seminar.)
After the April 17, 2026 session, the series will break for the summer and return on August 21, 2026.
Beenish Chaudhry, PhD
Associate Professor, School of Computing and Informatics, University of Louisiana at Lafayette
Topic: Ethical Sensemaking in AI Mental Health Chatbots
Date: Friday, April 17, 2026
Time: 2:30 pm – 3:30 pm CT
Biography
Dr. Chaudhry’s work focuses on human-computer interaction (HCI) and artificial intelligence (AI), exploring the intersection of technology, design, and user experience (UX). She is passionate about designing ethical, user-centered AI tools and improving the integration of AI into creative workflows and healthcare. She currently teaches courses on system design and analysis, HCI, and UX principles. She prefers hands-on techniques in her teaching, fostering an interactive and practical learning environment for her students. Her work continues to push the boundaries of AI in design and healthcare, ensuring that technology is accessible and beneficial to all users.
Abstract
As AI adoption expands, mental health chatbots are increasingly used for emotional support and self-management, yet their roles and responsibilities remain ethically ambiguous in everyday use. Prior research often treats these boundaries as externally defined, overlooking how users interpret and negotiate them in practice.
We analyze a large corpus of app store reviews of four widely used mental health chatbots using topic modeling and qualitative analysis. We identify three recurring processes: interactional role inference, ethical negotiation during success and breakdown, and collective boundary contestation through reviews.
We show that users infer roles from conversational cues and regulate trust as a bounded form of delegation. Breakdowns act as ethical inflection points, while reviews externalize judgments into collective boundary work and vernacular governance.
These findings demonstrate that ethical boundaries are actively constructed through interaction and platform participation, shifting design toward supporting ethical sensemaking as an ongoing, socially distributed process.