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Related Experiment Video

Updated: Feb 21, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Enhancing LGBTQ+ Inclusivity in an AI-Powered Sexual Health Chatbot: User-Centered Design Approach Through a

William Wibowo Liem¹, Elizabeth Casline¹, Julianna Lorenzo¹

¹Impact Institute, Northwestern University, Chicago, IL, United States.

Journal of Medical Internet Research

|February 19, 2026

Summary

LGBTQ+ teenagers co-designed an AI chatbot for sexual health, improving inclusivity and trustworthiness. Their insights shaped content and features, demonstrating the value of involving marginalized youth in digital health tool development.

Keywords:

AI LGBTQ+adolescents artificial intelligence digital health inclusivity lesbian, gay, bisexual, transgender, and queer sex education sexual and gender minorities sexual health stakeholder participation

Related Experiment Videos

Last Updated: Feb 21, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Digital Health
Artificial Intelligence
Sexual Health Education

Background:

Digital sexual health tools often exclude LGBTQ+ teenagers.
AI chatbots show promise but face bias and trust concerns.
Participatory design with marginalized youth is crucial for equitable digital health.

Purpose of the Study:

Examine LGBTQ+ teenagers' perceptions of the AI chatbot Roo.
Identify opportunities and challenges in delivering inclusive sexual health information.
Co-design improvements for AI-powered sexual health interventions.

Main Methods:

Collaborated with Planned Parenthood Federation of America (PPFA) to customize Roo.
Engaged a Youth Advisory Council of 15 LGBTQ+ teenagers.
Collected feedback via asynchronous Discord discussions and rapid qualitative analysis.

Main Results:

Teenagers expressed skepticism and curiosity about AI in sexual health.
Identified limitations in chatbot inclusivity, tone, and transgender-specific content.
Findings informed PPFA's updates to enhance content, reduce stigma, and improve transparency.

Conclusions:

Academic-nonprofit collaborations can enhance digital health tools using participatory methods.
LGBTQ+ teenagers acted as co-designers, shaping the chatbot's evolution.
Marginalized youth insights are vital for developing equitable AI health interventions.