"OpenBloom": A Question-Based LLM Tool to Support Stigma Reduction in Reproductive Well-Being

2026-01-30

AI Summary

This study addresses the persistent stigmatization of reproductive health education in multicultural contexts, which impedes access to and comprehension of critical knowledge. To support inquiry-based learning in such stigma-sensitive settings, the work proposes a design framework for large language models (LLMs) that generates reflective, question-driven prompts. Employing HCI methods—including surveys, interviews, and focus groups—the research systematically evaluates and iteratively refines AI-generated content. Findings indicate that while current LLMs exhibit some cultural sensitivity, their adaptations often remain superficial, lacking deep critical reflection. The project contributes four design principles: empathetic expression, inclusive language, values-based reflection, and explicit representation of marginalized identities. These principles offer a novel pathway toward culturally embedded and participatory workflows for AI systems in sensitive health domains.

Technology Category

• Humans and AI: AI for Accessibility
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Reproductive well-being education remains widely stigmatized across diverse cultural contexts, constraining how individuals access and interpret reproductive health knowledge. We designed and evaluated OpenBloom, a stigma-sensitive, AI-mediated system that uses LLMs to transform reproductive health articles into reflective, question-based learning prompts. We employed OpenBloom as a design probe, aiming to explore the emerging challenges of reproductive well-being stigma through LLMs. Through surveys, semi-structured interviews, and focus group discussions, we examine how sociocultural stigma shapes participants'engagements with AI-generated questions and the opportunities of inquiry-based reproductive health education. Our findings identify key design considerations for stigma-sensitive LLM, including empathetic framing, inclusive language, values-based reflection, and explicit representation of marginalized identities. However, while current LLM outputs largely meet expectations for cultural sensitivity and non-offensiveness, they default to superficial rephrasing and factual recall rather than critical reflection. This guides well-being HCI design in sensitive health domains toward culturally grounded, participatory workflows.

Problem

• reproductive well-being
• stigma
• AI-mediated system
• LLM
• health education

Innovation

• stigma-sensitive LLM
• question-based learning
• reproductive well-being
• AI-mediated health education
• inclusive language