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.

翻译：生殖健康福祉教育在多元文化背景下仍普遍存在污名化现象，制约了个体获取和解读生殖健康知识的方式。我们设计并评估了OpenBloom——一个对污名化敏感的、由人工智能介导的系统，该系统利用大语言模型将生殖健康文章转化为反思性的、基于问题的学习提示。我们将OpenBloom作为设计探针，旨在通过大语言模型探索生殖健康福祉污名化带来的新兴挑战。通过问卷调查、半结构化访谈和焦点小组讨论，我们研究了社会文化污名如何影响参与者与AI生成问题的互动，以及探究式生殖健康教育的机遇。我们的研究结果确定了对污名化敏感的大语言模型的关键设计考量，包括共情式框架、包容性语言、基于价值观的反思以及对边缘化身份的明确表征。然而，尽管当前大语言模型的输出在文化敏感性和非冒犯性方面基本符合预期，但其默认倾向于表面的措辞改写和事实回忆，而非批判性反思。这为敏感健康领域中福祉人机交互设计指明了方向，即应转向根植于文化、具有参与性的工作流程。