Ongoing discussions in Human-Computer Interaction(HCI) have examined the role of AI-based tools in health information seeking, particularly within sensitive domains such as reproductive health. We introduce "OpenBloom," a web application and an exploratory design probe that utilizes Large Language Models (LLMs) to turn reproductive health articles into question-based prompts to explore stigma around reproductive wellbeing. Through a survey study with 34 participants across their 136 interactions with OpenBloom, we explore how AI-generated question-based learning interacts with sociocultural stigma, contextual sensitivity, and reflexiveness. While current LLM outputs largely meet expectations for non-offensiveness, they default to superficial rephrasing or factual recall and lack critical reflections. We discuss implications for applying Feminist HCI, contestability, and value-sensitive AI frameworks to future LLM-mediated reproductive health technologies.

翻译：人机交互领域的持续性讨论已审视了基于AI工具在健康信息搜寻中的作用，尤其在生殖健康等敏感领域。本文提出"OpenBloom"——一项利用大语言模型将生殖健康文章转化为基于问题的提示以探索生殖福祉相关污名的网络应用与探索性设计探针。通过对34名参与者在与OpenBloom进行136次互动的调查研究，我们探究了AI生成的基于问题的学习如何与社会文化污名、情境敏感性及反身性产生交互。当前大语言模型输出虽在非攻击性方面基本满足预期，但其倾向于表面性改写或事实性复述，缺乏批判性反思。我们讨论了将女性主义人机交互、可争议性及价值敏感性AI框架应用于未来基于大语言模型的生殖健康技术的启示。