发表机构
University of Pennsylvania; Mamata Academy of Medical Sciences; Khushi Baby; Microsoft Research India（宾夕法尼亚大学; 马玛塔医学科学院; 库希贝比; 微软印度研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对印度农村社区卫生工作者咨询支持不足的问题，通过模拟电话、访谈和LLM角色扮演探针研究其沟通模式，提出AI应作为排练伙伴、提供描述性反馈并评估咨询过程的设计建议。
AI 中文摘要
全球南方的社区卫生工作者（CHWs）越来越多地接触AI驱动的工具，然而作为其核心职责的咨询工作在很大程度上仍未得到支持。我们通过模拟计划生育电话、半结构化访谈以及一项包含20名参与者的LLM聊天机器人角色扮演设计探针，研究了印度拉贾斯坦邦农村地区认证社会健康活动家（ASHAs）的沟通实践。在电话中，ASHAs常常通过转向健康风险信息、否认关切、承诺未指明的帮助或列出缺乏解释的医疗解决方案来回应社会或物质层面的关切。少数回应则转而关注关切本身，在涉及家庭成员前征求许可，或将决定权留给受益者。我们通过动机性访谈来解释这些模式，强调克制纠正、说服或过度解决。综合观察到的电话、访谈和探针反应，我们为AI角色扮演培训提出了设计考量：让AI保持排练角色，提供描述性而非规定性的反馈，并评估咨询过程而非与规定回应的一致性。
英文摘要
Community health workers (CHWs) in the Global South increasingly encounter AI-powered tools, yet the counseling work central to their role remains largely unsupported. We study communication practices among Accredited Social Health Activists (ASHAs) in rural Rajasthan, India, through simulated family-planning calls, semi-structured interviews, and an LLM chatbot roleplay design-probe with 20 participants. In calls, ASHAs often responded to social or material concerns by shifting to health-risk information, denying concerns, promising unspecified help, or listing medical solutions with limited explanation. A smaller set of responses instead engaged concerns, sought permission before involving family members, or left decisions with beneficiaries. We interpret these patterns through Motivational Interviewing, emphasizing restraint from correcting, persuading, or over-solving. Drawing across observed calls, interviews, and probe reactions, we derive design considerations for AI roleplay training: keep AI in a rehearsal role, provide descriptive rather than prescriptive feedback, and evaluate counseling process rather than agreement with prescribed responses.
