Spoken English proficiency is a powerful driver of economic mobility for low-income Indian youth, yet opportunities for spoken practice remain scarce in schools. We investigate the deployment of a voice-based chatbot for English conversation practice across four low-resource schools in Delhi. Through a six-day field study combining observations and interviews, we captured the perspectives of students, teachers, and principals. Findings confirm high demand across all groups, with notable gains in student speaking confidence. Our multi-stakeholder analysis surfaced a tension in long-term adoption vision: students favored open-ended conversational practice, while administrators emphasized curriculum-aligned assessment. We offer design recommendations for voice-enabled chatbots in low-resource multilingual contexts, highlighting the need for more intelligible speech output for non-native learners, one-tap interactions with simplified interfaces, and actionable analytics for educators. Beyond language learning, our findings inform the co-design of future AI-based educational technologies that are socially sustainable within the complex ecosystem of low-resource schools.

翻译：英语口语能力是印度低收入青年实现经济流动的重要驱动力，然而学校中口语练习的机会依然匮乏。本研究调查了在德里四所低资源学校部署基于语音的聊天机器人进行英语对话练习的情况。通过为期六天结合观察与访谈的实地研究，我们收集了学生、教师和校长的观点。研究结果证实了所有群体对此均有强烈需求，并观察到学生口语自信心的显著提升。我们的多利益相关者分析揭示了长期应用愿景中的矛盾：学生偏好开放式对话练习，而管理者则强调与课程对齐的评估。我们为低资源多语言语境下的语音聊天机器人提出设计建议，包括为非母语学习者提供更清晰易懂的语音输出、通过简化界面实现一键交互，以及为教育工作者提供可操作的学情分析。除语言学习外，本研究还为未来基于人工智能的教育技术协同设计提供参考，使其在低资源学校的复杂生态系统中实现社会可持续性。