This study investigates Shiksha copilot, an AI-assisted lesson planning tool deployed in government schools across Karnataka, India. The system combined LLMs and human expertise through a structured process in which English and Kannada lesson plans were co-created by curators and AI; teachers then further customized these curated plans for their classrooms using their own expertise alongside AI support. Drawing on a large-scale mixed-methods study involving 1,043 teachers and 23 curators, we examine how educators collaborate with AI to generate context-sensitive lesson plans, assess the quality of AI-generated content, and analyze shifts in teaching practices within multilingual, low-resource environments. Our findings show that teachers used Shiksha copilot both to meet administrative documentation needs and to support their teaching. The tool eased bureaucratic workload, reduced lesson planning time, and lowered teaching-related stress, while promoting a shift toward activity-based pedagogy. However, systemic challenges such as staffing shortages and administrative demands constrained broader pedagogical change. We frame these findings through the lenses of teacher-AI collaboration and communities of practice to examine the effective integration of AI tools in teaching. Finally, we propose design directions for future teacher-centered EdTech, particularly in multilingual and Global South contexts.

翻译：本研究调查了在印度卡纳塔克邦公立学校部署的人工智能辅助课程规划工具Shiksha Copilot。该系统通过结构化流程将大语言模型（LLM）与人类专业知识相结合：策划者与人工智能共同创建英语和卡纳达语课程计划；随后教师利用自身专业知识并借助人工智能支持，进一步为课堂教学定制这些已策划的计划。基于一项涉及1,043名教师和23名策划者的大规模混合方法研究，我们探讨了教育工作者如何与人工智能协作生成情境敏感的课程计划，评估人工智能生成内容的质量，并分析多语言、资源匮乏环境下教学实践的转变。研究发现表明，教师使用Shiksha Copilot既满足了行政文档需求，也辅助了实际教学。该工具减轻了行政工作负担，缩短了课程规划时间，降低了教学相关压力，同时促进了向活动式教学法的转变。然而，人员短缺和行政要求等系统性挑战制约了更广泛的教学变革。我们通过教师-人工智能协作和实践共同体视角来阐释这些发现，以审视人工智能工具在教学中的有效整合。最后，我们为未来以教师为中心的教育技术（EdTech）提出设计方向，尤其针对多语言和全球南方语境。