Providing timely and accurate learning support in large-scale online coding courses is challenging, particularly in resource-constrained contexts. We present Kwame 2.0, a bilingual (English-French) generative AI teaching assistant built using retrieval-augmented generation and deployed in a human-in-the-loop forum within SuaCode, an introductory mobile-based coding course for learners across Africa. Kwame 2.0 retrieves relevant course materials and generates context-aware responses while encouraging human oversight and community participation. We deployed the system in a 15-month longitudinal study spanning 15 cohorts with 3,717 enrollments across 35 African countries. Evaluation using community feedback and expert ratings shows that Kwame 2.0 provided high-quality and timely support, achieving high accuracy on curriculum-related questions, while human facilitators and peers effectively mitigated errors, particularly for administrative queries. Our findings demonstrate that human-in-the-loop generative AI systems can combine the scalability and speed of AI with the reliability of human support, offering an effective approach to learning assistance for underrepresented populations in resource-constrained settings at scale.

翻译：为大规模在线编程课程提供及时准确的学习支持具有挑战性，尤其在资源受限的环境中尤为突出。我们提出Kwame 2.0——一种基于检索增强生成的双语（英语-法语）生成式AI助教系统，并将其部署在面向非洲学习者的入门级移动编程课程SuaCode的人机协同论坛中。Kwame 2.0能检索相关课程资料并生成上下文感知的回复，同时鼓励人工监督和社区参与。我们通过一项为期15个月的纵向研究部署该系统，该研究覆盖35个非洲国家的15个教学批次共3717人次选课。基于社区反馈和专家评分的评估表明，Kwame 2.0提供了高质量及时的支持，在课程相关问题解答上达到高准确率，同时人类助教和同伴有效纠正了错误（尤其涉及行政类查询）。我们的研究证明，人机协同生成式AI系统能够将AI的可扩展性与速度同人类支持的可靠性相结合，为资源受限环境中弱势群体的大规模学习支持提供有效方案。