Kwame 2.0用AI+人工协作，为非洲编程课学生提供双语及时答疑。
Kwame 2.0: Human-in-the-Loop Generative AI Teaching Assistant for Large Scale Online Coding Education in Africa
- 基于检索增强生成，结合课程资料与人工监督回复问题。
- 15个月覆盖35国3717人，对课程问题准确率达高水平。
- 适合资源匮乏地区的大规模在线教育，尤其关注非洲学习者。
在资源受限的环境下，为大规模在线编程课程提供及时准确的学习支持极具挑战。我们提出 Kwame 2.0，一个基于检索增强生成技术的双语（英语-法语）生成式AI助教，部署于 SuaCode 项目的人工介入式论坛中。该系统可检索相关课程资料并生成上下文感知的回答，同时鼓励人工监督与社区参与。我们在为期15个月的纵向研究中覆盖15个教学周期，涉及来自35个非洲国家的3,717名学习者。通过社区反馈与专家评分评估，Kwame 2.0 在课程相关问题上展现出高准确性与及时性，人类助教与同伴有效纠正了错误，尤其在处理行政类查询方面表现突出。研究结果表明，人工介入的生成式AI系统可在保持规模化与快速响应的同时，融合人工支持的可靠性，为资源匮乏环境中代表性不足群体提供高效学习支持。
原文摘要 · Abstract (English)
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.
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