Africa has a high student-to-teacher ratio which limits students' access to teachers for learning support such as educational question answering. In this work, we extended Kwame, our previous AI teaching assistant for coding education, adapted it for science education, and deployed it as a web app. Kwame for Science provides passages from well-curated knowledge sources and related past national exam questions as answers to questions from students based on the Integrated Science subject of the West African Senior Secondary Certificate Examination (WASSCE). Furthermore, students can view past national exam questions along with their answers and filter by year, question type (objectives, theory, and practicals), and topics that were automatically categorized by a topic detection model which we developed (91% unweighted average recall). We deployed Kwame for Science in the real world over 8 months and had 750 users across 32 countries (15 in Africa) and 1.5K questions asked. Our evaluation showed an 87.2% top 3 accuracy (n=109 questions) implying that Kwame for Science has a high chance of giving at least one useful answer among the 3 displayed. We categorized the reasons the model incorrectly answered questions to provide insights for future improvements. We also share challenges and lessons with the development, deployment, and human-computer interaction component of such a tool to enable other researchers to deploy similar tools. With a first-of-its-kind tool within the African context, Kwame for Science has the potential to enable the delivery of scalable, cost-effective, and quality remote education to millions of people across Africa.
翻译:非洲有着很高的师生比例,限制了学生获得教师的学习支持,例如教育问题回答。在这项工作中,我们延长了我们以前的AI教学助理Kwame(Kwame),用于编译教育,将其改编为科学教育,并将其作为网络应用程序。Kwame for Science Compaces(Kwame for Science for Science)提供了来自精密知识来源的段落和相关的过去国家考试问题,作为对学生根据西非高级中学证书考试综合科学科目(WASSCE)提出的问题的回答。此外,学生们可以看到过去的国家考试问题,以及他们每年的回答和过滤、问题类型(目标、理论和实践)以及我们开发的题目探测模型自动分类(91%未加权平均回顾),并将其作为一个专题的分类。我们把Kwame for Science 8个月来实际科学,在32个国家(15个非洲国家)有750个用户,并询问了1.5K问题。我们的评估显示,87.2%的前3个准确度(n=109问题)意味着Kwame(Ky for science)至少有机会在展示一个有用的答案。 3个插图中,我们把这个模型解答了几个问题的原因解了,为未来科学质量工具的精度。我们把问题解解的精度问题解度问题与非洲内部的精度—— ——将一个潜在的工具放在了非洲应用工具中。我们把一个潜在的工具同一个潜在的工具与推向非洲,把一个潜在的工具放在了。我们把一个潜在的工具放在了。