论文标题
夸梅的科学：基于西非科学教育判决的AI助教
Kwame for Science: An AI Teaching Assistant Based on Sentence-BERT for Science Education in West Africa
论文作者
论文摘要
非洲的学生与教师比例很高，这限制了学生与老师的访问权限。因此，学生努力获取问题的答案。在这项工作中，我们扩展了以前的AI助教助理Kwame，将其改编成科学教育，并将其部署为Web应用程序。科学的夸梅（Kwame）根据西非高级中学证书考试（WASSCE）的综合科学主题回答学生的问题。 Kwame for Science是一个基于句子的问题，提问的Web应用程序，显示3段作为答案，以及回答科学问题的置信度得分。此外，除3段外，它还显示了前5个相关的考试问题及其答案。我们对2.5周的现实部署对夸梅科学的初步评估显示，在11个国家 /地区的190名用户中，前3个准确性为87.5％（n = 56）。夸梅的科学将使在非洲成千上万的人提供可扩展，成本效益和优质的远程教育。
Africa has a high student-to-teacher ratio which limits students' access to teachers. Consequently, students struggle to get answers to their questions. In this work, we extended Kwame, our previous AI teaching assistant, adapted it for science education, and deployed it as a web app. Kwame for Science answers questions of students based on the Integrated Science subject of the West African Senior Secondary Certificate Examination (WASSCE). Kwame for Science is a Sentence-BERT-based question-answering web app that displays 3 paragraphs as answers along with a confidence score in response to science questions. Additionally, it displays the top 5 related past exam questions and their answers in addition to the 3 paragraphs. Our preliminary evaluation of the Kwame for Science with a 2.5-week real-world deployment showed a top 3 accuracy of 87.5% (n=56) with 190 users across 11 countries. Kwame for Science will enable the delivery of scalable, cost-effective, and quality remote education to millions of people across Africa.
