Kwame: A Bilingual AI Teaching Assistant for Online SuaCode Courses

Abstract

Introductory hands-on courses such as our smartphone-based coding courses, SuaCode require a lot of support for students to accomplish learning goals. Online environments make it even more difficult to get assistance especially more recently because of COVID-19. Given the multilingual context of our students -learners across 38 African countries -in this work, we developed an AI Teaching Assistant (TA) -Kwame -that provides answers to students' coding questions from our SuaCode courses in English and French. Kwame is a Sentence-BERT(SBERT)-based question-answering (QA) system that we trained and evaluated using question-answer pairs created from our course's quizzes and students' questions in past cohorts. It finds the paragraph most semantically similar to the question via cosine similarity. We compared the system with TF-IDF and Universal Sentence Encoder. Our results showed that SBERT performed the worst for the duration of 6 secs per question but the best for accuracy and fine-tuning on our course data improved the result.

Diversified Teaching of English-Chinese Bilingual Courses Based on Integrating Multimodal Discourse Analysis

“…Secondly, for university teachers, bilingual teaching can learn from the excellent teaching models of Europe and the United States to a certain extent, improve the teaching level of teachers, can also reasonably introduce the original English textbooks to understand the latest academic research results at home and abroad [11]. Lastly, for students in universities, bilingual teaching can effectively improve the students' foreign language proficiency and professionalism for students to participate in the future either in the workplace or to go abroad for further study to lay a solid foundation of professionalism and language [12].…”