Introductory hands-on courses such as our smartphone-based coding courses, SuaCode require a lot of support for students to accomplish learning goals. Offering assistance becomes even more challenging in an online course environment which has become important recently because of COVID-19. Hence, offering quick and accurate answers could improve the learning experience of students. However, it’s difficult to scale this support with humans when the class size is huge. A few works have developed virtual teaching assistants (TA). All of these TAs have focused on logistics questions, and none have been developed and evaluated using coding courses in particular. Also, they have used one language (e.g. English). Given the multilingual context of our students — learners across 38 African countries — in this work, we developed an AI 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.