  Introductory hands-on courses such as our smartphone-based coding course,
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 SuaCode
students - learners across 42 African countries that are mostly Anglophone or
Francophone - in this work, we developed a bilingual Artificial Intelligence
(AI) Teaching Assistant (TA) - Kwame - that provides answers to students'
coding questions from SuaCode courses in English and French. Kwame is a
Sentence-BERT (SBERT)-based question-answering (QA) system that we trained and
evaluated offline using question-answer pairs created from the course's
quizzes, lesson notes and students' questions in past cohorts. Kwame 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 fine-tuning on the course data and returning the top 3 and 5
answers improved the accuracy results. Kwame will make it easy for students to
get quick and accurate answers to questions in SuaCode courses.
