Training AI Model that Suggests Python Code from Student Requests in Natural Language

Abstract

Programming is a creative activity, but it can be difficult to learn due to constant updates, poorly maintained documentation, and unexpected errors. One reason for the difficulties is the shortage of programming teachers, which often leaves students unable to get help when they need it, even for simple questions. Many unanswered questions are a barrier to improving programming skills for creative purposes. The purpose of this paper is to address this issue by exploring whether an AI-based system can help reduce the difficulties faced by students. Recent advancements in deep learning technology have made it easier for teachers to train AI models that can learn from their own experiences in the classroom, including the types of questions, requests, and difficulties that students encounter. We have developed an AI model that can translate Python code from Japanese by using machine translation techniques and large language models. We have integrated this model into a learning assistant system that suggests code to students when they express their programming intentions. In this paper, we present our experiences in developing and deploying this AI-based assistant in the classroom, as well as the feedback we have received from students. By sharing our initial experiences, we aim to envision the potential of educational AI development for the future.