Using small language models as tools for teaching in the ELSEI master's program

Abstract

The Question Answering Introduction to Python (QAIP) system aims to enhance the learning experience in introductory Python courses by providing accurate and efficient answers to Python-related queries. The rise of Large Language Models (LLMs) has significantly impacted education, particularly within the framework of Education 4.0, which seeks to prepare students for a technologically advanced world. LLMs such as OpenAI’s ChatGPT and GitHub’s Copilot have revolutionized content creation, personalized tutoring, and student engagement, aligning with the goals of Education 4.0. However, the challenge of developing appropriate programming exercises and maintaining active learning in introductory programming courses persists, especially given the rapid online sharing of solutions. In this context, Small Language Models (SLMs) offer a lightweight, efficient alternative for educational integration. This article explores the integration of SLMs into the QAIP system within the E-learning and Intelligent Educational Systems (ELSEI) program, aiming to empower students with the skills to develop innovative educational tools. By narrowing the existing AI content gap, this work aspires to contribute to the broader discourse on AI accessibility and diversity. The development process involves thorough data collection, strategic model training, and careful deployment to ensure that the AI-driven system effectively meets student needs and enhances learning outcomes. Through this interdisciplinary effort, we aim to foster a culture of innovation and contribute meaningfully to the evolution of AI in education.