PACE: Python AI Companion for Enhanced Engagement

Abstract

This research introduces PACE (Python AI Companion for Enhanced Engagement), an AI-driven, interactive tutoring system designed to support Python programming students through personalized, responsive guidance. Addressing common challenges in traditional educational settings, such as limited individualized feedback and scalability, PACE leverages the capabilities of Large Language Models (LLMs) to create a tailored, human-like tutoring experience. Fine-tuned using the Gemma 2B model with scaffolding and conversational datasets, PACE offers step-by-step instruction and adaptive problem-solving support that encourages active learning without directly revealing answers. The development of PACE involved a robust design and implementation process, including backend development with FastAPI, frontend construction in React, and data management in MySQL, to deliver seamless, real-time interactions with students. Interaction design and user interface principles were carefully applied to create an intuitive environment that promotes engagement and reduces cognitive load. The model’s fine-tuning was achieved using Low-Rank Adaptation (LoRA), optimizing computational efficiency while enhancing PACE’s ability to deliver domain-specific instruction in Python. The system was evaluated with undergraduate students using five pedagogical metrics: Relevancy, Correctness, Completeness, Motivation, and Clarity, and included a System Usability Scale (SUS) assessment. Findings demonstrate PACE’s success in fostering understanding, boosting student engagement, and encouraging critical thinking. By tracking student progress and providing scalable, personalized support, PACE also eases the instructional burden on educators in large classes. This work showcases the potential of LLM-based intelligent tutoring systems to deliver cost-effective, high-quality, personalized learning experiences, making them viable for broader educational deployment.