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An Empirical Study on the Usage and Effectiveness of the Smart Coding Tutor in a Python Course

Authors:
Nien-Lin Hsueh
Ying-Chang Lu
Lien-Chi Lai

Keywords: Programming education; Large Language Model; Online Judge System; Artifitial Intelligence.

Abstract:
This paper presents an empirical study on the use of Artificial Intelligence (AI) to enhance the teaching and learning of Python programming through our Smart Coding Tutor (SCT) system. Designed for an online course with 315 students from various academic disciplines and levels, the system provided an interactive coding environment with automated validation through hidden test cases and support from three specialized AI teaching assistants. These assistants provided personalized guidance on code structuring, debugging, and optimization, allowing students to address challenges effectively while developing essential programming skills. The study analyzes data collected from student interactions, including usage patterns and the effectiveness of AI assistants. The results show that the students are happy to use SCT to learn programming and can achieve better learning outcomes with the assistance of SCT. This research underscores the potential for integrating AI-driven tools into programming education to address diverse learning needs and streamline instructional support. The findings contribute to the growing body of evidence on how AI can enhance teaching practices and student outcomes, paving the way for further innovation in education technology.

Pages: 17 to 22

Copyright: Copyright (c) IARIA, 2025

Publication date: July 6, 2025

Published in: conference

ISBN: 978-1-68558-284-5

Location: Venice, Italy

Dates: from July 6, 2025 to July 10, 2025