Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2025

Abstract

Debugging is a fundamental skill that novice programmers must develop. Numerous tools have been created to assist novice programmers in this process. Recently, large language models (LLMs) have been integrated with automated program repair techniques to generate fixes for students' buggy code. However, many of these tools foster an over-reliance on AI and do not actively engage students in the debugging process. In this work, we aim to design an intuitive debugging assistant, CodeHinter, that combines traditional debugging tools with LLM-based techniques to help novice debuggers fix semantic errors while promoting active engagement in the debugging process. We present findings from our second design iteration, which we tested with a group of undergraduate students. Our results indicate that the students found the tool highly effective in resolving semantic errors and significantly easier to use than the first version. Consistent with our previous study, error localization was the most valuable feature. Finally, we conclude that any AI-assisted debugging approach should be personalized based on user profiles to optimize their interactions with the tool.

Keywords

Assisted debugging, programming education, intelligent tutoring systems, large language models, interactive debugging, novice programmers, AI assistants, AI tutoring, design guidelines

Discipline

Artificial Intelligence and Robotics | Educational Methods | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Areas of Excellence

Digital transformation

Publication

Proc. 25th Koli Calling International Conference on Computing Education Research (Koli Calling '25)

First Page

1

Last Page

7

ISBN

9798400715990

Identifier

10.1145/3769994.3769997

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3769994.3769997

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