Academia and Industry Synergy: Addressing Integrity Challenge in Programming Education

Rina Azoulay, Tirza Hirst, Shulamit Reches

2024

Abstract

This research addresses the profound challenges presented by sophisticated large language models (LLMs) like ChatGPT, especially in the context of educational settings, focusing on computer science and programming instruction. State of the art LLMs are capable of generating solutions for standard exercises that are assigned to students to bolster their analytical and programming skills. However, the ease of using AI to generate programming solutions poses a risk to the educational process and skill development, as it may lead students to depend on these solutions instead of engaging in their own problem-solving efforts. Our study suggests collaborative methods involving computer science educators and AI developers to provide evaluators with tools to distinguish between code produced by ChatGPT and code genuinely created by students. We propose a novel steganography-based technique for watermarking AI-generated code. By implementing this comprehensive strategy and effectively utilizing such technology through the combined efforts of educators, course administrators, and partnerships with AI developers, we believe it is possible to preserve the integrity of programming education in an age increasingly influenced by LLMs capable of generating code.

Paper Citation

in Harvard Style

Azoulay R., Hirst T. and Reches S. (2024). Academia and Industry Synergy: Addressing Integrity Challenge in Programming Education. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 1135-1143. DOI: 10.5220/0012451000003636

in Bibtex Style

@conference{icaart24,
author={Rina Azoulay and Tirza Hirst and Shulamit Reches},
title={Academia and Industry Synergy: Addressing Integrity Challenge in Programming Education},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={1135-1143},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012451000003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Academia and Industry Synergy: Addressing Integrity Challenge in Programming Education
SN - 978-989-758-680-4
AU - Azoulay R.
AU - Hirst T.
AU - Reches S.
PY - 2024
SP - 1135
EP - 1143
DO - 10.5220/0012451000003636
PB - SciTePress