The integration of large language models (LLMs) into educational settings represents a significant technological breakthrough, offering substantial opportunities alongside profound ethical challenges. Higher education institutions face the widespread use of these tools by students, requiring them to navigate complex decisions regarding their adoption. This includes determining whether to allow the use of LLMs, defining their appropriate scope, and establishing guidelines for their responsible and ethical application. In the context of computer science education, these challenges are particularly acute. On the one hand, the capabilities of LLMs significantly enhance the tools available to developers and software engineers. On the other hand, students’ over-reliance on LLMs risks hindering their development of foundational skills. This study examines these challenges and proposes strategies to regulate the use of LLMs while upholding academic integrity. It focuses on the specific impact of LLMs in programming education, where dependence on AI-generated solutions may erode active learning and essential skill acquisition. Through a comprehensive literature review and drawing on teaching experience and guidelines from global institutions, this study contributes to the broader discourse on the integration of these advanced technologies into educational environments. The goal is to enhance learning outcomes while ensuring the development of competent, ethical software professionals.

This paper addresses the emerging challenge presented by large language models (LLMs) such as ChatGPT that are able to generate solutions to tasks traditionally used to enhance student’s analytical and programming skills, particularly in programming education. This widespread availability of AI-generated solutions risks undermining the learning process and skill acquisition by enabling students to use AI generated solutions instead of practicing themselves. Addressing this challenge, our paper outlines a holistic strategy that combines educational initiatives, state-of-the-art plagiarism detection mechanisms, and an innovative steganography-based technique for watermarking AI-produced code. This multifaceted approach aims to provide evaluators with the tools to distinguish between a code generated by ChatGPT and a code genuinely created by students. With the collective efforts of educators, course administrators, and partnerships with AI developers, we believe it is feasible to uphold the integrity of programming education in this age of code-producing LLMs.