# Large Language Models in Computer Science Classrooms: Ethical Challenges and Strategic Solutions
## 1 Introduction
However, the authors promoted caution against risks such as over-reliance, intellectual laziness, and diminished critical thinking skills, and emphasized the critical role of educators in guiding the integration of ChatGPT, ensuring that students understand its limitations, including its biases and inaccuracies. Through fostering AI literacy and encouraging critical evaluation of outputs, educators can mitigate misuse and promote ethical practices. Ultimately, [PERSON] et al. advocated for the use of ChatGPT as a complementary tool to enhance intellectual engagement, ensuring that it supports rather than undermines core educational objectives. [PERSON]'s rapid review [PERSON] et al. (2018) explored the implications of ChatGPT in education, focusing on three key research questions: (1) How does ChatGPT perform across different subject domains? (2) How can ChatGPT enhance teaching and learning? (3) What challenges does ChatGPT pose, and how can they be addressed? The analysis of 50 academic articles revealed that ChatGPT's performance varies across domains. It excelled in areas requiring critical and higher-order thinking, particularly in economics--where it outperformed average students--but was less effective in fields such as law, medical education, and mathematics. The study emphasized the importance of updating assessment strategies, training educators, and educating students to integrate ChatGPT effectively while maintaining educational integrity. Our study focuses on improving the effectiveness of computer science education while ensuring academic integrity in an era where LLMs are pervasive. Specifically, we propose actionable strategies to encourage self-guided learning in programming exercises and to uphold integrity using both educational techniques and plagiarism detection methods. These strategies address the challenges faced by educators worldwide, aiming to promote the responsible and effective use of AI tools while safeguarding the integrity of educational objectives. By providing practical implementations and policy recommendations, we contribute to the ongoing effort to balance the opportunities and risks associated with LLMs in computer science education. This study makes the following key contributions:  
1. It explores the potential of LLMs to enhance computer science education and foster the development of students' skills and competencies. 2. It develops ethical and practical frameworks designed to encourage a culture of originality and independent effort among students. 3. It outlines actionable strategies for educators to encourage responsible AI usage while maintaining academic integrity and aligning with educational goals. 4. It evaluates the application of plagiarism detection methods, including both automated tools and manual techniques, to maintain academic standards. 5. It advocates for collaboration between LLM developers and educational institutions to embed watermarks in AI-generated code, ensuring easy identification and source traceability. 6. It assesses the effectiveness of various steganography methods for the insertion of watermarks to signify AI-generated code. The remainder of this paper is structured as follows. Section 2 details the systematic review methodology employed in this study. Section 3 explores the vast potential of LLMs in enhancing computer science education, as well as the challenges involved. Section 4 discusses the ethical frameworks necessary for the responsible use of LLMs in academia, including educational strategies to encourage independent work and recommendations for upholding academic integrity. Section 5 addresses tools and strategies for maintaining academic honesty, detailing practical methods to promote students' original work. Finally, Section 6 summarizes the findings and discusses future directions for research and policy development regarding the integration of AI technologies in higher education settings.