# Large Language Models in Computer Science Classrooms: Ethical Challenges and Strategic Solutions
## 3 LLMs for Computer Science Studies: Advantages and Challenges
Recommendations for educators include enhancing AI literacy, reshaping curricula to emphasize problem-solving skills, and addressing the inequities caused by unequal AI access and efficacy. In a systematic review conducted by [PERSON] et al. [53], covering articles from 2021 to 2024 and examining 72 studies, the roles of LLMs as coding assistants for computer science students were thoroughly analyzed, specifically focused on their applications in code generation, explanation, and debugging. While tools such as ChatGPT and Codex provide accessible resources to promote the understanding of programming concepts, supplement learning materials, and enhance debugging processes, they are often hindered by inaccuracies, inconsistencies, and occasionally misleading outputs. These shortcomings, coupled with the risk of over-reliance, can impede students' development of critical thinking and problem-solving abilities. To address these challenges, the authors recommended integrating LLMs as supplementary tools, rather than substitutes for traditional teaching methods. They emphasized the importance of ethical guidelines, instructor supervision, and continuous refinement of LLM capabilities to enhance their accuracy and usability. This balanced approach aims to maximize the benefits of LLMs while maintaining academic integrity and ensuring high-quality learning outcomes. The additional value of our study is that it introduces detailed strategies and frameworks aimed at integrating LLMs into computing education with a strong emphasis on maintaining academic integrity. We explore innovative methods such as text steganography to watermark AI-generated content, enabling the detection and tracking of LLM usage in student submissions. Our approach also involves the development of specific pedagogical and assessment methodologies that deeply engage students with the material and minimize reliance on AI for solutions. Additionally, we review and compare academic integrity policies from various prominent universities, such as the University of Pretoria, Columbia University, and Carnegie Mellon University, detailing a spectrum of institutional responses from restrictive to permissive. This comparison highlights diverse strategies to navigate the ethical and practical challenges posed by LLMs in education, aiming to equip educators with forward-looking tools to maintain academic integrity while leveraging technological advancements. In summary, LLMs offer significant opportunities for computer science students to enhance learning outcomes, streamline workflows, and develop technical skills. The studiesreviewed in this section emphasize the importance of implementing structured educational interventions--such as teaching prompt engineering, assigning complex and engaging tasks, and fostering independent reasoning--to address the challenges associated with LLM integration. Furthermore, the limitations of existing detection systems and the evolving roles of LLMs in academic settings highlight the necessity for educators to adopt balanced and forward-thinking strategies that responsibly incorporate LLMs while safeguarding the integrity of the learning process. Figure 4 illustrates the primary steps undertaken by computer science students to enhance and practice their programming abilities, alongside the assistance that LLMs can provide at each stage. As LLMs continue to reshape educational practices, a collaborative effort involving educators, researchers, and developers is essential to fully realize their potential while effectively addressing these inherent complexities. The next section delves into a particularly innovative application of LLMs: transforming them into personalized teaching tools. This approach leverages the unique capabilities of LLMs to act not just as educational aids but as personal tutors, offering customized instruction and guidance tailored to the individual needs of each student.