# Large Language Models in Computer Science Classrooms: Ethical Challenges and Strategic Solutions
## 3 LLMs for Computer Science Studies: Advantages and Challenges  
Building on the systematic review methodology described in the previous section, this part of our study delves into the specific impacts of LLMs on computer science education. We analyze how LLMs are being integrated into curricula and pedagogy, as evidenced by the literature and empirical findings we systematically reviewed. Our discussion begins by exploring the diverse ways in which students and educators in computer science are currently leveraging these tools, highlighting both the educational opportunities and the challenges they present. This section aims to provide a comprehensive assessment of the advantages offered by LLMs in enhancing learning and teaching processes, while also addressing potential pitfalls such as ethical dilemmas, over-reliance on technology, and risks to academic integrity. Through connecting the insights gathered from our extensive literature review to real-world applications and challenges in computer science education, we offer well-rounded strategies designed to optimize the benefits of LLMs while mitigating their drawbacks. This approach ensures that our findings are both grounded in evidence and directly applicable to educational practices. While the existing literature has extensively examined the potential and challenges of LLMs in education, our study builds upon and extends these discussions with several novel contributions. Previous studies have highlighted ethical risks, such as over-reliance, biases, and the need for critical evaluation of AI outputs; however, our study addresses these challenges through the proposal of actionable ethical and practical frameworks, specifically designed to align AI usage with educational goals. Furthermore, while prior research has identified issues such as plagiarism and dependency on automated solutions, we go further by introducing targeted strategies to uphold academic integrity. These include tailored coursework, unique project-based assignments, and a combination of manual and automated plagiarism detection methods. A particularly  
Figure 3: Cited publications by year. significant contribution of our work is the advocacy for watermarking techniques, which enable the traceability of AI-generated content through collaboration with developers--an approach largely absent in the existing literature. Our research also uniquely focuses on computer science education, tackling challenges such as dependency on code-generation tools and the erosion of foundational programming skills. Finally, while existing works call for ethical integration and transparency, we adopt a holistic approach by combining pedagogical innovations, policy recommendations, and technological solutions, such as advanced detection tools and text steganography. Together, these contributions address critical gaps in the literature, providing practical, discipline-specific strategies to enhance learning outcomes and maintain academic integrity in the era of generative AI. Exploring the Role of LLMs in Enhancing Learning and Skill Development for Computer Science Students  
Large language models possess transformative potential within the realm of education, particularly in enhancing and empowering learning processes. Notably, their extensive capabilities in the context of software development provide immense support for computer science students throughout their educational journey. [PERSON] et al. [PERSON] et al. [PERSON] et al. (2018) have emphasized the versatility of LLMs across multiple domains, particularly their benefits in programming and software design. As dynamic educational tools, LLMs engage students in complex programming tasks, thereby enriching their learning experiences. For instance, these models facilitate the generation of boilerplate code, assist in language translation, and can simulate various programming roles. This enables students to gain a comprehensive understanding of the software development life cycle. Building on the wide range of opportunities, [PERSON] et al. [PERSON] et al.