# Large Language Models in Computer Science Classrooms: Ethical Challenges and Strategic Solutions
## 4 Suggested Guidelines for Ensuring the Ethical Use of LLMs in Academia
### Institutional Frameworks for AI Tool Integration: A Cross-University Study  
As discussed in the previous section, it is crucial for academic institutions to establish robust governance structures to oversee and coordinate these efforts effectively. Universities should consider forming presidential working groups or AI taskforces to ensure alignment with institutional values and educational strategies. Furthermore, an effective implementation framework must be developed, focusing on aligning the capabilities of AI tools with specific educational objectives and evaluating their potential impacts on learning outcomes. This framework should enforce adherence to ethical standards, including fairness and transparency, and implement robust data privacy and security protocols. There is also a need to balance AI automation with human-centered approaches, providing clear usage guidelines to prevent over-reliance on technology. In addition, ethical considerations must underpin all AI-related decisions, emphasizing transparency, privacy, and equity. Collaboration with international bodies such as UNESCO could provide valuable guidance on ethical AI governance. Through the adoption of such a comprehensive framework, universities can effectively integrate AI tools, enhancing their educational missions while strictly adhering to their foundational values. It is important to note that, although our study primarily focuses on the use of LLMs by computer science students, the existing literature often addresses Generative Artificial Intelligence (GenAI) in a broader context. LLMs are specialized subsets of GenAI, dedicated exclusively to language-related tasks and utilizing deep learning techniques--particularly  
Figure 6: Uses for LLMs in computer science studies, separated into categories and legitimacy. Transformer architectures--to process and generate human-like text. In contrast, GenAI encompasses a broader spectrum of AI capabilities, such as generating diverse types of content including text, images, and music. Therefore, while our study focuses on the effects of LLMs in computer science education, we also discuss related studies and general university guidelines that use the broader term GenAI. This includes considering the impacts of both LLMs and other GenAI tools on students and the necessary guidelines for their appropriate use. The study by [PERSON] et al. [74] investigated the integration of large language models (LLMs) into software engineering (SE) education, adhering to the ACM Computer Science Curriculum Guidelines CS2023 to ensure the ethical and effective utilization of AI. They identified significant gaps in the current application of LLMs within SE curricula and advocated for the development of updated educational strategies to more fully exploit AI capabilities. Two primary directions for LLM usage were detailed: First, the creation of diverse, competency-focused exercises that align with competency-based education models, aiming to enhance programming classes with tasks that assess specific competencies and support the identification of knowledge gaps. Second, they examined the extension of AI tools to advanced SE courses, highlighting a discrepancy between AI applications in research and their educational implementations, particularly in areas such as software quality assurance, maintenance, and validation. They emphasized the importance of balanced didactic methods that integrate AI tools to complement traditional educational principles, thereby improving learning outcomes and equipping students for future professional environments. Expanding on these insights, [PERSON] [40] highlighted the challenges associated with integrating GenAI into education, particularly regarding originality and academic integrity. [PERSON] critiqued existing policies for narrowly defining originality as work free of AI influence and relying on surveillance-oriented measures to prevent misconduct.