# Large Language Models in Computer Science Classrooms: Ethical Challenges and Strategic Solutions
## 3 LLMs for Computer Science Studies: Advantages and Challenges
### Opportunities for Computer Science Teachers
#### 3.2.2 Adapting Assignments to the Era of LLMs  
Recent studies have suggested that computer science (CS) educators should adapt their pedagogical strategies and reconsider assessment methods and curricula to address the challenges posed by large language models (LLMs). Such adaptations aim to avoid over-reliance on these tools, integrate broader societal considerations--including ethical, legal, and economic impacts--and harness their potential as teaching aids [3]. Educators are encouraged to use LLMs to help students to master coding fundamentals while equipping them with the ability to critically evaluate and improve AI-generated outputs. Furthermore, the rapid advancement of AI models underscores the urgency of proactive adaptations, ensuring that learners not only use these tools effectively but also develop the foundational skills essential for long-term success in computing. [PERSON] et al. [57] provided a comprehensive systematic literature review (SLR) on the impact of LLMs in CS education--particularly regarding their integration across various educational levels--with a focus on introductory programming. Their review highlighted the effectiveness of LLMs, such as GPT and LLaMA, in enhancing personalized education, supporting curriculum development, and improving students' coding skills through the use of tools such as GitHub Copilot and ChatGPT. However, they also noted potential challenges, such as academic integrity issues (including plagiarism and over-reliance on these tools), which could undermine learning outcomes. This assessment emphasized the importance of carefully integrating LLMs into CS curricula in order to maximize their  
Figure 5: LLM-driven enhancements in the professional practices of computer science educators. benefits while mitigating risks, thereby preparing students for careers in which AI tools are commonplace. Interestingly, there are conflicting studies on the impact of LLMs on students' performance, attitudes, and abilities. Some research has highlighted negative influences. For example, [PERSON] et al. [31] examined the impact of LLMs on programming education, specifically in React application development. In a 10-week study involving 32 undergraduates, they found a negative correlation between heavy reliance on LLMs for code generation and debugging and students' grades. This finding suggests that dependence on LLMs for critical tasks may hinder the development of independent problem-solving skills. However, they also found that using LLMs for supplementary explanations positively supported learning. Similar to [PERSON] et al., [PERSON] et al. stressed the need to balance LLM use with fostering independent competencies. Conversely, other studies have reported positive outcomes from the use of LLM tools in CS and STEM education. [PERSON] et al. [35] explored the integration of ChatGPT in a first-year undergraduate object-oriented programming course. This course was adapted to the LLM era, considering the following adjustments: restructuring assignments, introducing an interactive assignment defense process, and adapting midterm exams to emphasize deeper learning. The restructuring of assignments included strategies such as requiring students to extend previous practical assignments, incorporate pre-given code into their applications, utilize concise descriptions supplemented by UML diagrams, and complete tasks involving specific input/output instructions or main program scaffolds. These adjustments were designed to limit ChatGPT's ability to provide straightforward answers while encouraging active problem-solving and critical thinking. [PERSON] et al. conducted a study involving 182 participants who took the above object-oriented programming course. They divided the participants into two groups: one with access to ChatGPT and one without. The study found no statistically significant differences in programming assignment performance (\(p=0.730\)), assignment grades (\(p=0.760\)), or midterm exam results (\(p=0.856\)).