# Large Language Models in Computer Science Classrooms: Ethical Challenges and Strategic Solutions
## 3 LLMs for Computer Science Studies: Advantages and Challenges
(2018) have investigated the use of ChatGPT among undergraduate computer science students, offering valuable insights into its practical applications and challenges. Their findings revealed that more than half of the participants benefited from ChatGPT's assistance in tasks such as information gathering, content summarization, coding, and academic writing. Notably, students appreciated the tool's ability to deliver prompt feedback and clarify programming syntax. However, the study also uncovered significant limitations, including frequent inaccuracies in programming and math problem-solving, which often necessitated external verification. Additionally, concerns about ChatGPT's impact on critical thinking and ethical considerations arose, with some students equating its use to an academic shortcut that could undermine intellectual effort and creativity. Despite these challenges, the study underscored ChatGPT's potential to enhance educational outcomes when used responsibly and in conjunction with traditional teaching methods, particularly under tight deadlines or heavy workloads. Transitioning from these broader observations, the effective utilization of LLMs requires proficiency in prompt engineering--a skill critical for obtaining accurate and useful outputs. [PERSON] et al. [PERSON] et al. (2018) explored this concept in the context of GitHub Copilot--an AI-driven code generation tool designed for introductory programming education. Their research examined Copilot's effectiveness in solving basic programming tasks while emphasizing the role of prompt engineering in refining the tool's outputs. Through adjusting natural language prompts, students can overcome initial code generation errors, turning prompt engineering into a mechanism for enhancing computational thinking and problem-solving skills. Through a systematic evaluation of Copilot's performance on a dataset of 166 programming problems, the study categorized successful and unsuccessful outcomes and proposed a structured protocol for prompt refinement. This positions prompt engineering not only as a valuable skill for optimizing AI outputs but also as a potential cornerstone of future programming education, given the sensitivity of such models to input phrasing. Together, these findings illuminate the evolving role of LLMs and related tools in shaping the future of computer science education. Given the growing availability of LLM tools such as ChatGPT, it is essential to examine their effects on students, both from the students' perspectives and through independent evaluations. A significant advantage of LLMs is their ability to guide students in completing tasks, thereby enhancing their problem-solving skills. [PERSON] [56] has examined the use of ChatGPT in undergraduate courses, focusing on foundational programming concepts. In his study, students were divided into two groups for a controlled comparison: one group used traditional educational resources such as textbooks and notes without internet access, while the other engaged with ChatGPT for programming assignments. The results indicated that students using ChatGPT generally outperformed their peers, particularly in more complex assignments, although they took longer to complete these tasks. Furthermore, his findings suggested that ChatGPT could act as a personalized learning tool, potentially transforming student support services. Despite the challenges associated with integrating AI into existing educational frameworks, the study emphasized the crucial role of hands-on learning in programming education and recommended implementing automated plagiarism detection tools to prevent the misuse of ChatGPT in educational settings. [PERSON] and [PERSON] [77] extended this exploration to computer science education, investigating ChatGPT's role during an eight-week Object-Oriented Programming II course. Students used ChatGPT for project assignments, and their experiences were analyzed through open-ended questionnaires.