# Large Language Models in Computer Science Classrooms: Ethical Challenges and Strategic Solutions
## 3 LLMs for Computer Science Studies: Advantages and Challenges
The study acknowledges challenges such as enhancing natural language understanding and managing large datasets, suggesting ongoing AI development and specific strategies such as content partitioning and advanced API development for future improvements. [PERSON] et al. [41] investigated the effects of an LLM-based assistant, called CodeTutor, on entry-level undergraduate students. They divided participants into an experimental group, which used CodeTutor along with standard educational materials and TA support, and a control group, which relied solely on traditional learning methods and TA support. The experimental group exhibited statistically significant improvements in their final scores, especially those with no prior experience with LLM tools. However, the study also highlighted concerns regarding CodeTutor's ability to foster critical thinking, with a noticeable shift over time as students increasingly preferred human TA support. This observation emphasizes the necessity for a balanced educational approach that integratesboth LLM tools and human instruction to effectively develop critical thinking and problem-solving skills while also promoting Generative AI literacy within the curriculum. While the above studies have examined the use of general-purpose LLMs by students as personalized assistants, there also exist specialized tools based on LLMs specifically designed to act as personalized tutors, some of which are designed to assist in code writing and computer science studies. [PERSON] et al. [59] introduced CodeHelp, an innovative LLM-powered virtual teaching assistant designed to support students in programming and computer science courses without directly providing solution code. This tool, integrated into an introductory computer and data science course, aims to complement instructor and TA support by offering real-time, always-available assistance. With robust guardrails preventing the direct output of solutions, CodeHelp minimizes the risks of over-reliance while helping students to address programming challenges. Over a semester, students used the tool primarily for debugging and implementation queries, highlighting a focus on immediate problem-solving rather than deeper conceptual understanding. The study found that students' queries often lacked sophistication or important details, such as error or expected outcome information, suggesting a need for targeted instruction to improve their interaction with the tool. Despite these limitations, students who used CodeHelp more frequently demonstrated modestly higher course performance, underscoring its potential as a supportive learning resource. The researchers also analyzed the patterns and effectiveness of CodeHelp usage. Debugging queries comprised the majority (48%) of student submissions, followed by implementation-related queries (38%), while understanding-related queries were less common (9%). This distribution indicates that students primarily sought immediate, task-specific assistance, rather than engaging with broader conceptual questions. Additionally, many queries were minimal or copied directly from the course instructions, reflecting a lack of active student input in directing assistance. CodeHelp's structured design and guardrails successfully mitigated such risks, demonstrating a modest positive correlation between tool usage and course performance. These findings suggest that thoughtfully designed LLM-powered tools such as CodeHelp can enhance programming education while addressing concerns about over-reliance and the undermining of critical thinking. [PERSON] et al. [32] introduced CodeAid, a purpose-built LLM-powered programming assistant designed to provide technically correct and helpful responses without directly revealing solutions. CodeAid was deployed in a 12-week introductory programming course with 700 students to support the diverse needs of the students, including conceptual queries, debugging, and code explanation, while emphasizing pedagogical principles.