# The Impact of Large Language Models on Programming Education and Student Learning Outcomes
## 2 Research Background
Failure to equip students with AI skills may leave them at a competitive disadvantage in the job market, underscoring the need for an educational framework that both employs and scrutinizes AI tools for students' benefit. Similarly, this was noted in another study [7], where the author stressed that the emergence of ChatGPT as a tool in education makes training for faculty and students necessary in order to maximize its utility. It is important that educators familiarize themselves with ChatGPT's functions, including evaluating accuracy and distinguishing between text and idea generation. It is recommended that educators encourage students to use ChatGPT, fostering equal opportunity for idea development and improving writing skills. As ChatGPT evolves, universities may integrate it with learning management systems, and specialized academic versions may be developed. ChatGPT is expected to enhance creativity and critical thinking skills by contrasting generated ideas with original human input. As education adapts to technological advancements, students will require skills such as the critical evaluation of information and effective presentation, which will be assessed through methods like presentations and defending work in collaboration with ChatGPT. The paper provides practical examples for utilizing ChatGPT in academic writing and suggests adopting its techniques for academic research and publication. Universities and educators are encouraged to adapt these suggestions to suit their specific needs and courses. As stated in another systematic literature review that focuses on using ChatGPT in education [8], ChatGPT has the potential to enhance the teaching and learning process by offering personalized learning experiences, improving student motivation and engagement, facilitating collaboration, and providing quick access to information. However, challenges such as teacher training, ethical considerations, accuracy of responses, and data privacy need to be further addressed. Similarly to our research, article [9] focuses on the role of debugging in software development and explores the potential of ChatGPT as a tool in this process. ChatGPT, primarily known for its proficiency in generating high-quality text and engaging in natural language conversations, possesses lesser-known capabilities that are equally remarkable. It can identify errors in code by analyzing it against its training data. However, caution is advised in its use for debugging, as it should be part of a comprehensive software development strategy. While ChatGPT's ability to learn from past debugging sessions and offer natural language suggestions can enhance code quality, it has limitations in domain knowledge or context awareness. Although ChatGPT can automate aspects of debugging and bug fixing, human review and testing remain crucial. Developers should view ChatGPT as a tool to complement their skills rather than replace them, using its suggestions as a starting point for further consideration and testing. Integrating ChatGPT with other debugging techniques can enhance the efficiency and effectiveness of the software development process, ultimately leading to higher-quality software delivery. Comparable statements about the use of LLMs as an aid in the process of debugging software solutions can also be made for LLM tools other than ChatGPT, like GitHub's Copilot. Authors in [10] investigate Copilot's capability in code generation, comparing its outputs with those crafted by humans. Their findings demonstrate that Copilot can produce accurate and efficient solutions for certain fundamental algorithmic problems. However, the quality of its generated code heavily relies on the clarity and specificity of the provided prompt by the developer. Moreover, the results of this study suggest that Copilot requires further refinement in comprehending natural language inputs to effectively act as a pair programmer.