# The Impact of Large Language Models on Programming Education and Student Learning Outcomes
## 6 Conclusions  
This study embarked on an exploration of the nuanced roles that informal usage of Large Language Models (LLMs) like ChatGPT plays in the learning outcomes of undergraduate students within programming education. Focused on a ten-week experimental framework involving thirty-two participants, this research specifically addressed how LLM usage impacts students' capacities for independent task implementation and knowledge acquisition in software development, with a particular emphasis on React applications. More specifically, our research shows the following. RQ1: The analysis directly answered the primary question concerning the overall impact of LLM usage on programming education outcomes. We identified a significant negative correlation between the average use of LLMs and students' final grades ([PERSON]'s rho = \(-0.347\), \(p\) = 0.026), clearly suggesting that an increased general reliance on LLMs correlates with diminished academic performance in programming assignments. RQ2: The study further dissected the impact of LLMs based on their specific uses--code generation, seeking additional explanations, and debugging. We found significant negative correlations for code generation ([PERSON]'s rho = \(-0.305\), \(p\) = 0.045) and debugging ([PERSON]'s rho = \(-0.360\), \(p\) = 0.021), supporting the hypotheses that these forms of LLM usage negatively affect students' ability to independently solve programming tasks. Conversely, the use of LLMs for seeking additional explanations did not significantly impact final grades ([PERSON]'s rho = \(-0.201\), \(p\) = 0.135), indicating its potential viability as a supplementary educational resource. Our study highlights the need for the balance necessary in leveraging LLMs within programming education. While LLMs can undoubtedly serve as powerful tools for enhancing learning through supplementary explanations, their role in critical thinking-intensive tasks like code generation and debugging appears to negatively influence student outcomes. This separation underscores the imperative for educators to integrate LLMs into their pedagogical strategies thoughtfully, ensuring they augment rather than undermine the development of core programming skills. While our findings provide valuable insights into the impact of LLMs on programming education, we acknowledge that our study is limited by the scale of the dataset. The relatively small sample size of thirty-two participants may not fully capture the broad spectrum of educational outcomes associated with LLM use in diverse educational settings. Additionally, the ten-week duration of our experimental framework may not adequately reflect long-term learning trajectories and the sustained impact of LLM usage on student capabilities. These limitations suggest the need for caution in generalizing our results across all programming education contexts. Further research involving larger sample sizes and extended study periods is essential to validate and refine our findings. In the future, it is important that further investigations delve into the specific dynamics through which LLMs influence learning processes and outcomes. Such research should aim to refine strategies for integrating these technologies into educational frameworks, maximizing their benefits while mitigating potential drawbacks. Exploring the differential impacts of LLM usage across various learning styles and educational contexts will also be crucial for tailoring AI-enhanced learning experiences to diverse student needs. Ultimately, a balanced approach that combines AI assistance with human guidance holds promise for optimizing learning experiences in the realm of software development and beyond. Conceptualization, G.J. and V.T. ; methodology, G.J. and S.K. ; validation, G.J. and S.K. ; formal analysis, G.J. and S.K. ; investigation, G.J., V.T. and S.K. ; resources, G.J. and V.T. ; data curation, G.J. and S.K. ; writing--original draft preparation, G.J., V.T. and S.K. ; writing--review and editing, G.J., V.T. and S.K. ; visualization, S.K.