Examining the Usage of Generative AI Models in Student Learning Activities for Software Programming

Abstract--The rise of Generative AI (GenAI) tools like Chat-GPT has created new opportunities and challenges for computing education. Existing research has primarily focused on GenAI's ability to complete educational tasks and its impact on student performance, often overlooking its effects on knowledge gains. In this study, we investigate how GenAI assistance compares to conventional online resources in supporting knowledge gains across different proficiency levels. We conducted a controlled user experiment with 24 undergraduate students of two different levels of programming experience (beginner, intermediate) to examine how students interact with ChatGPT while solving programming tasks. We analyzed task performance, conceptual understanding, and interaction behaviors. Our findings reveal that generating complete solutions with GenAI significantly improves task performance, especially for beginners, but does not consistently result in knowledge gains. Importantly, usage strategies differ by experience: beginners tend to rely heavily on GenAI toward task completion often without knowledge gain in the process, while intermediates adopt more selective approaches. We find that both over-reliance and minimal use result in weaker knowledge gains overall. Based on our results, we call on students and educators to adopt GenAI as a learning rather than a problem solving tool. Our study highlights the urgent need for guidance when integrating GenAI into programming education to foster deeper understanding. The rapid development of Generative Artificial Intelligence (GenAI) has led to its widespread adoption across various domains to boost productivity and streamline workflows. Large Language Models (LLMs), such as OpenAI's ChatGPT and Codex, Google Gemini, and GitHub Copilot, have been integrated into domains including software engineering [1], [2], healthcare [3], education [4], creative writing [5], [6], and digital music [7], offering capabilities such as code generation, question answering, and image generation. These authors contributed equally to this work. Some studies evaluated GenAI's performance on programming tasks [8], user interface design education [9], and computer vision coursework [10]. Others focused on assessing the accuracy and usability of GenAIgenerated responses [11], [12].