2026 ASEE Annual Conference & Exposition

The evolution of LLM has resulted in coding-focused models that are able to produce code snippets with high accuracy. We see the availability of various AI-based programming assistant tools, resulting in an increase in integration with development environments. For instance, GitHub Copilot, Claude-code, Junie by JetBrains, and Augment-code are popular AI coding assistants. All these tools have introduced new possibilities for enhancing software development workflows across diverse integrated development environments (IDEs). GitHub Copilot, a popular AI coding assistant, offers features including inline code autocompletion, comment-driven code generation, repository-aware suggestions, and a chat interface for code explanation and debugging. While benchmark evaluations have thoroughly assessed the performance of LLM-driven code generation tools, their usage and usefulness for developers, especially computer science students interacting with them, remain underexplored. By analyzing how students interact with these capabilities, we aim to understand the implications of AI-assisted programming and demographic and prior experience factors that impact its usage and trust towards it. Understanding in this direction will inform the design of future educational tools that responsibly incorporate AI coding support for computing students. For our investigation, we conducted an exploratory survey-based study where participants filled out the survey after completing an open-source project issue with the use and assistance of GitHub Copilot. Study participants were engineering students enrolled in a software engineering course at a US university. The study was paired with the course assignment of open-source contribution. Students provide their feedback through survey participation after completing an open-source issue assignment, where they are shown the features of GitHub Copilot and allowed to freely use available features. These AI capabilities are likely to ignite interest and active use for any help that students may need during programming. We analyzed the level of usage of each feature by the students and their perceived usefulness of it. Further, we explore and analyze significant differences in Copilot usage and students' perceptions of it based on gender and level of familiarity with programming and AI. Our results show that students used the Copilot chat feature and code generation feature more than other features. Gender, programming proficiency, and familiarity with AI impacted the usage of the Copilot feature for assistance in completing the open-source project contribution.