The evolution of LLM has resulted in coding-focused models that are able to produce code snippets with high accuracy. More and more AI coding assistant tools are now available, leading to greater integration of AI coding assistants into integrated development environments (IDEs). These tools introduce new possibilities for enhancing software development workflows and changing programming processes. 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. Different users use these tools differently due to differences in their perception, prior experience, and demographics. Furthermore, differences in feature use may affect users’ programming process and skills, especially for programming learners such as computer science students. While prior work has evaluated the performance of LLMdriven code generation tools, their use and usefulness for developers, especially computer science students, remain underexplored. Given that these tools are available to students, it’s important to understand how they use them, as this will affect their learning and programming processes. By analyzing how students interact with these tools, we aim to understand the implications of AIassisted programming and the demographic and prior experience factors that impact its use and trust toward 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 in which participants completed a survey after completing an open-source project issue using GitHub Copilot as part of a course. Study participants were engineering students enrolled in a software engineering course at a US university. Students were introduced to all the GitHub Copilot features, and they were free to use GitHub Copilot features as they seemed fit for the open-source project contribution. We analyzed students’ use of each feature and their perceived usefulness. Further, we explore and analyze significant differences in GitHub Copilot usage and students’ perceptions of it based on demographic factors. Our results show that students used the GitHub Copilot chat feature and code generation feature more than other features. Gender, programming proficiency, and familiarity with AI impacted the usage of the GitHub Copilot feature for assistance in completing the open-source project contribution.