  AI Code Completion (e.g., GitHub's Copilot) has revolutionized how computer
science students interact with programming languages. However, AI code
completion has been studied from the developers' perspectives, not the
students' perspectives who represent the future generation of our digital
world. In this paper, we investigated the benefits, challenges, and
expectations of AI code completion from students' perspectives. To facilitate
the study, we first developed an open-source Visual Studio Code Extension tool
AutoAurora, powered by a state-of-the-art large language model StarCoder, as an
AI code completion research instrument. Next, we conduct an interview study
with ten student participants and apply grounded theory to help analyze
insightful findings regarding the benefits, challenges, and expectations of
students on AI code completion. Our findings show that AI code completion
enhanced students' productivity and efficiency by providing correct syntax
suggestions, offering alternative solutions, and functioning as a coding tutor.
However, the over-reliance on AI code completion may lead to a surface-level
understanding of programming concepts, diminishing problem-solving skills and
restricting creativity. In the future, AI code completion should be explainable
and provide best coding practices to enhance the education process.
