Students' Perspective on AI Code Completion: Benefits and Challenges

TL;DR

This study explores computer science students' perceptions of AI code completion tools, revealing benefits like increased productivity and challenges such as over-reliance, through interviews and a custom research tool.

Contribution

It introduces AutoAurora, an open-source VS Code extension powered by StarCoder, and provides empirical insights into students' experiences with AI code completion.

Findings

AI tools improve coding efficiency and offer alternative solutions.

Over-reliance may hinder understanding and creativity.

Students desire more explainability and best practice guidance.

Abstract

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.