Analysis of Student-LLM Interaction in a Software Engineering Project
Agrawal Naman, Ridwan Shariffdeen, Guanlin Wang, Sanka Rasnayaka,, Ganesh Neelakanta Iyer

TL;DR
This study investigates how undergraduate students interact with AI assistants like ChatGPT and CoPilot during a software engineering course, revealing preferences, code quality improvements, and pedagogical implications for integrating LLMs into learning.
Contribution
It provides empirical insights into student interactions with LLMs in a pedagogical setting, highlighting preferences, code complexity, and interaction benefits.
Findings
Students prefer ChatGPT over CoPilot.
ChatGPT responses have lower computational complexity.
Conversational interaction improves code quality.
Abstract
Large Language Models (LLMs) are becoming increasingly competent across various domains, educators are showing a growing interest in integrating these LLMs into the learning process. Especially in software engineering, LLMs have demonstrated qualitatively better capabilities in code summarization, code generation, and debugging. Despite various research on LLMs for software engineering tasks in practice, limited research captures the benefits of LLMs for pedagogical advancements and their impact on the student learning process. To this extent, we analyze 126 undergraduate students' interaction with an AI assistant during a 13-week semester to understand the benefits of AI for software engineering learning. We analyze the conversations, code generated, code utilized, and the human intervention levels to integrate the code into the code base. Our findings suggest that students prefer…
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Taxonomy
TopicsHigher Education Learning Practices · Software Engineering Techniques and Practices
