Understanding and Enhancing CS Students’ Interaction Experience with AI Coding Assistant Tools

AI coding assistants (ACATs) are reshaping computer science (CS) education, yet students’ perception and responses to ACATs’ suggestions remains limited understood, especially regarding behavioral patterns, decision-making, and usability challenges. To address this gap, we conducted a study with 27 CS students, examining their interactions with three widely used ACATs across five key dimensions: interaction frequency and acceptance rate, self-perceived productivity, behavioral patterns, decision-making factors, and challenges and expectations. To support this investigation, we developed an experimental platform incorporating a VSCode extension for log data collection, screen recording and automatic generation of personalized interview and survey questions. Our findings reveal substantial variation in ACAT acceptance rates depending on task types, recommendation methods, and content. We propose a novel five-layer interaction behavior model that captures different stages of user interaction. Notable insights include the problem-solving value of rejected AI suggestions, the inefficiencies introduced by modifying existing code that often lead to backtracking, and the high stability of “slowly accepted” suggestions. Moreover, we identify 22 decision-making factors, 11 challenges, and 23 student expectations for future ACAT improvements—such as enhanced debugging accuracy and adaptive learning of individual coding styles. This study contributes actionable design implications for improving ACAT usability, informing student interaction strategies, and guiding future research in human-software interaction, ultimately aiming to better support CS education.