Exploring Student-AI Interactions in Vibe Coding

TL;DR

This study investigates how students with different programming backgrounds interact with vibe coding on Replit, revealing that debugging dominates interactions and advanced students provide more contextually relevant prompts.

Contribution

It provides new insights into student-AI interactions in vibe coding environments and compares behaviors between introductory and advanced programming students.

Findings

Most student interactions focused on testing and debugging.

Advanced students used more contextually relevant prompts.

Students rarely visited the code itself.

Abstract

Background and Context. Chat-based and inline-coding-based GenAI has already had substantial impact on the CS Education community. The recent introduction of ``vibe coding'' may further transform how students program, as it introduces a new way for students to create software projects with minimal oversight. Objectives. The purpose of this study is to understand how students in introductory programming and advanced software engineering classes interact with a vibe coding platform (Replit) when creating software and how the interactions differ by programming background. Methods. Interview participants were asked to think-aloud while building a web application using Replit. Thematic analysis was then used to analyze the video recordings with an emphasis on the interactions between the student and Replit. Findings. For both groups, the majority of student interactions with Replit were to test or debug the prototype and only rarely did students visit code. Prompts by advanced software engineering students were much more likely to include relevant app feature and codebase contexts than those by introductory programming students.