Technology Category
Humans and AI: User Experience and UsabilityNatural Language Processing: Code Generation / Program Synthesis from Natural LanguageCognitive Modeling & Cognitive Systems: Computational Creativity
Exploring Student-AI Interactions in Vibe Coding
🤖 AI Summary
This study investigates how novice and advanced software engineering students differ in their AI-augmented programming behaviors on the Replit platform (“vibe coding”). Method: Using concurrent think-aloud protocols paired with screen recording, we empirically observed both groups building web applications; thematic analysis was applied to identify recurring interaction patterns. Contribution/Results: (1) Both groups predominantly engaged in test–debug cycles, rarely proactively reviewing generated code. (2) Advanced students systematically embedded functional requirements and code context into prompts, significantly improving AI output relevance and usability. (3) Programming experience shifts AI collaboration from tool invocation toward contextualized co-construction. This work is the first to characterize an “experience gradient” in vibe coding—where expertise drives a qualitative evolution in AI interaction paradigms—providing empirical grounding for designing intelligent programming environments and fostering computational thinking.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Study student-AI interactions in vibe coding platforms
Compare interactions by programming background level
Analyze Replit usage patterns during web app development
Innovation
Methods, ideas, or system contributions that make the work stand out.
Using vibe coding platform Replit
Think-aloud interviews for interaction analysis
Thematic analysis of student-AI interactions
🔎 Similar Papers
No similar papers found.
