Language and Communication Students' Perceived Mastery in AI Chatbot Prompt Engineering: A Study on Vibe Coding in Educational Mobile Application Development

Abstract

This study examines language and communication students’ self-perceived mastery of AI chatbot prompt engineering, with particular attention to “vibe coding” for educational mobile application development. In this study, vibe coding refers to the deliberate use of prompts to shape tone, style, audience orientation, and user-facing educational content. Using a pattern-based framework centred on roles, constraints, and examples, the study investigates how students combine structure and exploratory prompting. A quantitative cross-sectional survey was administered online during March–July 2025 (Semester 2). Complete responses from 120 undergraduates were analysed from approximately 126 invited students (analytic response rate = 95.2%). The questionnaire demonstrated excellent overall internal consistency (Cronbach’s ? = .965). Results indicate that students usually begin tasks with structured prompts but later move towards mixed or unstructured prompting styles, suggesting a control-then-explore sequence. Longer exposure to AI chatbots and more extensive prompt-engineering training were associated with higher self-perceived competency and output efficiency, whereas weekly usage frequency did not show statistically significant differences. Educational background was associated with prompting style and usage, but not with overall perceived competency or output efficiency, and gender differences were negligible. The findings suggest that scaffolded instruction in prompt engineering can support more confident and consistent student use of AI chatbots. Future research should triangulate self-reports with behavioural logs, archived prompts, and performance-based outputs.