Vibe Coding for UX Design: Understanding UX Professionals' Perceptions of AI-Assisted Design and Development

2025-09-12

AI Summary

This study investigates how generative AI–enabled “vibe coding”—the rapid prototyping and code generation driven by natural language instructions—reconfigures UX workflows and collaborative practices. Drawing on in-depth interviews with 20 UX practitioners, and integrating insights from human–computer interaction and organizational behavior theory, the research identifies a four-phase practice model (ideation → generation → debugging → review) and introduces a conceptual tension framework between *intentional design* and *design intention*. Results indicate that vibe coding significantly accelerates prototype iteration and lowers technical barriers for designers; however, it concurrently introduces critical challenges—including diminished code reliability, integration complexity, ambiguous accountability, contested creative ownership, and erosion of team trust—thereby exposing risks of skill atrophy and professional stigmatization. The study contributes a theoretically grounded, empirically validated framework for responsible human–AI co-design in UX practice.

Technology Category

• Humans and AI: User Experience and Usability
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Generative AI is reshaping UX design practices through "vibe coding," where UX professionals express intent in natural language and AI translates it into functional prototypes and code. Despite rapid adoption, little research has examined how vibe coding reconfigures UX workflows and collaboration. Drawing on interviews with 20 UX professionals across enterprises, startups, and academia, we show how vibe coding follows a four-stage workflow of ideation, AI generation, debugging, and review. This accelerates iteration, supports creativity, and lowers barriers to participation. However, professionals reported challenges of code unreliability, integration, and AI over-reliance. We find tensions between efficiency-driven prototyping ("intending the right design") and reflection ("designing the right intention"), introducing new asymmetries in trust, responsibility, and social stigma within teams. Through the lens of responsible human-AI collaboration for AI-assisted UX design and development, we contribute a deeper understanding of deskilling, ownership and disclosure, and creativity safeguarding in the age of vibe coding.

Problem

• Examining AI's impact on UX workflows and collaboration dynamics
• Investigating challenges of code reliability and AI over-reliance
• Exploring tensions between prototyping efficiency and design reflection

Innovation

• Natural language translation into functional prototypes
• Four-stage workflow from ideation to review
• Balancing efficiency with responsible human-AI collaboration