Evaluating Generative AI-Based User Interfaces Using an Integrated Neutrosophic Multi-Criteria Decision-Making Framework

Abstract

User Interface (UI) design can be seen as an essential aspect of human-computer interaction (HCI) and makes communication easier between people and technology. In today's digital economy, interface quality has become one of the most important business concerns, since it has a direct impact on customer satisfaction and retention while affecting revenue. Although creating user-centered and accessible interfaces is crucial, doing so is a difficult and time-consuming process, which leads to burnout for many usability professionals. Although conventional artificial intelligence (AI) was utilized for design assessment and automation, the arrival of generative AI technology has created new possibilities for automated interface creation. This paper aims to provide an understanding of the systematic application of generative artificial intelligence in practical UI design methods. Additionally, this paper analyzes existing innovative generative techniques including zero-shot design prompting, structured grammar-based design creation and computer-aided fine-tuning techniques is analyzed. As a result, the study assesses the success of generative applications in fulfilling the real-world UI requirements, including usability and digital accessibility aspects. Overall, such research is meant to provide a comprehensive evaluation approach in the sphere of generative artificial intelligence technologies in the context of practical design problems.