Generating User Experience Based on Personas with AI Assistants

TL;DR

This work addresses the gap in adaptive UX by proposing a framework that combines Large Language Models with richly detailed personas to generate user interfaces tailored to individual needs. It outlines a structured research plan—encompassing foundational literature review, persona-focused analysis, LLM capability exploration, and iterative framework development driven by user feedback—to create a dynamic, runtime-responsive UX design methodology. The anticipated contribution is a practical, evolving framework that leverages prompting techniques and expert insights to translate persona information into adaptive UI designs, with potential to significantly improve personalization and accessibility in software interfaces. The approach aims to bridge technical capabilities of LLMs with nuanced human-centric UX requirements, offering a path toward more flexible and user-centered digital experiences.

Abstract

Traditional UX development methodologies focus on developing ``one size fits all" solutions and lack the flexibility to cater to diverse user needs. In response, a growing interest has arisen in developing more dynamic UX frameworks. However, existing approaches often cannot personalise user experiences and adapt to user feedback in real-time. Therefore, my research introduces a novel approach of combining Large Language Models and personas, to address these limitations. The research is structured around three areas: (1) a critical review of existing adaptive UX practices and the potential for their automation; (2) an investigation into the role and effectiveness of personas in enhancing UX adaptability; and (3) the proposal of a theoretical framework that leverages LLM capabilities to create more dynamic and responsive UX designs and guidelines.