Unlocking Adaptive User Experience with Generative AI

Anuradha Madugalla; Chetan Arora; John Grundy; Shruti Mahajan; Tanjila Kanij; Yutan Huang

Unlocking Adaptive User Experience with Generative AI

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arxiv 2404.05442 v1 pith:YNSCI3DI submitted 2024-04-08 cs.HC cs.SE

Yutan Huang , Tanjila Kanij , Anuradha Madugalla , Shruti Mahajan , Chetan Arora , John Grundy This is my paper · ORCID

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keywords adaptiveuserchatgptdevelopinterfacespersonascontentdata

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Developing user-centred applications that address diverse user needs requires rigorous user research. This is time, effort and cost-consuming. With the recent rise of generative AI techniques based on Large Language Models (LLMs), there is a possibility that these powerful tools can be used to develop adaptive interfaces. This paper presents a novel approach to develop user personas and adaptive interface candidates for a specific domain using ChatGPT. We develop user personas and adaptive interfaces using both ChatGPT and a traditional manual process and compare these outcomes. To obtain data for the personas we collected data from 37 survey participants and 4 interviews in collaboration with a not-for-profit organisation. The comparison of ChatGPT generated content and manual content indicates promising results that encourage using LLMs in the adaptive interfaces design process.

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Leveraging LLMs for Persona-Based Visualization of Election Data
cs.HC 2025-07 reject novelty 3.0 of 10

LLM-generated voter personas are used to derive design criteria and prototypes for UK election visualizations, which are then evaluated by another LLM.

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