APA
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Huang, Y., Kanij, T., Madugalla, A., Mahajan, S., Arora, C., & Grundy, J. (2024). Unlocking Adaptive User Experience with Generative AI. International Conference on Evaluation of Novel Approaches to Software Engineering.
Chicago/Turabian
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Huang, Yutan, Tanjila Kanij, Anuradha Madugalla, Shruti Mahajan, Chetan Arora, and John Grundy. “Unlocking Adaptive User Experience with Generative AI.” International Conference on Evaluation of Novel Approaches to Software Engineering (2024).
MLA
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Huang, Yutan, et al. “Unlocking Adaptive User Experience with Generative AI.” International Conference on Evaluation of Novel Approaches to Software Engineering, 2024.
BibTeX Click to copy
@article{yutan2024a,
title = {Unlocking Adaptive User Experience with Generative AI},
year = {2024},
journal = {International Conference on Evaluation of Novel Approaches to Software Engineering},
author = {Huang, Yutan and Kanij, Tanjila and Madugalla, Anuradha and Mahajan, Shruti and Arora, Chetan and Grundy, John}
}
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.
