Version 1
: Received: 1 February 2022 / Approved: 3 February 2022 / Online: 3 February 2022 (15:17:15 CET)
How to cite:
Choudhury, N. Can Artificial Intelligence be Used to Improve Productivity by Automating Elements of the User Experience Design Processes?. Preprints2022, 2022020057. https://doi.org/10.20944/preprints202202.0057.v1.
Choudhury, N. Can Artificial Intelligence be Used to Improve Productivity by Automating Elements of the User Experience Design Processes? . Preprints 2022, 2022020057. https://doi.org/10.20944/preprints202202.0057.v1.
Cite as:
Choudhury, N. Can Artificial Intelligence be Used to Improve Productivity by Automating Elements of the User Experience Design Processes?. Preprints2022, 2022020057. https://doi.org/10.20944/preprints202202.0057.v1.
Choudhury, N. Can Artificial Intelligence be Used to Improve Productivity by Automating Elements of the User Experience Design Processes? . Preprints 2022, 2022020057. https://doi.org/10.20944/preprints202202.0057.v1.
Abstract
Artificial Intelligence (AI) has many advantages over humans in that it can detect incredibly subtle patterns within large quantities of data. This study suggests using AI algorithms in user research tasks for mining variables ranging from the tone of voice, image banks, historical records, and product use to determine where brands sit and where there could be competitive advantages. This study reviews the current design processes of User Experience (UX) designers and the advancements in Deep Learning (DL). It identifies potential areas of automation in the Preparation, Incubation and Illumination stages of the design process. It recognises the possibility of using Long Short-Term Memory (LSTM) models to automate design feedback and the creation of alternate designs with Generative Adversarial Networks (GANs).
Keywords
Artificial Intelligence; Deep Learning; Design Processes; Graphic Design; User Experience
Subject
ARTS & HUMANITIES, Media Studies
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
