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Developing Functional Literacy of Machine Learning Among UX Design Students.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Developing Functional Literacy of Machine Learning Among UX Design Students./
Author:	Srivastava, Akshat.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	84 p.
Notes:	Source: Masters Abstracts International, Volume: 83-05.
Contained By:	Masters Abstracts International83-05.
Subject:	Design. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28845263
ISBN:	9798460491964

Developing Functional Literacy of Machine Learning Among UX Design Students.
Srivastava, Akshat.

Developing Functional Literacy of Machine Learning Among UX Design Students. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 84 p.

Source: Masters Abstracts International, Volume: 83-05.

Thesis (M.Des.)--University of Cincinnati, 2021.

This item must not be sold to any third party vendors.

Machine Learning (ML) plays an increasingly important role in modern user experience (UX) design practice. Being one of the fastest evolving tech phenomena within the `AI boom,' ML stands out as a field that could significantly benefit from more creative and critical thinking. Simultaneously, UX design students can benefit from increased literacy and competency in the already pervasive, highly relevant technology. However, contemporary UX design education fails to sufficiently empower young designers to work with cutting-edge technologies such as ML.A considerable amount of research has been conducted around designers' (lack of) comprehension of ML. These prior studies focus on identifying and discussing the challenges faced by UX design practitioners in designing for ML; not much research exists to propose and/or evaluate solutions for designers' lack of comprehension of ML. Further, none of the research in the UX design for ML space has focused on UX design students or education so far, though much of it identifies the lack of education of designers about ML as a major issue.Based on an analytical review of 88 introductory educational resources on UX design for ML, this thesis establishes a starting point for design educators to incorporate ML into undergraduate design curricula. It makes three primary contributions: 1) a set of guidelines for introductory education on UX design for ML, 2) a taxonomy of ML capabilities, use cases, and exemplars, and 3) a sample course proposal that demonstrates the application of (1) and (2) in undergraduate design education.

ISBN: 9798460491964Subjects--Topical Terms:

518875
Design.
Subjects--Index Terms:

User Experience Design

Developing Functional Literacy of Machine Learning Among UX Design Students.
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520 $a Machine Learning (ML) plays an increasingly important role in modern user experience (UX) design practice. Being one of the fastest evolving tech phenomena within the `AI boom,' ML stands out as a field that could significantly benefit from more creative and critical thinking. Simultaneously, UX design students can benefit from increased literacy and competency in the already pervasive, highly relevant technology. However, contemporary UX design education fails to sufficiently empower young designers to work with cutting-edge technologies such as ML.A considerable amount of research has been conducted around designers' (lack of) comprehension of ML. These prior studies focus on identifying and discussing the challenges faced by UX design practitioners in designing for ML; not much research exists to propose and/or evaluate solutions for designers' lack of comprehension of ML. Further, none of the research in the UX design for ML space has focused on UX design students or education so far, though much of it identifies the lack of education of designers about ML as a major issue.Based on an analytical review of 88 introductory educational resources on UX design for ML, this thesis establishes a starting point for design educators to incorporate ML into undergraduate design curricula. It makes three primary contributions: 1) a set of guidelines for introductory education on UX design for ML, 2) a taxonomy of ML capabilities, use cases, and exemplars, and 3) a sample course proposal that demonstrates the application of (1) and (2) in undergraduate design education.
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