Date of Award

Spring 2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Program/Concentration

Occupational and Tenical Studies (Workforce & Organizational Development)

Committee Director

Michael Kosloski

Committee Member

Helen Crompton

Committee Member

Philip A. Reed

Abstract

The purpose of this study is to investigate how artificial intelligence (AI) is currently employed in workforce learning and development. The study examined the types of AI employed and the affordances realized for organizations and employees. A PRISMA systematic review methodology was utilized to address the overarching problem statement and answer the three questions guiding the study. The PRISMA extension Preferred Reporting Items for Systematic Reviews and Meta Analysis for Protocols was used to direct each phase of the research. In addition, the Preferred Reporting Items for Systematic Reviews and Meta Analysis was used to conduct the article selection process. Findings revealed studies were distributed predominantly between Asia, Europe, and the United States. For types of AI, grounded coding resulted in three prominent trends: 1) Applied AI, 2) machine learning, and 3) natural language processing. Grounded coding revealed five trends for affordances: 1) learning approaches, 2) learner experience, 3) usability 4) organization efficiency and 5) cost. This systematic review is limited as it only examined peer-reviewed journal and conference publications available in English with reported organizational and learning outcomes. This study is unique in that it provides organizations and educators with the first systematic review examining AI’s role in the development and delivery of workforce training and development.

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DOI

10.25777/ctdq-8178

ISBN

9798280754768

ORCID

0000-0002-9879-2477

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