Synopsis
Artificial Intelligence for I-O Psychologists: Research and Applications is the first comprehensive handbook dedicated to helping industrial-organizational psychologists navigate, understand, and shape the rapidly evolving landscape of artificial intelligence in the world of work. Edited by Georgi P. Yankov, Isaac Thompson, and Ivan Hernandez, this volume brings together 31 chapters written by 81 leading scholars, practitioners, technologists, and policy experts. Its purpose is clear: to provide authoritative, scientifically grounded, and practice-oriented guidance on how AI is transforming assessment, organizational research, and talent management-and how I-O psychologists can lead this transformation responsibly.
The handbook is organized into five sections that mirror the major domains in which AI is reshaping I-O psychology. The opening section offers historical context, emerging trends, and theoretical foundations, explaining why AI represents a paradigm shift for the field. Subsequent sections explore how AI is revolutionizing assessment development and scoring, from automated interviews and simulations to generative item creation and novel data types. Additional chapters examine AI's influence on organizational psychology topics such as leadership development, coaching, DEI initiatives, teamwork with AI agents, workplace monitoring, stress, and access to work.
A major focus of the handbook is equipping I-O psychologists to collaborate effectively with data scientists and engineers. Chapters introduce readers to product development methods, MLOps, evaluation and audit frameworks, and AI-enabled approaches to research design. The final section addresses the ethical, legal, and regulatory implications of AI-based HR technologies, offering practical guidance for ensuring fairness, transparency, and compliance in high-stakes organizational contexts.
Throughout the volume, contributors balance innovation with scientific rigor, offering both critical commentary and practical tools. Readers will find roadmaps for adopting AI responsibly, frameworks for validating AI-based scores, and insights into future trends that will define the next decade of work psychology.
About the Author
Georgi Yankov, Ph.D., is Senior Scientist of Assessment Innovation at Hogan Assessment Systems, specializing in psychometrics, individual differences, and machine learning. He has led major projects on personality and intelligence tests, work-attitudes surveys, competency models, and organizational diagnostics. Previously at DDI, he developed LLM-powered products that score written behavioural simulations with human-level reliability. His work appears in journals such as Personnel Psychology, PAID, TIP, and JTPP, and at SIOP conferences. He has authored books on MMPI forensic applications and personality. Georgi holds master's degrees from Sofia University, Baruch College (Fulbright), and a Ph.D. in I-O Psychology from Bowling Green State University.
Isaac Thompson, Ph.D., serves as Senior Research Scientist at Amazon, leading the development and implementation of science-based AI tools in the hiring domain. Previously, as Director of Data Science at Modern Hire, he created novel AI-scored assessments used by 50% of the Fortune 100. Before that, Thompson was the second data scientist ever hired at Red Hat, where he led advanced analytics projects during the company's high-growth years. His research applies machine learning and deep learning to personnel selection, psychometrics, and organizational measurement. Thompson has published in top-tier journals and chairs the annual SIOP Machine Learning Competition. He earned his Ph.D. in Industrial-Organizational Psychology from North Carolina State University.
Ivan Hernandez, Ph.D., serves as Assistant Professor and Director of the Computational Organizational Research Lab at Virginia Tech. He earned his Ph.D. in Social/Organizational Psychology from the University of Illinois at Urbana-Champaign in 2015, with postdoctoral training at Northwestern University. His research applies computational methods and neural networks to organizational science, with publications in Organizational Research Methods and Journal of Personality and Social Psychology. Hernandez has secured significant research funding, including a $1.5 million project from the Army Research Institute. He led the winning team in the 2023 SIOP Machine Learning Competition and has presented workshops at institutions including MITRE Corporation and Michigan State University.
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