Magy Seif El-Nasr is Professor and Department Chair of Computational Media at the University of California, Santa Cruz, where she directs the Game User Interaction and Intelligence (GUII) Lab. She has been recently appointed the UC Presidential Chair at Santa Cruz to support her work on AI literacy and innovations in AI and Education. Her research focuses on developing new methods that push the boundary of research in understanding and augmenting human experiences towards making social impact in areas of education, training, and health. Elin Carstensdottir is Assistant Professor of Computational Media at the University of California, Santa Cruz, where she directs the Interaction Dynamics (ID) Lab. Her research focuses on the development and design of research tools and games for impact, where she investigates the dynamics between interfaces, AI and user experience with a focus on interactive narrative, virtual characters and social simulation. Michael John is Associate Teaching Professor of Computational Media at the University of California, Santa Cruz, where he directs the Games and Playable Media MS Degree Program, teaching courses in game development as well as games for impact. He previously worked in the commercial industry for both entertainment games and educational games, including as a co-founder of the GlassLab educational games studio.
ENDORSEMENTS “The most grounded and multi-voiced introduction to serious games available—told by the people who’ve actually built, sold, and studied them.” —David J. Gagnon, Director, Field Day Lab, University of Wisconsin–Madison; founder of Vault Learning Games and Open Game Data “An excellent textbook detailing the design of serious games that weaves together industry-driven and science-driven perspectives to create a powerful user-centered design process.” —Jan L. Plass, Paulette Goddard Chair, Digital Media and Learning Sciences, and Co-Director, Games for Learning Institute, New York University; coeditor of Handbook of Game-Based Learning; coauthor of Rethinking Cognitive Load Theory