Preface Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant Number 2434184, Collaborative Research: EducateAI: CUE-T: Designing Artificial Intelligence Curricula for All Undergrads. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the editor, authors, and contributors and do not necessarily reflect the views of the National Science Foundation.

I would like to thank Berea College for its support for student internships and for the Berea College students:

You can see their contributions as well as those of others at github.com/pearcej/ai4all/forks.

For use in Section 1.1 the Berea College Internship Program gratefully acknowledges the U.S. Department of Justice, Office of Justice Programs, National Institute of Justice, for allowing us to reproduce, in part or in whole, the article, A Brief History of Artificial Intelligence. The opinions, findings, and conclusions or recommendations expressed in this book are those of the author(s) and do not necessarily represent the official position or policies of the U.S. Department of Justice.

Section 1.1 was also was informed by Introduction to Artificial Intelligence, authored by Microsoft and the World Travel & Tourism Council (WTTC). The original publication includes a copyright notice that permits redistribution under specified conditions but requires that the content not be amended. Accordingly, this textbook uses the publication as a reference for background information and organization rather than adapting or modifying its copyrighted text. The original work therefore remains under its own licensing terms, while the original text for this textbook is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

Section 1.2 was developed using several openly licensed educational resources. Portions of this section were adapted from chapters “Understanding Artificial Intelligence” and “Social Implications of AI” in The Future is Now: Empowering Society Through AI Literacy, by Jason S. Wrench and Sanae Elmoudden, published by Milne Open Textbooks, and from A People’s Guide to AI, by Mimi Onuoha and Diana Nucera. The Turing Test subsection was largely abridged from The Turing Test by Diane Proudfoot, published in the Open Encyclopedia of Cognitive Science (MIT Press, 2024). A People’s Guide to AI, and The Future is Now: Empowering Society Through AI Literacy are licensed under the Creative Commons Attribution–NonCommercial–ShareAlike 4.0 International (CC BY-NC-SA 4.0) license while “The Turing Test” is distributed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, all of which permit adaptation with attribution. The material incorporated into this textbook has been revised, reorganized, and expanded to fit its learning objectives, terminology, and style. The original sources remain under their respective licenses, while the original text written for this textbook is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

Chapter 2 was developed using several openly licensed educational resources. Portions of Section 2.1, Section 2.3, Section 2.4, and Section 2.6 were adapted from the Student Guide to Artificial Intelligence, published by Elon University in partnership with the AAC&U (CC BY-NC-SA 4.0). Additional material in Section 2.1 draws upon Faster Completion, Less Learning by Sina Rismanchian and Hasan Uzun and Thinking Less, Trusting More by Rudrajit Choudhuri et al. (both CC BY 4.0). Section 2.2 was informed by cognitive science research, including works by Soderstrom & Bjork, Dunlosky et al., and open-access review literature (CC BY 4.0). Section 2.3 also incorporates How AI Can Help You by Colin de la Higuera and Jotsna Iyer (CC BY 4.0). Section 2.4 includes content from Prompt Engineering Survey by Tong Xiao and Jingbo Zhu (CC BY-NC 4.0) and Prompting Guide by Elvis Saravia / DAIR.AI (MIT License). Finally, Section 2.5 draws from AI Literacy by Kainan Jarrette and Diana Daly (CC BY-NC-SA 4.0) and AI Hallucination from Students’ Perspective by Abdulhadi Shoufan and Ahmad-Azmi-Abdelhamid Esmaeil (CC BY 4.0). All underlying works permit adaptation with attribution. The material incorporated into this chapter has been revised, reorganized, and expanded to fit its learning objectives, terminology, and style. Original sources remain under their respective licenses, while new text is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

Chapter 3 was developed using several openly licensed educational resources. Material for Section 3.1 was adapted from CC 315 - Data Structures & Algorithms II, published by Kansas State University (CC BY-NC-SA 4.0), and MIT OpenCourseWare’s Artificial Intelligence (CC BY-NC-SA 4.0), with Figure 3.1.7 generated using FigureLabs and additional images sourced from CC 315 - Data Structures & Algorithms II. Content for Section 3.2 uses snippets from State-Space Search in AI: A Complete Guide published by Economics Town and draws from MIT OpenCourseWare’s Artificial Intelligence (CC BY-NC-SA 4.0), with section diagrams generated using ChatGPT. Section 3.3, Section 3.4, and Section 3.5 draw upon foundational concepts from Machine Learning Systems, by Vijay Janapa Reddi (CC BY-NC-SA 4.0); MIT Data-Centric AI Course Materials (CC BY-NC-SA 4.0); AI for Teachers: an Open Textbook, by Colin de la Higuera and Jotsna Iyer (CC BY 4.0); and NeuroAI Course (CC BY 4.0). All underlying works permit adaptation with attribution. The material incorporated into these sections has been revised, reorganized, and expanded to fit the learning objectives, terminology, and style of this textbook. Original sources remain under their respective licenses, while new text is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

Chapter 4 was developed using several openly licensed educational resources. Section 4.1 was developed using Data and Datasets, authored by Dr. Shaun V. Ault, Dr. Soohyun Nam Liao, and Larry Musolino, published by OpenStax (CC BY 4.0). Both Section 4.2 and Section 4.3 were adapted from Online Statistics Education: A Multimedia Course of Study, led by David M. Lane (Public Domain). Finally, Section 4.4 was modified from A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle, by Harini Suresh and John Guttag (CC BY-NC 4.0), with additional material adapted from On Splitting Training and Validation Set, by Yun Xu and Royston Goodacre, published in the Journal of Analysis and Testing (CC BY 4.0). All underlying works permit adaptation with attribution. The material incorporated into this chapter has been revised, reorganized, and expanded to fit its learning objectives, terminology, and style. The original sources remain under their respective licenses, while the original text written for this textbook is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

Chapter 5 was developed using several openly licensed educational resources. Section 5.1 draws upon Deep Neural Networks, by Colin de la Higuera and Jotsna Iyer (CC BY 4.0), alongside Python Machine Learning Projects, by Lisa Tagliaferri, Michelle Morales, Ellie Birbeck, and Alvin Wan, and Chapter 4: Learning Introduction, by MIT OpenCourseWare (both CC BY-NC-SA 4.0). Both Section 5.2 and Section 5.3 were adapted from Python Machine Learning Projects, MIT OpenCourseWare’s Chapter 4: Learning Introduction, and The Little Book of Deep Learning, by François Fleuret (all CC BY-NC-SA 4.0). Section 5.4 incorporates material from MIT OpenCourseWare’s Chapter 4: Learning Introduction and Fleuret’s The Little Book of Deep Learning (both CC BY-NC-SA 4.0). Finally, Section 5.5 was adapted from MIT OpenCourseWare’s Chapter 4: Learning Introduction (CC BY-NC-SA 4.0) and Artificial Intelligence and Librarianship, by Martin Frické (CC BY 4.0). All underlying works permit adaptation with attribution. The material incorporated into this chapter has been revised, reorganized, and expanded to fit its learning objectives, terminology, and style. The original sources remain under their respective licenses, while the original text written for this textbook is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

Chapter 6 was developed using several openly licensed educational resources. Section 6.1, Section 6.2, and Section 6.4 were adapted from Neural Networks and Deep Learning, by Michael A. Nielsen (CC BY-NC 3.0), with images of handwritten digits sourced from Nielsen’s work, the sigmoid curve generated via ChatGPT, and additional section images generated via Google Gemini. Section 6.3 incorporates material from Nielsen’s Neural Networks and Deep Learning (CC BY-NC 3.0) and Convolutional Neural Networks Chapter Notes, by MIT OpenCourseWare (CC BY-NC-SA 4.0), alongside images generated via Google Gemini. Finally, Section 6.5 was adapted from An Introduction to Neural Networks and Deep Learning, by Tong Xiao and Jingbo Zhu (CC BY-NC 4.0). All underlying works permit adaptation with attribution for noncommercial purposes. The material incorporated into this chapter has been revised, reorganized, and expanded to fit its learning objectives, terminology, and style. The original sources remain under their respective licenses, while the original text written for this textbook is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

Chapter 7 was developed using several openly licensed resources. Section 7.1 and Section 7.2 were both adapted from Understanding Potential Sources of Harm throughout the Machine Learning Life Cycle, by Harini Suresh and John Guttag (CC BY 4.0), and A People’s Guide to AI, by Mimi Onuoha and Diana Nucera (CC BY-NC-SA 4.0), with Section 7.1 also drawing from Algorithmic Redistricting and Black Representation in US Elections, by Zachary Schutzman (CC BY 4.0). Section 7.3 was adapted from E-waste Challenges of Generative Artificial Intelligence, by Peng Wang and Ling-Yu Zhang (CC BY 4.0); The Water Footprint of Data Centers, by Md Abu Bakar Siddik, Arman Shehabi, and Landon Marston (CC BY 4.0); The Environmental Cost of Generative AI, by Cooper Elsworth and Keguo Huang (CC BY 4.0); The Environmental Impact of Data Centres, by David Mytton (CC BY 4.0); and Energy and AI, published by the International Energy Agency (IEA) (CC BY 4.0). Section 7.4 was adapted from “AI Literacy” in Decoding Deception, by Kainan Jarrette and Diana Daly (CC BY-NC-SA 4.0), and Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security, by Robert Chesney and Danielle K. Citron (CC BY-NC-SA 4.0). Section 7.5 was adapted from AI for Teachers: An Open Textbook, by Colin de la Higuera and Jotsna Iyer (CC BY 4.0), and Artificial Intelligence and Librarianship: Notes for Teaching, by Martin Frické (CC BY 4.0). Section 7.6 was adapted from “Social Implications of AI” in The Future is Now: Empowering Society Through AI Literacy, by Jason S. Wrench and Sanae Elmoudden (CC BY-NC-SA 4.0); “Is it true that robots and AI will take away people’s jobs?” in Better Together: How to Create a More Just and Equitable World Through Artificial Intelligence, by Fazil Acar (CC BY-NC-SA 4.0); and Automation, AI & Work, by Laura D. Tyson and John Zysman (CC BY-NC 4.0). All underlying works permit adaptation with attribution. The material incorporated into this chapter has been revised, reorganized, and expanded to fit its learning objectives, terminology, and style. The original sources remain under their respective licenses, while the original text written for this textbook is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

Section 1.3, Section 2.7, Section 5.6, Section 6.6, and Section 7.7 were developed by adapting and synthesizing content from several openly licensed sources. These sections were adapted from AI Myths and Misunderstandings by Vassilis Galanos, SJ Bennett, Ruth Aylett, and Drew Hemment, published in The New Real; AI Literacy by Kainan Jarrette and Diana Daly; Social Implications of AI by Jason S. Wrench and Sanae Elmoudden, published in The Future is Now: Empowering Society Through AI Literacy; Deconstructing AI Myths: A Comprehensive Exploration of Misconceptions and Realities in Artificial Intelligence by Louie Giray, published in Higher Learning Research Communications; A Critical Examination of Machine Learning Myths and Misconceptions: An Exploratory Study by Sidharta Chatterjee; AI Myths Debunked: A Guide to Understanding Artificial Intelligence by Sunish Vengathattil; The Myth of the AI Apocalypse by Constance de Saint Laurent; Artificial Intelligence: A Clarification of Misconceptions by Frank Emmert-Streib, Olli Yli-Harja, and Matthias Dehmer; Myths and Misconceptions About Artificial Intelligence: A Review by Arne Bewersdorff, Xiaoming Zhai, Jessica Roberts, and Claudia Nerdel; A Comprehensive Review of AI Myths and Misconceptions by Frank Nussbaum; and "It’s Going to Kill Us!" and Other Myths About the Future of Artificial Intelligence by Robert D. Atkinson. Most sources are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, with the exception of The Myth of the AI Apocalypse, which is under the Creative Commons Attribution 3.0 International (CC BY 3.0) license, and AI Literacy and Social Implications of AI, which are under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. The material incorporated into this textbook has been revised, reorganized, and expanded to fit its learning objectives, terminology, and style. The original sources remain under their respective licenses, while the original text written for this textbook is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

This book was authored in PreTeXt. Hence, I would like to thank Rob Beezer for the creation of PreTeXt and for his responsiveness in the PreTeXt support channels. Additionally, I am very grateful to Oscar Levin both for his responsiveness in the Runestone Discord channels and for work in the creation of PreText converter for Pandoc which made adaptation of some materials much less time consuming.

Brad Miller deserves a special thanks for his work in creating Runestone Academy where this book is hosted, for his collaborative work with the PreTeXt authoring group, as well as for his responsiveness in the Runestone Discord channels.

Finally, I would like to thank my husband, Bob Fairchild, for his patience and for being my best friend.