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Chapter 1 References — AI Foundations

Curated resources for deeper exploration of the topics in this chapter.

Books

  • Russell, Stuart, and Peter Norvig. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. The definitive textbook on AI fundamentals; Chapter 1 draws on its accessible framing of intelligent agents and machine learning to explain AI basics to non-technical readers.

  • Mitchell, Melanie. (2019). Artificial Intelligence: A Guide for Thinking Humans. Farrar, Straus and Giroux. Demystifies AI for general audiences, addressing both the promise and the limits of modern machine learning in plain language.

  • Christian, Brian. (2020). The Alignment Problem: Machine Learning and Human Values. W. W. Norton. Explains how large language models are trained and why they sometimes produce confident but incorrect outputs — directly relevant to understanding hallucination.

Articles and Reports

  • Bommasani, Rishi, et al. (2021). "On the Opportunities and Risks of Foundation Models." Stanford HAI. https://arxiv.org/abs/2108.07258 Introduces the term "foundation models" and surveys how large language models work, offering grounding for the GenAI concepts introduced in this chapter.

  • Marcus, Gary, and Ernest Davis. (2019). "Rebooting AI." Wired. https://www.wired.com/story/rebooting-ai Provides an accessible critical perspective on the limits of current AI, helping educators calibrate realistic expectations about what AI can and cannot do.

  • U.S. Department of Education, Office of Educational Technology. (2023). "Artificial Intelligence and the Future of Teaching and Learning." ed.gov. https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf A foundational federal report that defines key AI terms for an education audience and frames opportunities and challenges for schools.

  • Floridi, Luciano, et al. (2018). "AI4People — An Ethical Framework for a Good AI Society." Minds and Machines, 28, 689–707. https://link.springer.com/article/10.1007/s11023-018-9482-5 Establishes accessible definitions of AI types (narrow, general) and ethical principles used throughout this book.

Online Resources

  • Google. (2024). Machine Learning Crash Course. https://developers.google.com/machine-learning/crash-course A free, self-paced introduction to machine learning concepts that administrators and teachers can explore at their own pace.

  • MIT. (2024). Introduction to Deep Learning (6.S191). http://introtodeeplearning.com MIT's openly available introductory course on neural networks and large language models, with accessible lecture videos.

  • OpenAI. (2024). How ChatGPT Works. https://openai.com/blog/chatgpt OpenAI's own explanation of how large language models generate text, including an honest discussion of limitations such as hallucination.

  • UNESCO. (2023). K-12 AI Curricula: A Mapping of Government-Endorsed AI Curricula. https://unesdoc.unesco.org/ark:/48223/pf0000380602 Surveys how countries are introducing AI literacy concepts in K-12 settings, providing global context for this chapter's foundational content.

Videos

  • 3Blue1Brown. (2017). "But What Is a Neural Network?" YouTube. https://www.youtube.com/watch?v=aircAruvnKk A visually stunning and widely praised explanation of how neural networks learn that is accessible to non-technical audiences.

  • Lex Fridman. (2023). "Sam Altman: OpenAI CEO on GPT-4." YouTube. https://www.youtube.com/watch?v=L_Guz73e6fw A conversational deep-dive into how large language models are built and what multimodal AI means in practice.