Demystifying AI: A Practical Introduction for Instructors

by Pamela L. Huntington

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This isn’t a book about becoming an AI expert. It’s a book about starting where you are. It’s about understanding what AI is, how it works at a basic level, and how it can (and can’t) support your teaching. It’s written for instructors across disciplines and levels of technical comfort, with real-world teaching in mind—not theoretical research or tech-sector hype.

Description from LibreTexts.

Publisher
Merced College
Subjects
Social Sciences
Licence
CC BY 4.0, as LibreTexts states it
Who can read
Teens profiles and older, judged from its title and subjects

Contents

LibreTexts writes equations in TeX, and here they show as TeX; the PDF has them typeset.

  1. Front Matter
  2. TitlePage
  3. InfoPage
  4. Table of Contents
  5. About the Book
  6. Licensing
  7. 1: An Introduction to AI
  8. 1.1: What is a language model?
  9. 1.1.1: How language models generate text by predicting the next word
  10. 1.1.2: Examples of large language models used in education (e.g., ChatGPT, Bard)
  11. 1.1.3: How Instructors and Students Are Using Large Language Models in the Classroom
  12. 1.2: What does AI "learn" from? (Basics of training data)
  13. 1.2.1: Overview of training data sources (books, websites, code, etc.)
  14. 1.2.2: The role of data quality and quantity in shaping AI behavior
  15. 1.2.3: Limitations- bias, outdated info, and misinformation in training sets
  16. 1.3: The role of algorithms in prediction and content generation
  17. 1.3.1: What an algorithm is and how it powers AI decision-making
  18. 1.3.2: How AI predicts text, images, and more based on patterns
  19. 1.3.3: Key difference between rules (algorithms) and examples (training data)
  20. 1.4: Examples of input-output prompts (e.g., ChatGPT, DALL·E)
  21. 1.4.1: What prompts are and how wording affects output
  22. 1.4.2: Sample prompts for ChatGPT and DALL·E across disciplines
  23. 1.4.3: Introduction to prompt refinement (aka “prompt engineering”)
  24. 1.5: Where AI gets it wrong- hallucinations, bias, outdated info
  25. 1.5.1: What hallucinations are and why AI makes confident errors
  26. 1.5.2: Common sources of bias and misinformation in outputs
  27. 1.5.3: Why educators should always verify AI-generated content
  28. 1.6: Why AI doesn’t “think” or “understand” the way humans do
  29. 1.6.1: How AI “predicts” rather than understands or reasons
  30. 1.6.2: Differences between AI-generated language and human cognition
  31. 1.6.3: Implications for teaching- critical AI literacy for instructors and students
  32. 2: Practical AI Applications in Teaching and Learning
  33. 2.1: AI for Course Planning
  34. 2.1.1: Drafting syllabi, learning outcomes, and course descriptions
  35. 2.1.2: Generating module overviews or unit plans
  36. 2.1.3: Sequencing learning activities with AI assistance
  37. 2.2: AI for Assignment and Activity Design
  38. 2.2.1: Designing case studies or scenarios tailored to your course
  39. 2.2.2: Using AI to generate examples, metaphors, or analogies
  40. 2.3: AI for Feedback and Assessment
  41. 2.3.1: Drafting rubric language
  42. 2.3.2: Using AI to provide formative feedback (with caveats)
  43. 2.3.3: Reviewing student reflections or written responses for patterns
  44. 2.4: AI for Student Support
  45. 2.4.1: Suggesting writing starters or research questions
  46. 2.4.2: Summarizing complex readings or concepts
  47. 2.4.3: Translating or adapting content for multilingual learners
  48. 2.5: AI for Instructor Productivity
  49. 2.5.1: Composing announcements, assignment descriptions, and reminders
  50. 2.5.2: Drafting professional emails or recommendation letters
  51. 2.5.3: Organizing meeting notes or to-do lists
  52. 2.6: Discipline-Neutral Examples
  53. 2.6.1: For STEM, humanities, social sciences, CTE, and the arts
  54. 2.6.2: Prompts you can adapt directly for Canvas discussions or assignments
  55. 2.7: What to Watch Out For
  56. 2.7.1: When AI is helpful—and when it might backfire
  57. 2.7.2: Importance of verifying AI-generated content
  58. 2.7.3: Transparency and ethics in using AI for instruction
  59. 3: Teaching with AI- Boundaries, Bias, and Best Practices
  60. 3.1: AI and Academic Integrity
  61. 3.1.1: What counts as “cheating” with AI?
  62. 3.1.2: Should instructors disclose their own AI use?
  63. 3.1.3: Encouraging transparency in student assignments
  64. 3.2: Bias and Limitations in AI
  65. 3.2.1: How AI reflects human biases in its training data
  66. 3.2.2: Risks of inaccurate or misleading information
  67. 3.2.3: Equity concerns for marginalized student groups
  68. 3.3: Student Privacy and Data Security
  69. 3.3.1: Equity concerns for marginalized student groups
  70. 3.3.2: FERPA considerations
  71. 3.3.3: Platform privacy policies (e.g., ChatGPT, Grammarly, Copilot)
  72. 3.4: Transparency and Consent
  73. 3.4.1: Notifying students when AI is used in course design or grading
  74. 3.4.2: Inviting students to opt in/out of AI-based tools or supports
  75. 3.5: AI and Accessibility
  76. 3.5.1: How AI can support UDL and learners with disabilities
  77. 3.5.2: Cautions about assuming AI is “accessible” by default
  78. 3.6: Frameworks and Guidelines
  79. 3.6.1: Sample institutional or course-level policies
  80. 3.6.2: Short AI use declarations instructors can adopt
  81. 4: Moving Forward with AI in Your Teaching
  82. 4.1: Recap- What You Now Know About AI
  83. 4.1.1: Revisit the basics, practical uses, and ethical considerations
  84. 4.1.2: Acknowledge what’s still evolving
  85. 4.2: Your AI Teaching Toolkit
  86. 4.2.1: A checklist of low-barrier tools or practices to try
  87. 4.2.2: Questions to ask when evaluating new AI tools
  88. 4.3: Reflection Prompts
  89. 4.3.1: How might AI help you solve a persistent teaching challenge?
  90. 4.3.2: What concerns or hopes do you have about student use of AI?
  91. 4.3.3: Where do you feel most confident—and least confident—about using AI?
  92. 4.4: Start Small- Low-Risk Ways to Try AI
  93. 4.4.1: Drafting assignment descriptions
  94. 4.4.2: Using AI to brainstorm quiz questions
  95. 4.4.3: Co-writing announcement templates
  96. 4.5: Ongoing Learning
  97. 4.5.1: 5.5.1-
  98. 4.5.2: Suggested blogs, videos, podcasts, or newsletters
  99. 4.5.3: Staying up-to-date with tools and policy changes
  100. 4.6: Final Encouragement
  101. 4.6.1: There’s no one “right way” to use AI.
  102. 4.6.2: Curiosity and critical thinking are your most valuable tools.
  103. Back Matter
  104. 6.1: Glossary
  105. Detailed Licensing
  106. Glossary
  107. Index