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Demystifying AI: A Practical Introduction for Instructors

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Demystifying AI: A Practical Introduction for Instructors

Social Sciences
Author
Pamela L. Huntington
Publisher
Merced College
Source
LibreTexts
License
CC BY
Language
EN
Category
Basic Sciences
Read online Download PDF (open licence)
Contents· 96 sections
  1. 01: An Introduction to AI
  2. 1.01: What is a language model
  3. 1.1.01: How language models generate text by predicting the next word
  4. 1.1.02: Examples of large language models used in education (e.g. ChatGPT Bard)
  5. 1.1.03: How Instructors and Students Are Using Large Language Models in the Classroom
  6. 1.02: What does AI learn from (Basics of training data)
  7. 1.2.01: Overview of training data sources (books websites code etc.)
  8. 1.2.02: The role of data quality and quantity in shaping AI behavior
  9. 1.2.03: Limitations- bias outdated info and misinformation in training sets
  10. 1.03: The role of algorithms in prediction and content generation
  11. 1.3.01: What an algorithm is and how it powers AI decision-making
  12. 1.3.02: How AI predicts text images and more based on patterns
  13. 1.3.03: Key difference between rules (algorithms) and examples (training data)
  14. 1.04: Examples of input-output prompts (e.g. ChatGPT DALLE)
  15. 1.4.01: What prompts are and how wording affects output
  16. 1.4.02: Sample prompts for ChatGPT and DALLE across disciplines
  17. 1.4.03: Introduction to prompt refinement (aka prompt engineering)
  18. 1.05: Where AI gets it wrong- hallucinations bias outdated info
  19. 1.5.01: What hallucinations are and why AI makes confident errors
  20. 1.5.02: Common sources of bias and misinformation in outputs
  21. 1.5.03: Why educators should always verify AI-generated content
  22. 1.06: Why AI doesnt think or understand the way humans do
  23. 1.6.01: How AI predicts rather than understands or reasons
  24. 1.6.02: Differences between AI-generated language and human cognition
  25. 1.6.03: Implications for teaching- critical AI literacy for instructors and students
  26. 02: Practical AI Applications in Teaching and Learning
  27. 2.01: AI for Course Planning
  28. 2.1.01: Drafting syllabi learning outcomes and course descriptions
  29. 2.1.02: Generating module overviews or unit plans
  30. 2.1.03: Sequencing learning activities with AI assistance
  31. 2.02: AI for Assignment and Activity Design
  32. 2.2.01: Designing case studies or scenarios tailored to your course
  33. 2.2.02: Using AI to generate examples metaphors or analogies
  34. 2.03: AI for Feedback and Assessment
  35. 2.3.01: Drafting rubric language
  36. 2.3.02: Using AI to provide formative feedback (with caveats)
  37. 2.3.03: Reviewing student reflections or written responses for patterns
  38. 2.04: AI for Student Support
  39. 2.4.01: Suggesting writing starters or research questions
  40. 2.4.02: Summarizing complex readings or concepts
  41. 2.4.03: Translating or adapting content for multilingual learners
  42. 2.05: AI for Instructor Productivity
  43. 2.5.01: Composing announcements assignment descriptions and reminders
  44. 2.5.02: Drafting professional emails or recommendation letters
  45. 2.5.03: Organizing meeting notes or to-do lists
  46. 2.06: Discipline-Neutral Examples
  47. 2.6.01: For STEM humanities social sciences CTE and the arts
  48. 2.6.02: Prompts you can adapt directly for Canvas discussions or assignments
  49. 2.07: What to Watch Out For
  50. 2.7.01: When AI is helpfuland when it might backfire
  51. 2.7.02: Importance of verifying AI-generated content
  52. 2.7.03: Transparency and ethics in using AI for instruction
  53. 03: Teaching with AI- Boundaries Bias and Best Practices
  54. 3.01: AI and Academic Integrity
  55. 3.1.01: What counts as cheating with AI
  56. 3.1.02: Should instructors disclose their own AI use
  57. 3.1.03: Encouraging transparency in student assignments
  58. 3.02: Bias and Limitations in AI
  59. 3.2.01: How AI reflects human biases in its training data
  60. 3.2.02: Risks of inaccurate or misleading information
  61. 3.2.03: Equity concerns for marginalized student groups
  62. 3.03: Student Privacy and Data Security
  63. 3.3.01: Equity concerns for marginalized student groups
  64. 3.3.02: FERPA considerations
  65. 3.3.03: Platform privacy policies (e.g. ChatGPT Grammarly Copilot)
  66. 3.04: Transparency and Consent
  67. 3.4.01: Notifying students when AI is used in course design or grading
  68. 3.4.02: Inviting students to opt in out of AI-based tools or supports
  69. 3.05: AI and Accessibility
  70. 3.5.01: How AI can support UDL and learners with disabilities
  71. 3.5.02: Cautions about assuming AI is accessible by default
  72. 3.06: Frameworks and Guidelines
  73. 3.6.01: Sample institutional or course-level policies
  74. 3.6.02: Short AI use declarations instructors can adopt
  75. 04: Moving Forward with AI in Your Teaching
  76. 4.01: Recap- What You Now Know About AI
  77. 4.1.01: Revisit the basics practical uses and ethical considerations
  78. 4.1.02: Acknowledge whats still evolving
  79. 4.02: Your AI Teaching Toolkit
  80. 4.2.01: A checklist of low-barrier tools or practices to try
  81. 4.2.02: Questions to ask when evaluating new AI tools
  82. 4.03: Reflection Prompts
  83. 4.3.01: How might AI help you solve a persistent teaching challenge
  84. 4.3.02: What concerns or hopes do you have about student use of AI
  85. 4.3.03: Where do you feel most confidentand least confidentabout using AI
  86. 4.04: Start Small- Low-Risk Ways to Try AI
  87. 4.4.01: Drafting assignment descriptions
  88. 4.4.02: Using AI to brainstorm quiz questions
  89. 4.4.03: Co-writing announcement templates
  90. 4.05: Ongoing Learning
  91. 4.5.01: 5.5.1-
  92. 4.5.02: Suggested blogs videos podcasts or newsletters
  93. 4.5.03: Staying up-to-date with tools and policy changes
  94. 4.06: Final Encouragement
  95. 4.6.01: Theres no one right way to use AI.
  96. 4.6.02: Curiosity and critical thinking are your most valuable tools.