AI Literacy: Three-credit, Semester-long course on AI Literacy

Below is a sample course guide for a three-credit, semester-long course on AI Literacy designed and taught by a librarian. This outline includes a course description, learning objectives, suggested weekly topics, assessment ideas, and key readings or resources. Instructors can adapt the scope and depth of each subject based on institutional needs and the background of enrolled students.

Course Title and Description

Course Title: AI Literacy in the Information Age

Course Description:

This course introduces students to the rapidly evolving field of artificial intelligence (AI), focusing on developing critical, ethical, and informed practices in using AI-generated information. Students will learn foundational concepts in AI, evaluate AI-generated content for accuracy and bias, explore the social and environmental impacts of large-scale AI adoption, and practice responsible research techniques in the AI era. The course will also delve into the practical applications of AI, sparking students' interest and engagement. Emphasis will be placed on librarians' skills in teaching information literacy—source evaluation, ethical use of information, and inquiry-based learning.

Course Learning Objectives

By the end of this course, students should be able to:

1. Explain core AI concepts: Describe how AI and machine learning models (including generative AI) are developed and how they produce outputs.
2. Evaluate AI outputs: Apply information literacy skills, especially lateral reading and source evaluation, to determine the reliability and accuracy of AI-generated content.
3. Identify and analyze bias: Recognize common biases in AI tools, discuss how these biases are introduced, and assess the impact of biased outputs in real-world contexts.
4. Understand ethical and legal considerations: Examine major ethical, legal, and environmental questions surrounding AI, including issues of data privacy, intellectual property, and sustainability.
5. Use AI responsibly: Demonstrate best practices for incorporating AI into academic, professional, or personal research, including proper citation, collaboration, and tool selection.
6. Engage in critical reflection: Articulate balanced perspectives on AI's benefits and limitations and propose ways to mitigate its potential harms.

Recommended Course Materials

• Primary Texts/Articles (these can be substituted with equivalent resources):
  • Race After Technology by Ruha Benjamin
  • Artificial Unintelligence by Meredith Broussard
  • Selected scholarly articles on AI ethics, copyright, and bias from reputable journals (e.g., Communications of the ACM, Journal of Information Ethics).
  • Reports from organizations like the Algorithmic Justice League, Partnership on AI, and Stanford Institute for Human-Centered AI.
• Supplementary Texts/Resources:
  • Online documentation of popular AI tools (ChatGPT, Bard, Bing Chat, Midjourney, DALL·E, etc.).
  • Video lectures, podcasts, and webinars from leading AI researchers and ethicists.

Course Outline (15 Weeks)

Below is a suggested weekly breakdown for a 15-week semester. Each week includes a Topic, Key Concepts/Readings, and Hands-on Activities/Assignments. These activities are designed to make students feel involved and active in their learning process. Instructors can modify the depth of each module and assign readings accordingly.

Week 1: Introduction to AI and Information Literacy

• Key Concepts: Definition of AI, Machine Learning vs. Traditional Computing, Generative AI, Overview of Information Literacy
• Readings/Resources: Introductory article on AI (e.g., a concise overview from a reputable tech news source), short video on the importance of information literacy
• Activities:
  • Course overview and expectations
  • Diagnostic quiz on AI basics
  • Discussion: "What do we already know (or think we know) about AI?"

Week 2: AI Throughout History and Society

• Key Concepts: Historical milestones in AI (symbolic AI, neural networks), AI in everyday life (recommendation systems, voice assistants)
• Readings/Resources: Excerpts from Artificial Unintelligence (Broussard) or a brief historical survey of AI breakthroughs
• Activities:
  • Timeline creation: Students map key AI milestones
  • Reflection post: "Personal encounters with AI."

Week 3: Generative AI Tools and Their Mechanisms

• Key Concepts: Large Language Models (LLMs), text generation, image generation, prompt engineering
• Readings/Resources: A basic technical overview of ChatGPT or other leading models
• Activities:
  • Hands-on exploration: students try out a text-based generative AI tool (in a guided manner)
  • Discussion: Where do generative AI tools succeed or fail?

Week 4: Evaluating AI-Generated Content (1) — Fact-Checking

• Key Concepts: Lateral reading, source verification, detecting hallucinations and fabrications
• Readings/Resources: Articles on misinformation in AI-generated text; examples of AI hallucinations
• Activities:
  • Workshop: Students practice lateral reading on short AI-generated passages
  • Assignment: Fact-check a paragraph of AI-generated text, documenting the verification process

Week 5: Evaluating AI-Generated Content (2) — Bias and Stereotypes

• Key Concepts: How bias enters training data, examples of biased outputs, societal impact
• Readings/Resources: Race After Technology (Benjamin) chapters on bias in automated systems
• Activities:
  • Group discussion: Examine a case study of algorithmic bias (e.g., facial recognition bias)
  • Reflective writing: "What are the real-world consequences of biased AI tools?"

Week 6: Ethical and Legal Considerations (1) — Intellectual Property and Citation

• Key Concepts: AI and copyright, AI-authorship concerns, citing AI outputs, fair use
• Readings/Resources: Articles on AI-generated art controversies, institutional policies regarding AI-generated work
• Activities:
  • Guest lecture from a campus copyright specialist or IP lawyer (if available)
  • Assignment: Compare a properly cited AI-assisted paper vs. a paper using AI without attribution

Week 7: Ethical and Legal Considerations (2) — Privacy and Data Protection

• Key Concepts: Data collection for AI training, user data privacy, regulatory frameworks (GDPR, CCPA)
• Readings/Resources: Selections from data privacy advocacy organizations, relevant legal cases
• Activities:
  • Scenario analysis: Students evaluate hypothetical situations where AI tools handle sensitive information
  • Midterm Project Check-In: Students propose topics for the final project or paper

Week 8: Environmental Impact of AI

• Key Concepts: Energy consumption in model training, environmental costs of large data centers, sustainable AI initiatives
• Readings/Resources: Research articles on AI's carbon footprint, tech sustainability reports
• Activities:
  • Class debate: "Do the benefits of AI outweigh the environmental costs?"
  • Reflection: Students brainstorm ways to reduce the carbon footprint of AI

Week 9: Socioeconomic Considerations and Labor

• Key Concepts: Automation and displacement of labor, the gig economy and AI, new job roles in AI era
• Readings/Resources: Articles on AI and the future of work; case studies on content moderation and gig-work labeling
• Activities:
  • Discussion: "Who benefits most from AI deployments, and who might be left behind?"
  • Group presentations: Quick research on emerging 'AI economy' jobs

Week 10: Prompt Engineering and Effective Use of AI

• Key Concepts: Crafting prompts for quality outputs, iterative refinement, domain-specific AI tools (e.g., medical, legal, creative)
• Readings/Resources: Guides/tutorials on advanced prompt techniques, best practices
• Activities:
  • Workshop: Students try refining prompts across multiple iterations
  • Peer critique: Partners evaluate each other's prompts and results

Week 11: Application in Academic Research

• Key Concepts: Integrating AI into literature reviews, generating research questions, summarizing articles, finding relevant datasets
• Readings/Resources: Articles describing AI's role in academic or professional research settings
• Activities:
  • Assignment: Use an AI tool for a preliminary literature review on a chosen topic; compare with database research
  • Reflective writing: "Strengths and weaknesses of AI for my discipline"

Week 12: Collaboration, Creativity, and AI

• Key Concepts: Co-creating with AI for writing, design, and multimedia; understanding the human-AI interface
• Readings/Resources: Studies on AI in creative industries; best practices for writer/designer-AI collaboration
• Activities:
  • Hands-on exploration: Students experiment with an AI image-generator for creative projects
  • Class discussion: "Is creativity lost or augmented with AI assistance?"

Week 13: Strategies for Instruction and Outreach

• Key Concepts: How to teach AI literacy, library-led workshops, equity in access to AI tools
• Readings/Resources: Library and education association statements on AI literacy, open educational resources for teaching AI
• Activities:
  • Small-group collaboration: Design a brief AI literacy workshop for a target audience (e.g., K–12, undergraduates, community members)
  • Peer feedback sessions on workshop plans

Week 14: Final Project Workshop and Reflection

• Key Concepts: Synthesizing course knowledge, peer review of final projects
• Readings/Resources: Students' chosen references for final projects
• Activities:
  • In-class presentations: Each student (or group) presents progress on their final project
  • Instructor and peer feedback for refinement

Week 15: Final Presentations and Looking Forward

• Key Concepts: Future trends in AI, personal action plans, research frontiers, policy developments
• Activities:
  • Final project presentations (can be written papers, digital projects, or multimedia works)
  • Reflective Discussion: "How will AI evolve, and how do we stay informed?"
  • Course wrap-up and evaluations

Assessment and Grading

Below are possible assessment components to ensure students meet the learning objectives:

1. Participation and Discussion (15–20%)
   • Regular contributions in class discussions, online discussion forums, or small-group activities.
   • Engagement with assigned readings and willingness to reflect on personal learning progress.
2. Short Writing Assignments (20–25%)
   • Weekly response posts to assigned articles or videos.
   • Brief reflection papers on ethical dilemmas, bias incidents, or prompt-engineering exercises.
3. Midterm Project (20%)
   • Students propose and complete a short project halfway through the course, such as a fact-checking exercise or a small annotated bibliography evaluating multiple AI-based sources.
   • Emphasis on demonstrating lateral reading, source verification, and critical use of AI.
4. Final Project or Paper (30–35%)
   • A comprehensive research paper, multimedia presentation, or digital project.
   • Students analyze a specific AI tool or phenomenon, integrating scholarly research, factual verification, and ethical considerations.
   • Projects should reflect a command of core AI literacy skills and exhibit a balanced perspective on opportunities and risks.
5. Peer Reviews and Presentations (5–10%)
   • Students provide constructive feedback to peers on prompts, outlines, or drafts.
   • Class presentations foster communication skills and collaborative learning.

Teaching Methods and Classroom Activities

• Lectures and Interactive Discussions: Presentations on core AI concepts and related ethics and Q&A sessions to clarify complex ideas.
• Hands-On Workshops: Guided labs where students experiment with AI tools (text, image, or other specialized AI).
• Case Studies: Group analysis of real-world incidents, such as controversies over AI-generated art or unethical data collection.
• Role-Plays and Debates: Students adopt different stakeholder perspectives—e.g., AI developers, librarians, legal professionals, or activists—and debate AI policy or ethical guidelines.
• Reflective Journals: Written reflection prompts at key intervals encourage ongoing personal engagement with the course material.
• Guest Speakers: Inviting experts (librarians with tech experience, AI ethics researchers, and attorneys specializing in IP law) can enrich classroom discussions.

 Additional Notes

• Course Format: The course can be adapted for in-person, hybrid, or online delivery. Consider interactive discussion boards, recorded lectures, and real-time virtual workshops for online or hybrid models.
• Prerequisites: Some familiarity with essential computer use and willingness to learn about new technologies. Technical requirements (e.g., programming experience) are not required, as the focus is on critical engagement rather than coding.
• Accessibility: Ensure that all readings and activities are accessible to students with disabilities. Provide alternative text for images and captions/transcripts for videos.
• Updating Content: AI evolves quickly. Instructors should plan to update resources, readings, and examples each semester to reflect the latest developments and tools.

 Sample Final Project Ideas

1. "AI in My Discipline" Research Paper: Students delve into how AI is currently impacting—or is poised to impact—a particular field (e.g., health sciences, education, business, art), focusing on ethical implications and best practices for responsible use.
2. Toolkit for AI Literacy: Students create a publicly accessible toolkit or LibGuide highlighting key AI tools, evaluation strategies, guidelines for ethical citation, and recommended resources. This could be tailored for a particular audience (e.g., high school students, fellow undergraduates, or the public library community).
3. Case Study Analysis: Students select a real-world incident where AI was deployed problematically or successfully. They examine the stakeholders, identify where biases or ethical lapses arose, and propose policy recommendations or mitigation strategies.
4. Original Data Collection or Survey: Learners design and administer a small survey to gauge peers' understanding and attitudes toward AI. They analyze the responses and present recommendations for improving AI literacy on campus.

Conclusion

This three-credit AI Literacy course aims to equip students with the intellectual tools to navigate and critically engage with emerging AI technologies. Led by a librarian who brings expertise in information literacy and familiarity with the research process, the course offers a grounding that emphasizes ethics, accuracy, and responsibility. By combining conceptual understanding with hands-on practice, students develop a nuanced perspective on how AI can be integrated into their academic, professional, and personal lives—without losing sight of the broader social, environmental, and ethical contexts in which AI operates.

Instructors are encouraged to adapt readings, weekly topics, and assessments to match evolving AI developments, disciplinary interests, and students' skill levels. Above all, the course should foster a culture of inquiry and reflection, ensuring that AI is not merely embraced uncritically but understood and used thoughtfully to serve the common good.