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AI/IA

An AI Literacy class for information professionals.

Course Description

The rapid evolution of algorithms for processing cultural artifacts has ushered in a new era of computational tools, exemplified by large language models and generative AI systems. These advancements are redefining the landscape of information work and information behavior, while introducing complex new challenges in ethics, intellectual property, and literacy.

This course builds a new AI Literacy through understanding how these tools function, exploring their applications, and critically engaging with their ethical, legal, and societal challenges. Through hands-on experience and critical analysis, students will develop both practical skills and theoretical understanding of AI’s role in the information society.

This is the closest thing to the maxim of this course: Deft use of AI isn’t in the service of replacing you, but working with you to focus on, iterate, inspect, and realize your ideas.

Learning Outcomes and Skills Tree

Students must demonstrate competency across five key areas. The final portfolio of labs allows you to demonstrate specialties of your choosing, but you will need to show grounding for all key competencies.

Critical Analysis, Fluency, and Evaluation

  • Evaluate AI outputs for accuracy, bias, and appropriateness
  • Demonstrate ability to identify potential limitations, biases and failure modes
  • Demonstrate critical thinking in evaluating AI outputs and claims
  • Demonstrate ability to bootstrap knowledge of existing tools evaluate appropriateness of (self-skilling)

Society, Ethics, and Policy

  • Understand ethical implications and societal impacts of AI systems
  • Apply frameworks for evaluating AI fairness, transparency, and accountability
  • Analyze policy considerations and regulatory approaches to AI governance
  • Consider issues of privacy, consent, and data rights in AI applications

Technical Understanding

  • Demonstrate foundational knowledge of machine learning concepts and architectures
  • Understand key AI concepts like neural networks, transformers, and embeddings
  • Show ability to explain technical concepts to non-technical audiences
  • Recognize the role of data and compute power in modern AI systems

Skilling and Productivity

  • Demonstrate proficiency in using AI tools for meeting a range of information, knowledge management, and productivity needs
  • Demonstrate deftness with prompt construction and refinement techniques, including fundamental strategies like chain-of-thought and few-shot learning
  • Understand research-informed approaches to effective prompting approaches

Art and Creativity

  • Demonstrate an ability to work with AI to grow and realize ideas
  • Demonstrate understanding of AI’s creative capabilities and limitations
  • Show ability to thoughtfully integrate AI into creative workflows
  • Apply AI tools to enhance rather than replace human creativity

Creativity in the Class

A tool that can help you learn and execute better can free you to be more creative.

Many of the labs in this course are open-ended. This is by design, and is part of what you’re being evaluated on - the creativity, playfulness, and appropriateness of how you conceive of the tool.

Schedule Overview

Class is organized into 5 multi-week models.

Module 1: Introduction and Technical Foundations. Weeks 1, 2.

Topics: Introduction to AI in the Modern World, Technical Foundations of Modern AI, Core Concepts.

Module 2: Working with Generative AI Systems. Weeks 3, 4, 5.0.

Topics: Prompt Engineering Fundamentals, Co-Creativity and Professional Practice, Evaluation and Comparison Methods.

Module 3: Information Access and Analysis. Weeks 5.1, 6.

Topics: AI in Information Behavior, Classification and Subject Access, Information Organization.

Module 4: Ethics, Bias, and Governance. Weeks 7, 8.

Topics: Ethics and Bias in AI Systems, Safety Considerations, Regulatory Frameworks.

Module 5: Advanced and Emerging Topics. Weeks 9, 10.

Topics: Multimodal Systems, Advanced Applications, Future Directions in AI.

Assessment Structure

Your grade is comprised of 1000 pts. There is only one Assignment, by typical definitions, but it is is a portfolio comprised of multiple components that you complete or iterate throughout the entire class.

Lab Portfolio (700pts)

Progress Report (75pts, Due: Week 7, 2 hours before class) + Portfolio (625pts, Due: Monday after Week 10, 11:59pm)

Collect 9 Labs from throughout the quarter that you feel represent your best work.

Nine may seem like a lot, but some are Big Labs that count for two in the portfolio. (If it’s easier - you can think of it as nine lab ‘points’, where labs may be worth one point or two).

The majority of labs will be performed throughout the course of learning in class and homework. You are welcome to edit, improve, or redo them if you want to include them in the portfolio, and some labs will have extra requirements if including in the portfolio. There are a few optional labs that won’t be in class, but you can do to round out your portfolio.

Your final portfolio must include at least one lab representing each of the five core competency areas: Critical Analysis, Fluency, and Evaluation; Society, Ethics, and Policy; Technical Understanding; Skilling and Productivity; and Art and Creativity. This ensures a well-rounded demonstration of your AI literacy capabilities across all essential domains.

To ensure that you’re compiling your portfolio throughout the quarter, there’s a progress report submission due in Week 7. This consists of an In-Progress Draft of the Full Portfolio, compiling 4-5 Labs up to that point.

Class Participation (150pts)

Class attendance, contribution to class discussion, engagement with material and exercises.

Lab Participation (150pts)

Even if not including them in your portfolio, many labs will be part of our class learning, in building your AI literacy skills. Your participation grade reflects your consistent, good-faith engagement with these activities. This isn’t about perfection - it’s about demonstrating genuine effort to learn and grow your capabilities.

Format

Structure

The structure of each week includes:

  • Lecture, discussion, and exercises
  • An ungraded skill check quiz (ungraded, first 10 minutes, small group format)
  • Time for lab work

Technology Use

You will need to bring your computer to class each week. If you don’t have one available, the classroom has computers in the lab.

You’ll be expected to have an account with a leading-edge ‘chat bot’, such as:

  • Claude
  • ChatGPT
  • Google AI Studio
  • Google Gemini

Assignments are designed around free capabilities of these tools. I don’t want to compel you to pay for paid plans, though you’re welcome to. If you have to choose one, my current recommendation is Claude. With free plans, there are sometimes usage limits, so avoid doing all your home at the last minute or have accounts across multiple tools.

The best tools tend to be from large companies. We’re in a place where companies are spending more than they’re making as they compete for your future allegiance. What can be done with free accounts can change at any time. If that’s the case and it challenges the completion of an exercise or assignment, let me know and we’ll work it out. Incidentally, be willing to try new things and don’t give any particular company your allegiance - nothing good (for consumers) comes from technology companies that no longer feel like that should work for your business.

For some class use, the quality of small models that run on your computer has improved drastically. Your mileage may vary, depending on your specific computer. If you want to try installing a model that runs only on your own computer - optionally - one good mix of tools is Llama or Deepseek R1 (models) with ollama (software for using the model) and Open Web UI (a nice front end to your model). What your computer can run, however, is inevitably limited, and many exercises will still require an account to one of the hosted services listed above.

Student Expectations

Honor Code

All work submitted in this course must be your own and produced exclusively for this course. The use of sources (ideas, quotations, paraphrases) must be properly acknowledged and documented. For the consequences of violating the Academic Misconduct policy, refer to the University of Denver website on the Honor Code (studentaffairs.du.edu/student-rights-responsibilities/honor-code)

AI Use in the Classroom

In this class, you are welcome to use artificial intelligence tools where they enhance rather than replace your learning and creative process. AI can be used to help you understand concepts, brainstorm ideas, and refine your work. However, it should not be used to complete assignments without meaningful engagement or to bypass important learning experiences.

When in doubt, consider the underlying expectations on plagiarism and intellectual integrity that make up the honor code for every single class: don’t pass off contributions that are not your own as if they were. If still in doubt, ask your instructor.

The most straightforward ‘inappropriate’ use is to writing for you - asking a system to generate a completed narrative for you. Yet, even in the context of writing, you can use it to help edit, you can use it to bounce ideas around or organize it, and so on. Indeed, you’re encouraged to do so, especially as you develop the skills and intuition to ask the right questions and invite constructive criticism. You’re also encouraged to share your prompts, strategies, and learnings with your classmates - we learn best by seeing how others approach these tools.

Inclusive Learning Environments

In this class, we will aim for a learning environment that is inclusive and respectful. Our diversity may be reflected by differences in race, culture, age, religion, sexual orientation, socioeconomic background, and myriad other social identities and life experiences. Inclusiveness encourages and appreciates expressions of different ideas, opinions, and beliefs, so that conversations and interactions that could potentially be divisive turn instead into opportunities for intellectual and personal enrichment.

A dedication to inclusiveness requires respecting what others say, their right to say it, and the thoughtful consideration of others’ communication. Both speaking up and listening are valuable tools for furthering thoughtful, enlightening dialogue. Respecting one another’s individual differences is critical in transforming a collection of diverse individuals into an inclusive, collaborative and excellent learning community. Our core commitment shapes our core expectation for behavior inside and outside of the classroom.