Dan S. Myers

Residential street in the solarpunk city of the future by me using Playground

In this interactive workshop, we'll move beyond policing AI use and learn how to develop AI-first assignments and courses that enhance learning. Attendees will leave with:

• Concrete examples of successful assignments and activities you can adapt to your own classes
• Practical tips for designing AI-first courses, including balancing AI and non-AI skills
• Strategies for teaching students to use AI collaboratively and with agency
• A guided brainstorming and vision activity for the future of AI in your curriculum

For example, this assignment, which we cover in detail during the workshop, shows how to combine AI and reflective journaling into a multi-stage research paper, with specific prompts and guidelines for every phase of the project.

More than a simple survey of tools or prompting techniques, we'll focus on how to incorporate AI into your classes and assignments, giving you new ideas without compromising your values or existing pedagogy.

The workshop is divided into seven sessions of about 40 minutes each.

Morning

Session 1: Teaching and learning in the AI world

• It's okay to have both positive and negative feelings
• The limitations of policing AI use
• Moving from delegation to collaboration
• The continued relevance of critical thinking, information literacy, and written communication as core skills
• Using AI to enhance and extend, not replace: "Like having a minor in everything"
• Practical considerations: choosing an AI for your class

Session 2: Model assignments part 1

• Academic writing and research with AI
• Key idea: combining prompts with reflective journaling
• Reflective AI use in a first-year programming course
• Tips for literature searches

Session 3: Hands-on practice

• Guided activities to practice using AI reflectively

Session 4: Why AI is weird

• Understanding a little about large-language models will help you teach with them
• This is not the AI we were promised: Less rational and logical, more conceptual and humanistic
• Non-technical overview of what LLMs do and how they're trained
• Key idea: AIs are great at working with concepts and theories, but bad at specific facts
• Tokenization and the context window
• How to message AI limitations to your students

Afternoon

Session 5: More model assignments

• Using AI for simulation and facilitation
• Building conversation partners
• Using AI for review and coaching
• Pros and cons of AI-supported assessment: when is automated grading appropriate?

Session 6: Workshopping your own assignments

Session 7: Guided group brainstorming and visioning

• Reflect as a group on what we've learned and new ideas for AI in the curriculum
• Structured think-pair-share activity
• Participants answer individual questions, then answer discussion questions in pairs
• End with whole-group debrief and mind-mapping activity
• Leave with ideas for the next steps on your campus