Well, the game isn’t big enough unless it scares you a little.

Cmdr. William Riker

I’m not the biggest fan of Star Trek: The Next Generation, but Commander Riker’s words from “Pen Pals,” Season 2, Episode 15, hit the heart of what it feels like to learn something new.

It’s scary.

Any time I take on something new and feel over my head, I’m learning. I am adding experience points: new knowledge, skills, and thinking. Recently, AI has been my game of choice. The tools are constantly changing, and it’s not clear how they will impact us all.

What game are you playing?

It can feel like the rapid evolution of artificial intelligence (AI) calls for a complete reworking of what and how college instructors teach, a feeling compounded by pressures to “meet the moment—and quickly!” But this sense of urgency should not short-circuit best practices for education. This plenary session draws on research across the curriculum to showcase frameworks and strategies where instructors drive the design of classroom, program, and institutional (re)visions in this time of technological transition. The presenters, co-facilitators of Miami University’s “AI in the Majors” initiative, advocate for building from instructors’ collective wisdom, professional expertise, and field practices to craft the futures we want for our students and ourselves as we all adapt to teaching and learning with and about AI.

I’ve been locked in some heavy coding with Claude Code in VS Code and Google’s new Antigravity. The tools have come a long way in 3 months. (Antigravity keeps running out of its content window, argh! Wrecked my codebase and I had to backtrack!)

Takeaway: Building is so much faster. I have learned so many techniques I did not know were possible. I’m building robust web apps I could not do solo (Damnit, Jim, I’m a designer and researcher, not an expert developer! ). When I build by hand, I am so much faster because I take the time to learn from the process/what I make with the tools.

But AI Literacy involves…

• Knowing how to write a sequence of complex prompts that segment the activity into manageable bits.
• Parsing the task into stages that all point to an eventual goal. Ya gotta have in mind what you are trying to accomplish
• Building off of surprises: when an unexpected feature emerges when using AI, evaluate it and then build on it… sometimes changing direction
• Engaged critical thinking: my brain is always on, thinking systemically and systematically… evaluating results and adjusting with the whole picture in mind

Experience is invaluable. I can cut off so many dead ends when using AI because I have caused them in my code in the past, and I know when a turn will lead to a cul-de-sac. The more experience you have, the better the result (this is an issue for early career folx, the make-it-take-it syndrome. ) Then again, folks who are self-initiated and try stuff, break it, make it, read it, and explore will get experience. I think we need to mentor students to choose active exploration and experimentation more, instead of passive consumption. This has been a theme for me for decades.

GenAi is getting better at generating new and useful feature directions rather than wild-goose-chasing all over the place. Then again, maybe I am more AI Literate. A merge perhaps? Am I getting better? Is it getting better? I am going further and can do more. I like that. And the work is stronger. I am more curious and inspired than I used to be. Hope springs eternal.

Thinking is actually more important and active than when reading because the content evolves and morphs. The content I read—as long as I use the content like putty in my hands that can be manipulated and reconfigured—is not an inanimate authority, but a resource that changes me and results in new (creation, experience, knowing, awareness).

Hm. I imagine previous scholars, writers, and creators would rather their work be alive—moving people, changing societies—than a static testament to their prowess. (okay, some folx probably do want their work to just be self-referential). Note: I am ignoring concepts of the self, ego, legacy, ownership, etc. here. These are real and factor into why people create and how they think creations should be used and credited. Folx need to make a living from their creations because in our current society, basic needs are not met if you do not have money or cannot trade your goods/services.

Oh, and the user’s agency is oh-so-important here. Intent (See: Theory of Planned Behavior, Ajzen) determines if AI use is “fix it for me” or “let’s explore ways to apply content for discovery.” Brains that are still developing and folx who are in a hurry to get to the next thing are not gonna spend time to discover. They’ll use AI to crap something out because they value something other than the thing they are doing.

What if students valued what we invite them to do? To learn?

Media Pairing: I wrote this while listening to “Glittering Grandeur” by Paul Mottram. It may have been an influence: https://soundcloud.com/paulmottram/glittering-grandeur

• Identify ways four capabilities—Ideation, Research, Creation, and Testing—can amplify research in aging
• Apply Gen AI Ideation, Research, Creation, and Testing capabilities to a real research project in aging
• Discuss intended and unintended outcomes of GenAI use for projects in aging research

This first-year course, which enrolls 40-55 students, combines hands-on activities to develop Generative AI skills, design thinking methodology, futures studies, “wicked problems,” and an analysis of pop culture’s examination of human-machine interactions to help learners develop technical competency and critical thinking skills. This session will highlight specific teaching strategies, explain how specifications grading is used for competency development, share student outcomes, and provide practical insights for educators interested in integrating AI education with liberal arts approaches.

Outcomes

After attending this session, participants will be able to: 1) Identify strategies for integrating liberal arts approaches with AI skill development to help students become doers and leaders in the workplace. 2) Apply insights from a successful first-year AI course to develop learning experiences that combine technical skills with ethical consideration and creative problem-solving. 3) Explain how specifications grading, which emphasizes iterative improvement through multiple revision cycles, supports student development of AI technical skills and critical thinking.

Evidence

The session will present products from the Spring and Fall 2024 course offerings. These include: 1) Themes and responses from an assignment called “Let’s Talk About,” where students posted questions about AI for class discussion. 686 “Let’s Talk About” questions were posted over two semesters, and I will share a few main themes that reveal concerns about workforce preparedness, AI ethics, and ways course content sparked inquiry. 2) Examples of student work and specially designed course materials to demonstrate ways learners engaged with and applied course content.

This poster presentation shares examples of process-centric learning outcomes that dilute generative AI’s use and emphasize a learner’s ability to apply it. I will demonstrate how to convert existing outcomes that prioritize products into ones that give feedback on the student’s applied skills and thinking. I will also briefly introduce specifications grading, an alternative grading model I have used for three semesters that emphasizes iteration and improvement by responding to feedback. Viewers will be invited to discuss and develop strategies for facilitating human development and growth when generative AI is a standard tool in our fields.

During this presentation, I’ll share themes from these responses, including concerns about academic integrity, an interest in AI sentience, money-making with AI, career concerns, ethical guardrails, and AI’s impact on entertainment. Participants will learn how students were just as concerned about AI as their instructors and how these students are also already dreaming of ways Generative AI can help us address wicked problems that affect our local and global societies. During our time together, participants will have an opportunity to ask questions, and we’ll take a few moments to follow our students’ lead by generating our own “Let’s Talk About” topics. 

Measure success not by what you get but by what you give.