Getting Started
Start small, start now.
It seems like a daunting task: learn AI, figure out what to teach, figure out how to teach, overhaul your syllabus, heck, develop an entirely new pedagogical approach!
Integrating AI literacy in one's curriculum can become a struggle to survive in the quicksand. At the same time, the cost of inaction in today's rapidly changing technology environment is too great. So this is what I suggest: find one small thing you can do toward AI literacy this semester.
The trick is to start small but keep the option for future expansion open. To do this you will need a conceptual roadmap to guide you through your own AI literacy development and gradual curricular integration. I developed the "AI To Lean" framework with this kind of curricular building process in mind. You can start with one activity right now. It won't change student practices overnight, but you can at least expect a meaningful discussion about using AI productively and responsibly in the academic context.
For example, in some recent workshops, I shared a simple class activity called "What is a large language model?". It starts by getting students in the classroom to use the same prompt to elicit AI response, then discussing what we found. It will take 20-30 minutes of class time, and yet, I found it to be a far more effective way of teaching the very basic mechanism of how AI tools generate its responses, and how human judgment plays a critical role in that process. It can open up a whole new perspective on generative AI and what it takes to engage it productively and responsibly, and unlike more passive forms of information transmission, like reading an article or watching a video about the current transformer architecture, students remember what they got out of the hands-on exercise.
Managing Information Overload
It is important for all of us to stay updated on how AI technology is evolving. But the amount of information about AI development and its future implications are voluminous and complex, and expert opinions are diverse and often conflicting. "Start small, start now" applies to this aspect of teaching and learning with AI. Here are the things I keep in mind so that I don't get overwhelmed and keep my head (sort of) above water:
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Forget the idea that I'm going to ever "keep up." There is just so much stuff out there and there's no way anyone can know about everything out there. Besides, AI technology keeps evolving rapidly, and a fact or a tip that was valid last week might become totally obsolite overnight. No one can truly "keep up" with all that.
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Prioritize, prioritize, prioritize. Choose one or two very specific aspects of the AI universe and ignore everything else. My two priorities are AI literacy pedagogy and image/video generation. I try to keep an eye on any new information that are relevant to these priority areas and relentlessly skip, delete, and ignore everything else that pops up on Google search or in my email inboxes. Trust me, you are still going to have a plenty to read and watch, and because everything is interconnected, you'll still be learning about other AI-related topics. But at least, you know why you are reading them - because they are somehow relevant to what you are trying to do in your priority areas.
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Find a few go-to sources where you are most likely to to find news and information that are most helpful to you. Be totally selfish and utilitarian: you keep whatever work best for you or seems interesting to you and set aside the rest. It will take time to find the right ones for you, but it's necessary investment to keep your sanity in a long run. My current go-to's are the Discord servers of Google and OpenAI for latest tech development in image/video gen, a few subscribed channels on Substack and YouTube that focus on AI literacy and technology breakdown.
Quick Start Resources
"AI To Learn" Curriculum
AI To Learn Framework Quick Guide conceptualizes the pathway of learning from the development of the AI literacy disposition to context-specific competencies. It can serve as the roadmap for the AI literacy curricular integration.
Intro to AI Literacy Faculty Workshop Slide Deck (PDF) - intended for faculty and teachers who are considering AI literacy integration, this slide deck outlines the AI To Learn framework, a guided inquiry exercise, and how to start implementing AI literacy curriculum.
Cultivating Judgement Faculty Workshop Slide Deck (PDF) - intended for faculty and teachers who are considering AI literacy integration, this slide deck focuses on the concept of "workflow" to consider how to integrate AI assistance responsibly and productively in an assignment process.
Books by AI To Learn Institute Affiliated Authors
Attention First: A Human Framework for Learning, Creation, and Development in the Age of AI : This handbook outlines the pedagogical philosophy and practices of the "attention-driven design," developed by a computer scientist and instructional design expert. My AI literacy curriculum is inspired by this attention-driven approach.
AI To Learn: Evolution of an AI Literacy Curriculum:This book chronicles my journey of building an AI literacy curriculum from the ground up and the iterative process of improvement from the first year of implementation.
External Resources
Recommended Sources: If you are just getting started and don't know where to even start, click the button to the right to take a look at my list of references and sources. Some are "outdated" in strictly technical terms, but still great at breaking down the technology under the hood for a basic understanding of AI technology.
Elon University "Human Wisdom" Project: Elon University has published a complete AI literacy tool kit with an underlying human-centered AI collaboration model. You can download their student guide and the slide deck for10-session AI literacy curriculum for free under creative commons license.