Next Steps for The Beyond ChatGPT Series

Photo by Brett Jordan on Unsplash

My sincere thanks to those who helped support the creation of The Beyond ChatGPT series by subscribing to my newsletter. Your continued support has helped me carve out the time to research AI’s impact on skill development this summer. I’m committed to keeping the content of this series open to all.  Moving forward, I’ll be revisiting each one of these past essays to explore ways educators can ethically use AI with students to help them learn or ways they can include intentional friction in the learning process to counter AI’s marketing promise of a frictionless learning experience.

I’ll be taking a break for the next week, but then I plan to return to the series and more posts about how we can build AI literacy. Beth McMurtrie’s series on the challenges of getting students to read offers one of the most comprehensive and engaged looks at how students’ relationships with this vital skill have changed. I’d like to provide educators with something similar for each one of the essays below—a deep dive followed by strategies educators can use to work with AI or against AI in their teaching.

The Beyond ChatGPT Series

• Reading: No One is Talking About AI’s Impact on Reading

• Note Taking: AI’s Promise to Pay Attention for You

• Feedback: What Does Automating Feedback Mean for Learning?

• Tutoring: Why Are We In a Rush to Replace Teachers with AI?

• Research: What’s At Stake When We Automate Research Skills

• AI Detection: The Price of Automating Ethics

• Instructional Design: AI Instructional Design Must Be More Than a Time Saver

Building a Critical Framework for Intentional Friction

To this end, I’ll revisit each essay and build a critical framework applying what it means to use intentional friction in learning. I’ve discussed why we need to preserve friction as a crucial part of the learning process in We Need to Reclaim Slowness. Emily Pitts Donahoe, Katie Conrade, and Jane Rosenzweig have all taken deep dives into why friction matters in the learning process. Each of their posts is much more in-depth than my own and worth reading. Katie's Friction v. Magic is a wonderful deep dive into demystifying the marketing AI companies use to try and sell AI as learning shortcuts. Jane's op-ed ChatGPT is at odds with what education is for and articulates why friction is at the heart of learning. And Emily’s nuanced take in The Frictions of Ungrading unpacks all the weighted complexities friction brings to grading and ungrading practices.

Applying Resources You Can Use

To support this framework, I’ll be highlighting the terrific work people and organizations have been doing to build activities that engage AI critically, like Harvard’s AI Pedagogy Project, TextGenEd, and the MLA/CCCC Joint Taskforce on AI and Writing’s Assignment Library. I’ll be juxtaposing these resources with those that talk about strategies that lean into our human skills, like those hosted on UVA’s Teaching Resources Hub and OneHE’s open activities. The goal is to connect the resources to the use cases to show how working with AI or against AI is possible.

What Uncritical Adoption of AI Means

Throughout the Beyond ChatGPT series, a theme I’ve hit on over and over is how quickly generative AI is moving outside of skills like writing, but it is also how hollow the marketing promise was that AI will be adopted as a tool to augment your skills instead of replace them. In December of 2022, I wrote a guest essay for John Warner’s blog series at Inside Higher Education arguing just that: AI Will Augment, not Replace. Less than two years later, I cannot say I agree with this view any longer. Not in how the tools are marketed. And certainly not in how the technology is being developed and deployed as a continued experiment.

Generative AI in education has a “could be” problem that isn’t going to be solved by technology. The AI hype claims that the software could revolutionize education, providing each student with immediate feedback, empathetic and engaging tutoring, and truly personalized learning. This techno-utopian future doesn’t take into account the massive privacy challenges AI poses for students and educators, the completely unknown downstream impacts on relationships synthetic tutors or mentors will play, or whether algorithmic interventions will help or hinder learning.

The Beyond ChatGPT series isn’t designed to uncover answers for all the issues education faces related to AI, but it is my most sincere hope that it can help some educators navigate this emerging technology and establish meaningful boundaries that preserve what matters to them in the profession of teaching. We cannot lose hope and fall into despair and anguish about what AI is doing to our profession or our students.

Sal Khan’s Brave New Worlds: How AI Will Revolutionize Education (and Why That's a Good Thing) issues a call for educators to be brave and embrace this new technology. Khan uses the majority of the book to discuss how these tools will help rather than harm students. Maybe he’s right, but I’m increasingly convinced the path that takes courage is not one of hurried adoption, but one marked by cautious skepticism. As we continue to explore the intersection of AI and education, let's remember that true courage in this context may not lie in rushing to adopt every new technology, but in thoughtfully questioning, discussing, and sometimes resisting changes that threaten to undermine the fundamental human connections and struggles that make learning meaningful. The new worlds will come. However, we should not be in a rush to burn down the old world in favor of the new. Promises are often fleeting. If we invest too much energy focusing on an ever shifting horizon, debating what could be, we risk losing touch with what matters now.