Beyond the Hype: Taking a 50 Year Lens to the Impact of AI on Learning
How do we make sure LLMs are not "digital duct tape"?
In this enlightening conversation, I had the privilege of speaking to Professor Chris Dede who I met during my time at Harvard. Prof. Chris is a luminary in the field of learning technologies and I credit him for getting me to understand the centrality of learner motivation in education.
With a career spanning over 50 years in learning technology from the introduction of the Apple 1 to the latest developments in AI, Dede brings a critical perspective on the hype-cycles that often surround emerging technologies and their implications for education.
His insights are particularly timely and relevant given the increasing interest in how artificial intelligence can transform learning, teaching, and assessment - or fail to do so.
Dede shares his thoughts on a range of topics, from the centrality of a process-oriented model of education to his concerns around LLMs creating a “digital duct-tape” that actually prevents paradigm shifts in learning.
(The interview has been condensed and edited for brevity)
What are you spending your time thinking about most these days?
I've been contemplating the evolution of educational technology, particularly the persistent hype-cycles surrounding every new and shiny technology.
Despite witnessing the advent of various learning technologies since the era of the Apple 1, the cycle of excitement and disillusionment remains consistent. This pattern has not only persisted but has also obscured genuine expertise in the space.
Nobody goes to a “quack doctor” to understand the latest developments in medicine, but we continue to see it in education and learning. Ironically, the instant experts that are getting all the attention now were instant experts in previous cycles like the Metaverse.
I spend a lot of my time now dispelling these myths and highlighting the value of true expertise in the field.
In a recent co-authored paper, Navigating a World of Generative AIs: Suggestions for Educators, you talk about taking a process-oriented model of education and learning (as opposed to a product-led one). Can you talk more about that?
We often think of the product of teaching as the outcome (e.g. an essay, a drawing, etc.). The essence of education, in my view, lies not in the products or outcomes of learning but in the journey itself. The artifact is just a symbol that you’ve taken the journey.
The process of learning — the exploration, challenges, and personal growth that occur along the way — is where the real value lies. For instance, the act of writing an essay is valuable not merely for the final product but for the intellectual journey it represents. It forces you to improve and organize your thinking on a subject.
This distinction becomes important with the rise of generative AI, because it uniquely allows us to produce these artifacts without taking the journey.
The danger lies in people believing that, since we no longer need to take that journey to produce the artifact, the skills become less important. Instead, it should push us to think more critically and deeply about the skills needed for the future and how we will assess them.
The rise of AI, should push us to think about IA - intelligence augmentation. My advocacy for IA stems from a belief in the symbiotic relationship between human practical-wisdom and artificial intelligence, emphasizing the unique strengths of each.
Specifically, AI is becoming increasingly proficient at what I call “reckoning” skills like calculation, computation, and prediction. As such, we will see increased demand for human “judgment skills” such as decision-making under conditions of uncertainty and deliberation.
The rise of AI in education has led to a greater emphasis on learner motivation. Do you agree with this sentiment?
The paramount importance of learner motivation becomes increasingly evident in the context of AI's rise in education. This was starkly highlighted by the experiences with Massive Open Online Courses (MOOCs). While capable of disseminating content broadly, they fell short in fostering engagement, leading to high dropout rates due to a lack of motivation.
The pandemic further underscored this, as educators who utilized engaging teaching methods succeeded in remote teaching environments, affirming that, beyond cognitive pedagogy, learner motivation is crucial.
The contrast between human and AI tutors illuminates this point further. Almost 50% of the feedback given by human tutors is “affective feedback” that fosters motivation. Even if you programmed an AI Tutor to randomly say similar things, it’s not clear the effect would be as motivating coming from a machine rather than a person.
The challenge in getting more people to take learner motivation seriously is rooted in educational expectations—many expect education to mirror their own past, often suboptimal experiences and advocating for traditional methods like lecturing.
In many ways, it seems that the biggest bottlenecks in improving learning, once we’ve figured out access, lie in motivation and assessment. Do you agree with that? What’s missing from that paradigm?
I agree that motivation and assessment represent significant challenges in education. Our current assessment methods are adept at evaluating the types of tasks AI excels at, which does not bode well for fostering unique human capabilities.
We need to redefine assessment to value creativity, critical thinking, and other distinctly human skills. Tools like AI can aid in this endeavor, but they must be designed to complement, not replace, human judgment and creativity.
As I’ve argued previously, I am worried that all this hype around LLMs renders them a “type of digital duct-tape to hold together an obsolete industrial-era educational system”.
Instead of creating true paradigm shifts in learning, we end up automating outdated instruction and assessment. Rather than focusing on using LLMs to do traditional things better, we need to transform education to do better things.
Finally, I want to emphasize that whatever skills we learn will have a very short half-life. People will have multiple careers. We need to think about how to prepare people for that reality.
We are not in the education business, we are in the lifelong learning business.
I hope you enjoyed the last edition of Nafez’s Notes.
I’m constantly refining my personal thesis on innovation in learning and education. Please do reach out if you have any thoughts on learning - especially as it relates to my favorite problems.
If you are building a startup in the learning space and taking a pedagogy-first approach - I’d love to hear from you.
Finally, if you are new here you might also enjoy some of my most popular pieces:
The Gameboy instead of the Metaverse of Education - An attempt to emphasize the importance of modifying the learning process itself as opposed to the technology we are using.
Using First Principles to Push Past the Hype in Edtech - A call to ground all attempts at innovating in edtech in first principles and move beyond the hype
We knew it was broken. Now we might just have to fix it - An optimistic view on how generative AI will transform education by creating “lower floors and higher ceilings”.
Couldn't agree more ;)