Using AI to Bridge the Gap from Theory to Practice

It’s easy to get dismissive about educational theory. I’ve heard teachers say “Don’t bother with the theory. Just focus on practical strategies.” But when done well, theory can provide a roadmap and framework we can use. It can be a nuanced dialogue that cuts to the core of why we do what we do. The problem is that teachers are busy and the research is often dense. However, with generative AI, it is easier than ever to access theory and research in a way that is practical and relevant.

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Discovering the Joy of Theory

Years ago, when I was doing my dissertation, I fell in love with theory. I know that sounds odd. I had always seen theory as important but also . . . boring and a little stale. I knew that educational theory mattered but I treated it as a foundational element that we then build the fun stuff on top of. It was like the educational equivalent of rebar, concrete, and steel. Important but not fun.

However, when I began working on my dissertation, I discovered the sheer joy of taking a deep dive into theory and research. Although I knew so much of the research on project-based learning, I had never thought too deeply about motivation and self-efficacy and how it connected to PBL. If anything, I had a simplified idea that self-efficacy was the belief that one can accomplish a task while motivation was the desire to accomplish a task.

While those definitions weren’t entirely wrong, I had never made the distinction between attribution theory (how one defines the success of a task) and self-efficacy. I hadn’t really considered the shadow side of self-efficacy and the way it can make someone complacent. I hadn’t thought much about how efficacy and connected to ideas like Grit and Growth Mindset or even Flow Theory.

In other words, I had an oversimplified view of human motivation. As a classroom teacher, I always made the distinction between intrinsic and extrinsic motivation. I had a cursory understanding of Self-Determination Theory and the idea of autonomy, mastery, and purpose (due, in part to Daniel Pink’s book Drive). However, I had a false binary between extrinsic motivation (punishment and rewards) and intrinsic motivation (being internally driven to do a task). The reality is that these exist on a continuum.

The image above is a Copyright Image from the Center for Self-Determination Theory. It is covered as Fair Use for the purposes of education and to explain a concept. Here’s a tweet where they explicitly give permission use it and to cite it.

Often, I had thought about intrinsic motivation as being driven by a sense of purpose or goal-attainment. However, intrinsic motivation is more a matter of enjoyment, interest, and satisfaction. What I had been thinking of intrinsic motivation was actually identified regulation. It is more autonomous than external regulation but it is still on the extrinsic motivation spectrum.

So why does this matter? It’s just a matter of theory, right?

Actually, diving into the complexity of self-efficacy within Social Cognitive Theory and motivation through Self-Determination was a powerful process for me. It was humbling to learn just how little I knew about motivation, which, in turn, sparked curiosity. That curiosity led to a more nuanced perspective on motivation. On a more practical level, understanding that motivation is task-specific has helped me to see that I often shift between different aspects of the Self-Determination Continuum on a single creative project.

Consider the continuum of autonomy and the dissertation process itself. When I met a friend for a pint and we geeked out on these ideas, it’s a matter of pure enjoyment. This was intrinsic motivation.

When I am made connections between the dissertation topic and my belief that classrooms should be “bastions of creativity and wonder,” I was motivated by identified regulation (identification). When I grew insecure and felt like I needed to with impress my dissertation chair, I fell into introjected regulation (introjection). When I was reformatting it to be APA compliant and I am using external rewards to get through a phase (like my promise to go get a massage once I submitted the final draft), I am motivated by external regulation.

Instead of seeing these motivational domains as good or bad, I now view them as more or less autonomous and I am thinking strategically about how and when to use each approach within a specific project.  I get it. It’s “just theory,” but knowing this theory at a deeper level has improved my ability to do creative work and to motivate my own students.

Seven Reasons Theory Is Important

When I first started teaching, I was dismissive of the role of educational theory. It felt cold and calculated and overly scientific. For me, teaching was an art that I could move through intuitively. However, I read a piece by Neil Postman about the role of metaphors in language. I began to read up on Conceptual Metaphor Theory and realized that the language I used in talking about education implied that teaching was a messy art while theory was a cold, distant science. By studying a linguistic theory, I had a new framework for making sense out of everyday language. Now, when someone says “escalate” or “shot down” or “get defensive” in a conversation, I recognize the implied metaphor of a discussion as a battle — and I am then able to reshape my language to de-escalate it.

I began to realize that theory isn’t simply something you study abstractly. It is something you use. Theory provides us with a foundation for practical knowledge and a lens for making sense out of our world. With that in mind, here are seven reasons teachers should know educational theory.

1. Theory provides a foundation for practical information.

Theory is all around us if we’re paying attention. Theories are answer the bigger “why” questions which then drives the “how.” Our theories helps us make sense out of how things work. Theory provides us with evidence. Consider this, if you work in design, chances are you use UX Design Theory to create a better user experience:

If you are an author, you probably follow certain theories for story-craft without even realizing it. If you’re an engineer, your work is rooted in specific scientific theories that are tested and re-tested. In other words, theory is a part of both the art and the science of the work we do. Theory is the foundation for our craft.

On a practical level, theory provides us with frameworks and models that we then use to create something new. For example, Schlechty’s Levels of Engagement provides a protocol for making sense out of the engagement levels of our students:

Theory provides us with frameworks and models. This is why I love having students make sense out of theoretical or conceptual frameworks. Often, they deconstruct the visualization of a theory. For example, we analyze the pros and cons of viewing human needs on a pyramid (Maslow’s Hierarchy of Needs) and talk through when and how a framework might be used.

2. Theory provides nuance.

I’ve met people who can’t stand the idea of Growth Mindset:

For what’s worth, I was skeptical of this theory because I felt like it ignored systemic injustice and perpetuated the myth that a can-do attitude is all it takes to be successful. However, once I began reading the actual journal articles, I found that Dweck is often nuanced in her findings. She openly shares the limitations and talks about the need for systemic support. The real issue has been the oversimplification of theory. While you don’t have to geek out on the uni-dimensional versus multidimensional interaction of self-efficacy, understanding Growth Mindset at a deeper level sheds some nuance on when and how it works.

Often, we slip into false binaries in education. Homework is bad or it’s good. Learning should be based on direct instruction or inquiry. Motivation should be intrinsic or extrinsic. However, when we understand educational theory, we move past the false binaries and see that there are often multiple layers. It’s no accident that many theorists place their ideas on a continuum. We also get the chance to see the complex interplay between various theories and the disagreements researchers have about how to conceptualize things.

Note that nuance is not the same as compromise or “finding the middle.” It’s about understanding complexity and being okay with paradox. It’s about asking, “When is this true?” as often as we ask, “Is this true?” Ultimately, this process humbles us.

3. Theory humbles us.

The more you immerse yourself in theory, the more painfully aware you are of how much you don’t understand. You begin to realize that people devote their entire lives to researching certain truths and sharing that with the world.

 

When we come to terms with what we don’t know, we avoid making bold, ignorant pronouncements about education. Often, we become more open to new ideas. In turn, we are more curious about how learning works.

4. Theory reminds us of that ideas happen in community.

We tend to ascribe theories to single Great Thinkers. For example, within self-efficacy, we think of Bandura. However, I love Dr. Edward Clapp’s idea of the biography of an idea. I had the chance to see him give a TEDx Talk years ago when I gave my own TEDx Talk:

I love this idea of the story of an idea, shaped by culture, and forged in community. Here, you get a richer concept of the conversations between various researchers as they tried to make sense out of the world. When this happens, we avoid the temptation to elevate specific thinkers into the status of gurus. We can see each member of the community as deeply human and even flawed.

In fact, there is real value in seeing the biases within the community. For example, I love Flow Theory, but the examples he used in his early work were pretty sexist (men doing art versus a woman ironing shirts). Similarly, there’s a fascinating paper on gender bias and how we describe creativity within Self-Determination Theory. When we understand theory at a deeper level, we can use a critical lens to ask how these theories have been shaped by sexist or racist ideologies.

5. Theory keeps us from mistaking novelty for innovation.

When you take a deep dive into any theory, you realize that great ideas endure over time. In Vintage Innovation, I made the distinction between relevance as “flashy and new” versus “different and better.”

Understanding educational theory helps us avoid the trap of following the latest fads. We are able to see that certain great ideas are older. But it also keeps us from growing curmudgeonly and saying, “This is just the latest fad.” Consider design thinking. While it might be a bit more trendy in education right now, the theoretical framework has a rich history going back 60+ years. Understanding that history helps us see how theories evolve over time with new discoveries. We’re also able to see how theories adapt to new contexts.

6. Theory gives us a critical lens.

There are a lot of bad ideas in education that somehow seem to persist. For example, there are no right brain or left brain people. We don’t have learning styles. While this might not seem like a big deal, I have seen language arts teachers who have said, “I’m not going to make them read the book. They’ll watch the movie because many of my students are visual learners.” Actually, we all benefit from reading and from using visuals. When we understand theory, we are able to see through some of these myths.

Theory also provides us with a critical lens for understanding new research. For example, my knowledge of behavioral economics helps me see motivation and self-efficacy in a new light.

7. Theory gives us evidence for the work we do.

Ultimately, when we know educational theory, we are able to have evidence to back up what we do. But we also know the limitations of the evidence. We know, for example, that there are tons of complex variables at work in teaching and that we can’t simply codify a list of “best practices.” Even Hattie has moved away from listing the effect size of strategies and switched, instead, to talking about the ideal context for specific strategies:

The more we understand theory, the more we are able to see that there are huge elements of teaching that can’t be addressed by theory. Teaching is deeply relational and deeply contextual, which means all theories will be tempered by our collective classroom experiences.

From Theory to Practice

There’s often an anti-theory mentality in K-12 education. I see this when a pre-service teacher works with a cooperating teacher who says, “College classes give you a bunch of theory but this is where you’ll actually learn how to teach.” There are other variations of this, including, “That might work in theory but a real classroom is different.” There are understandably some reasons for this. Theories are often presented in a systematic, abstract way. Here are some better approaches:

• Make the connection between theory and practice more explicit. When we explore a theory, it can help to explore it through a case study or problem. At times, we might even want to use strategic confusion to wrestle with the ideas before seeing a particular theory. We should also build in discussion time to talk about how a particular theory might play out in one’s context.
• Treat theory as something you use as well as something you understand. When I share the research on UX Design Theory with my students, they also use a self-reflection rubric based on design principles that they then use to critique the design of their classroom space, materials, and systems. Here, they get the chance to see that theory isn’t merely an abstract idea. It’s something you use on a regular basis.
• Provide visual models for theories. We often make sense out of concepts by visualizing how things work. Most theorists have created visual models for their theories. It helps to talk about the pros, cons, and implementation of a particular model.
• Critique educational theory. Don’t shy away from the inherent conflicts and power dynamics within educational theories. I once had a conversation with someone about Universal Design for Learning. One thing he said really stuck out to me. “Yes, we want to aim for universal design. But sometimes a disability calls for a specific modification that cannot and should not be available to everyone. We should be asking ourselves when something should be universal and when it should be particular.” As a special education teacher with Multiple Sclerosis, he was living the nuances of Universal Design on a daily basis.
• Talk about the limits of specific theories. If we talk about Grit or Growth Mindset, let’s be open about the limits of these theories. Let’s talk about what a theory fails to address and why that might be problematic.
• Avoid the temptation to create false binaries. As I mentioned before, motivation isn’t as simple as extrinsic or intrinsic. It exists in a continuum. Understanding that continuum leads to a richer, more dynamic, view of human motivation.
• Provide better access to educational theory. This one is the hardest option. There is often a chasm between educational theory and practice. This is partly due to the cost prohibitive nature of paying for journal articles. We need to reform the way we provide access to information. This is also due, in part, to the nature of academic writing. It can be overly complex and even boring — and I say that as someone who loves research. But this is also where we can communicate theory in a way that is also deeply practical and accessible. This is why I love Cult of Pedagogy and the way Jennifer Gonzalez makes theory accessible. It’s why I love Wikipedia. But it’s also why I love how we can use generative AI to develop a deeper conceptual understanding of theory.

Using AI to Understand Educational Theory and Research

We often hear the terms “research-based” or “data-driven” to describe all kinds of different educational practices. A quick perusal of X will tell you that direct instruction is better than discovery-based, play-based, project-based, or inquiry-based learning because “that’s what the research says.” A stream of messages offer simplistic descriptions of the “science of reading” that often fail to recognize the research about prior knowledge, vocabulary, motivation, and other key factors.

With so many pundits using “the research says” to justify different practices, it can be tough to figure out what the research actually proves and how that connects to educational theory. This is why I love the Consensus. A quick disclaimer. Consensus is a podcast sponsor. However, please note that while they are a sponsor, they have no influence over the content or editorial decisions of this post. All opinions and discussions are solely guided by our own insights and analyses.

If you’re not familiar, Consensus is an app built on an LLM (Large Language Model) that functions as a chatbot. You can ask a question and you get a set of nuanced answers with direct links to journal articles. You can ask follow-up questions. You can have it simplify the descriptions. You can ask for specific strategies that connect to the research it has summarized.

In other words, it becomes a conversation where you can chase your curiosity and develop a deeper, more nuanced, view of research. Unlike most chatbots, Consensus is less vulnerable to hallucinations (where it makes things up). And, while you can never completely avoid bias, I’ve been impressed by way it handles nuanced information.

Consensus describes it this way:

Consensus is powered by AI, but grounded in science. Consensus only utilizes the 200 Million scientific research papers in its database to create answers. And, it clearly shows you which papers contribute to an answer.

So, imagine you are new to project-based learning and you hear that it’s ineffective as a teaching strategy. It doesn’t lead to improvements in academic achievement. It causes cognitive overload. However, when you go into the Consensus app, you’ll discover it’s more nuanced. There are many studies demonstrating the positive role in specific subject areas. You might read some of the articles and ask some follow-up questions.

Or you might ask, “Are there times when PBL doesn’t work well? Any types of learning that it’s not good for?” Here, you’ll see some of the challenges related to students with little prior knowledge. You’ll see the need for addressing extraneous cognitive load. You then use the app to get an explanation of how cognitive load works and what it looks like when there is too much extraneous cognitive load.

This might seem a little too theoretical but it’s drilling down into the “why” before getting into the “how.” You can then ask for specific strategies for reducing cognitive load it PBL (such as dual coding, using templates, using protocols, creating a roadmap or schema, etc.).

As a professor, I use Consensus during my initial exploration phase of research before doing my own literature review. It functions like a scaffold for me. I also use it to create short summaries of research that my students can access related to each class’s topic. I use to develop a deeper understanding of some of the areas where I am weaker (for example traditional behavior management versus Restorative Justice practices). I’ve used it to explain bigger educational theories and I love the ability to ask follow up questions.

This is just the beginning. Generative AI is great for deep analytical work and I see some real potential for using these tools to develop a deeper understanding of both research and theory in a way that’s nuanced and dynamic.