Making AI a More Effective Teacher: Lessons from TPACK

Human Teachers and AI Teachers

Would you be surprised if you pulled a random person off the street, shoved them into a classroom full of students, and then found that they weren’t a particularly effective teacher? Of course not. And why wouldn’t that be surprising? Because effective teaching requires a great deal of knowledge and skill, and the person you pulled off the street most likely had no relevant training.

Why, then, do we constantly act surprised when we select a random generative AI model, try to use it to support student learning, and find that it isn’t particularly effective? Like the random person pulled off the street, most generative AI models are neither pre-trained nor fine-tuned with education in mind. And even if AI eventually achieves “human-level intelligence,” it will be like we said above – humans aren’t particularly effective teachers without some specific training. 

The question becomes, then, if you were going to provide additional skills and knowledge to a generative AI model to help it be a more effective teacher, which specific skills and knowledge would you provide? Here’s my answer: the same skills and knowledge we help humans who want to become teachers develop during their training and ongoing professional development. Since everything needs a name, I’ll call this the “TRaining AI to be a Teacher” (TRAIT) hypothesis. 

The TRAIT hypothesis is something like this: the effectiveness with which a generative AI model supports student learning will be proportional to the extent to which it has the skills and knowledge of an appropriately trained human teacher.

We can and should study the most effective methods of providing models with these skills and knowledge. For example, should the skills and knowledge be “taught” in such a way that they “enter the model’s long-term memory” (i.e., by fine-tuning the model), or should they be provided in a way that looks more like performance support (retrieval augmented generation, context augmentation, etc.)? I have opinions on this question and will address them in another essay. But today I want to focus on which specific skills and knowledge I believe generative AI models need in order to be effective teachers.

The TPACK Framework

There are a range of organizational structures you could impose on this thought exercise. I’m going to use Mishra & Koehler’s Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge as my organizing framework here. (I readily acknowledge that I’m using the framework in a different way than the authors intended here, but I’m finding that it suits my purpose quite well.) 

You’ve likely seen this image before:

 

In a moment I want to focus on what TPACK implies for developing generative AI that’s capable of teaching effectively. But first, let’s briefly summarize TPACK.

There are three primary kinds of knowledge represented in the diagram (quotes below are from Mishra & Koehler, 2006): 

• Content Knowledge, or “knowledge about the actual subject matter that is to be learned or taught. The content to be covered in high school social studies or algebra is very different from the content to be covered in a graduate course on computer science or art history. Clearly, teachers must know and understand the subjects that they teach, including knowledge of central facts, concepts, theories, and procedures within a given field; knowledge of explanatory frameworks that organize and connect ideas; and knowledge of the rules of evidence and proof.”
• Pedagogical Knowledge, which is “deep knowledge about the processes and practices or methods of teaching and learning and how it encompasses, among other things, overall educational purposes, values, and aims. This is a generic form of knowledge that is involved in all issues of student learning, classroom management, lesson plan development and implementation, and student evaluation”
• Technological Knowledge is “knowledge about standard technologies, such as books, chalk and blackboard, and more advanced technologies, such as the Internet and digital video. This involves the skills required to operate particular technologies. In the case of digital technologies, this includes knowledge of operating systems and computer hardware, and the ability to use standard sets of software tools.”

Then, there are three points at which two of these overlap in the diagram:

• Pedagogical Content Knowledge, which “includes knowing what teaching approaches fit the content, and likewise, knowing how elements of the content can be arranged for better teaching. This knowledge is different from the knowledge of a disciplinary expert and also from the general pedagogical knowledge shared by teachers across disciplines. PCK is concerned with the representation and formulation of concepts, pedagogical techniques, knowledge of what makes concepts difficult or easy to learn, knowledge of students’ prior knowledge, and theories of epistemology.”
• Technological Pedagogical Knowledge, which “is knowledge about the manner in which technology and content are reciprocally related. Although technology constrains the kinds of representations possible, newer technologies often afford newer and more varied representations and greater flexibility in navigating across these representations. Teachers need to know not just the subject matter they teach but also the manner in which the subject matter can be changed by the application of technology.”
• Technological Content Knowledge, which is “knowledge of the existence, components, and capabilities of various technologies as they are used in teaching and learning settings, and conversely, knowing how teaching might change as the result of using particular technologies. This might include an understanding that a range of tools exists for a particular task, the ability to choose a tool based on its fitness, strategies for using the tool’s affordances, and knowledge of pedagogical strategies and the ability to apply those strategies for use of technologies.”

And then there is the central overlapping area in the diagram:

• Technological Pedagogical Content Knowledge is “an emergent form of knowledge that goes beyond all three components… is the basis of good teaching with technology and requires an understanding of the representation of concepts using technologies; pedagogical techniques that use technologies in constructive ways to teach content; knowledge of what makes concepts difficult or easy to learn and how technology can help redress some of the problems that students face; knowledge of students’ prior knowledge and theories of epistemology; and knowledge of how technologies can be used to build on existing knowledge and to develop new epistemologies or strengthen old ones.”

Implications of TPACK for Generative AI 

Fully elaborating on the implications of TPACK for effective instruction by generative AI would require much more time that I can allocate to this essay, and I’m trying (and failing) to keep it brief. So I will highlight just a few points, using my work on Open Educational Language Models as a concrete example.

When creating Open Educational Language Models, the designer explicitly represents Content Knowledge and Pedagogical Knowledge independently: 

• In an OELM, you might visualize Content Knowledge as a detailed summary of a chapter from an open textbook. Content Knowledge helps the model give accurate explanations and answers, and significantly decreases inaccurate responses. As I mentioned above, this can be accomplished in a number of ways, including fine-tuning, RAG, or context augmentation.
• In an OELM, Pedagogical Knowledge is represented in prompts that are enacted by the model. These prompts represent pedagogical practices that truly cross disciplinary boundaries, like engaging in retrieval practice, or connecting new information to existing knowledge.

Pedagogical Content Knowledge is also part of the design:

• In an OELM, Pedagogical Content Knowledge is represented in prompts that are enacted by the model in specific disciplinary contexts, like instruction, practice, and feedback that are specific to writing a strong topic sentence, or factoring a polynomial.

The prototype OELM authoring tool, which will be published on GitHub later this week (I’ll make an announcement), helps authors capture relevant Content Knowledge and Pedagogical Knowledge so they can be remixed and enacted by the model, creating interactive learning activities for students (I previously shared screenshots of the prototype student tool):

 

As I’ve been envisioning them to date, an OELM is comprised of many of these declarations of Content Knowledge and Pedagogical Knowledge (or to use other language, many OER for context augmentation and prompts describing evidence-based teaching and learning practices) combined with open model weights and open source software that orchestrates these all into coherent teaching and learning interactions.

But perhaps the thing that has delighted me the most about applying the TPACK framework to my work on OELMs is that it has helped me see an entire area of opportunity I had missed previously! I have less to say here because I am still working through these implications, but here’s the beginning of my thinking: 

When creating Open Educational Language Models, Technological Knowledge is expressed in information about what external tools are available to the model to use. For example, a scientific calculator tool might be available to the model via the Model Context Protocol (MCP), or real-time data about the weather or the stock market or the learner’s own performance might be available to the model via an API.

Information about when and how the model would use a specific tool as part of a specific teaching strategy to teach a specific concept would be that center-of-the-diagram sweet spot of Technological Pedagogical Content Knowledge.

Let’s Hear Some Other Ideas!

If the TRAIT hypothesis is true, and generative AI needs the same kind of training humans do in order to become an effective teacher, then there’s a lot of potentially fruitful ground to plow by drawing out the implications for generative AI of different frameworks for representing teacher knowledge and approaches to teacher training and professional development. (For example, what do “professional development” models about ways to overcome problems with models due to their “knowledge cutoff” dates?) What’s your favorite framework or approach, and what does it imply for teaching with generative AI?