In the 1980s classic The Karate Kid, Miyagi-sensei teaches Daniel-san martial arts through seemingly mundane tasks: waxing cars, painting fences, and sanding floors. To Daniel, these chores appear unrelated to his goal of becoming a karate champion. But as the story unfolds, he discovers that these repetitive actions have been building muscle memory and discipline—essential skills for mastering karate.

Fast forward to today, and a similar debate is unfolding in education. Generative AI (GenAI) tools promise faster, more efficient learning. They can generate essays, solve math problems, and even code complex programs in seconds. But are we skipping the foundational “wax on, wax off” moments that prepare students to truly excel?

In this blog post, we’ll explore how the lessons of Miyagi-sensei align with three key pedagogical frameworks—Constructivist Learning Theory, Zone of Proximal Development (ZPD), and Mastery Learning—and how they can guide our approach to teaching and learning in the AI era.

The Struggle Is the Learning

Constructivist Learning Theory emphasizes that knowledge is not passively received but actively built. It’s the process of doing, reflecting, and problem-solving that leads to deep understanding. In the video, we see Daniel-san frustrated by the repetition of tasks he doesn’t understand. He wants to learn karate, not chores! But Miyagi-sensei’s teaching is rooted in constructivist principles. Painting fences and waxing cars are not random tasks; they are carefully designed experiences that connect physical movements to martial arts techniques. This mirrors what students face today. With AI tools like ChatGPT or Google Bard, students can bypass the struggle of crafting a thesis statement or debugging code, arriving at the solution without the process. While this efficiency is tempting, it risks robbing students of the deeper learning that comes from trial and error.

As educators, we can draw on constructivist principles to design lessons that engage students in meaningful tasks. For example:

Writing: Instead of starting with an AI-generated essay, ask students to brainstorm and outline their own ideas. Once they’ve built a foundation, AI can be introduced as a tool for refinement.
Coding: Before using AI to write scripts, students should practice writing basic code themselves. This helps them understand the structure and logic, enabling them to critically assess AI-generated solutions.

In short, the process is as important as the product. Struggling to build understanding is where true learning happens.

Scaffolding the Journey

Lev Vygotsky’s Zone of Proximal Development (ZPD) provides another lens to examine the Karate Kid analogy. ZPD is the “sweet spot” where learners can achieve more with the help of a mentor or peer than they could on their own. Miyagi-sensei is a master of scaffolding. He doesn’t overwhelm Daniel-san with complex karate moves. Instead, he breaks down the skills into manageable pieces—sand the floor, paint the fence, wax the car—and gradually builds Daniel’s confidence and ability. In the age of GenAI, scaffolding becomes even more critical. AI can act as a mentor, guiding students through tasks they might struggle with alone. However, the human teacher’s role in structuring these interactions can be hard to replace.

Here’s how educators can scaffold effectively in an AI-enhanced classroom:

Define the Basics: Start by identifying foundational skills students must master. For example, before using AI for research, students should learn how to identify credible sources manually.
Guide with AI: Introduce AI as a supportive tool. In writing, this could mean asking AI for grammar suggestions after students have drafted an essay themselves.
Reflect and Iterate: Encourage students to reflect on their learning. What did they struggle with? How did AI help? Reflection deepens understanding and builds metacognitive skills.

Scaffolding ensures that students progress through their ZPD, gradually moving from dependent to independent learners. It’s the balance between guidance and autonomy that makes the learning experience transformative.

Mastery Over Shortcuts

The principle of Mastery Learning is simple but powerful: students must achieve a deep understanding of one concept before moving to the next. This approach is particularly relevant in today’s AI-driven world, where the temptation to skip foundational steps is greater than ever.

In The Karate Kid, Daniel-san’s mastery journey is slow and methodical. By perfecting the basics—through endless repetition—he builds a strong foundation for more advanced techniques. This echoes the core idea of mastery learning: proficiency in the fundamentals is the key to long-term success.

Generative AI tools, while powerful, often short-circuit this process. For example:

A student might use AI to generate a beautifully written essay without understanding how to structure an argument.
A novice programmer could rely on AI to create functional code without learning the logic behind it.

To counter this, educators can structure lessons to prioritize mastery:

Frequent Feedback: Break learning into smaller chunks, providing regular feedback to ensure students fully grasp each step.
Progressive Challenges: Gradually increase the complexity of tasks. For instance, in a coding class, students might start by writing simple loops before tackling more complex algorithms.
AI as a Reinforcer: Use AI to reinforce learning rather than replace it. For example, after students solve a math problem manually, AI can provide additional practice problems or explain alternative solutions.

Mastery learning requires patience and persistence, but it equips students with the confidence and competence to tackle future challenges.

Finding Balance in the AI Era

The lessons of Miyagi-sensei remind us that the journey matters as much as the destination. While AI offers incredible opportunities to enhance education, it’s essential to strike a balance between efficiency and effort. Here are some guiding principles for integrating AI into teaching:

Process Over Product: Prioritize the learning journey. AI can support the process but shouldn’t replace it.
Explain the “Why”: Help students understand the purpose behind their efforts, as Miyagi-sensei did with Daniel-san.
Encourage Reflection: Build moments into lessons where students can reflect on what they’ve learned and how they’ve grown.
Collaborative Learning: Foster a classroom culture where students learn from each other and from AI, embodying the philosophy of “together learning.”

Automation in Education and the Budding Botanist Paradox

The themes explored in The Karate Kid—the importance of process, foundational skills, and perseverance—can also help us frame modern conversations about automation in education. As we integrate tools like virtual and augmented reality (AR/VR) into learning environments, we encounter a paradox: while these technologies can enhance efficiency and engagement, they also risk diminishing the critical processes of inquiry and discovery that are central to meaningful education. This paradox of automation, as explored in my research and projects, reflects the challenges of relying too heavily on automated systems. The budding botanist paradox, for example, illustrates this tension vividly. Imagine a student encountering a plant in the wild. Traditionally, identifying its species would involve observing its environment, petals, colors, and other characteristics—an exercise in inquiry that strengthens observational skills and critical thinking. With AR tools, however, a simple snapshot can instantly identify the plant and display its details, bypassing the inquiry process entirely.

While these tools are powerful, they can lead to over-reliance, mirroring issues seen in other fields like aviation. The tragic Air France Flight 447 incident, caused in part by over-reliance on autopilot, underscores the importance of maintaining foundational skills even as we adopt advanced systems. Similarly, in education, if students lose the ability to inquire, question, and problem-solve because they rely too much on automation, they risk becoming passive recipients of information rather than active learners.

Hawkinson, E. (2022). The budding botanist paradox: Automating human inquiry with immersive technology. Proceedings of the 30th International Conference on Computers in Education. Asia-Pacific Society for Computers in Education. Retrieved from https://library.apsce.net/index.php/ICCE/article/download/4550/4425/5513

This is where immersive technology like AR/VR has both potential and pitfalls. My projects—such as Reality Labo and My Hometown Project—embrace these tools to enhance learning. AR games for tourism training and VR simulations for cultural exchange offer engaging, real-world applications that improve skills. However, I approach these projects with caution, ensuring they supplement rather than replace the inquiry process. For example, in a virtual tourism scenario, students might use AR to explore landmarks, but the experience is designed to encourage reflection and deeper questioning about history, culture, or geography. The solution to the budding botanist paradox lies in bolstering digital literacy. Students need to understand the systems curating their information—how algorithms work, the biases they may carry, and how to question automated outputs. This aligns with my efforts to integrate digital citizenship into the curriculum, ensuring learners not only use but critically evaluate emerging technologies.

As we move toward a future of increasing automation, let’s remember: it’s not just about finding the answer but also about cultivating the skills to ask the right questions. The budding botanist, like Daniel-san, needs to learn the process before relying on the tool.

Wax On, AI Forward

The debate about AI in education isn’t about whether to use it—it’s about how. By drawing on frameworks like Constructivist Learning Theory, ZPD, and Mastery Learning, educators can harness AI’s potential while preserving the integrity of the learning process. As Miyagi-sensei famously said, “First learn stand, then learn fly. Nature’s rule, not mine.” The same holds true in the classroom. Before students use AI to write essays, code programs, or solve problems, they must build the foundational skills that enable them to use AI effectively and critically. AI is a tool—a powerful one—but it’s not a replacement for the hard work of learning. By embracing both the struggle and the technology, we can prepare students not just to succeed, but to thrive in an AI-driven world.

So let’s pick up our brushes, wax on, and move AI forward—together.

About the Author

Eric Hawkinson
Learning Futurist

erichawkinson.com

Eric Hawkinson is a Learning Futurist at Kyoto University of Foreign Studies, where he focuses on the integration of technology into education. Specializing in the creation of immersive learning environments, Eric employs augmented and virtual reality to enhance learning outcomes. He is an advocate for digital literacy and privacy, promoting open access to information and ethical technology practices. Outside his academic role, Eric is engaged in public outreach and professional development. He has established immersive learning labs, designed online courses, and advised on technology strategies across various sectors. His professional designations include Adobe Education Leader, Google for Education Certified Innovator, and Microsoft Innovative Expert. Eric’s notable projects, such as AR experiences for TEDxKyoto and WebVR for Model United Nations, reflect his commitment to using advanced technologies for global education and collaboration. Eric is dedicated to exploring the challenges and opportunities presented by emerging technologies, contributing significantly to the evolution of educational practices.

Roles
Professor – Kyoto University of Foreign Studies
Research Coordinator – MAVR Research Group
Founder – Together Learning
Developer – Reality Labo
Community Leader – Team Teachers
Co-Chair – World Immersive Learning Labs