In an era dominated by artificial intelligence and personalised algorithms, it’s important to remember that teachers have been perfecting the art of adaptive learning long before technology took the spotlight.

Long before Netflix could predict your next binge or ChatGPT could tailor responses to your needs, TEFL (Teaching English as a Foreign Language) teachers were implementing sophisticated systems of differentiation that responded to individual learner signals in real-time.

The Teacher Algorithm: Reading the Room

When I walk into my TEFL classroom, I’m immediately processing countless data points—who looks confused, who’s ahead of the curve, and who’s disengaged. This ongoing assessment mirrors how modern algorithms track user engagement and adjust content delivery accordingly. Consider the “traffic light” system that many teachers use, where students signal their understanding with red, yellow, or green indicators. This simple feedback mechanism allows teachers to “adjust on the fly, ensuring no one feels left behind or bored.” Sound familiar? It’s essentially the same concept behind adaptive learning software—just without the flashy interface.

Differentiated Tasks: The Original A/B Testing

Tech companies often optimise user experiences through A/B testing—showing different versions of content to different users to see what works best. Teachers have been doing something similar for years with differentiated tasks. Take, for example, a graded dictation exercise: one text is transformed into three different tasks—error correction for slower learners, gap fills for the middle group, and note-taking for advanced students. This isn’t just good teaching; it’s content optimisation at its finest, the kind that would impress a Silicon Valley product manager.

The Human Touch in the Feedback Loop

AI systems improve through feedback loops, adjusting based on user interactions. But teachers pioneered this approach with “interactive feedback loops,” where students thrive when feedback is part of the learning process, not just the end. After activities, skilled teachers ask students to reflect on what worked and what didn’t, then use those responses to adjust their approach for the next task. This dynamic, responsive learning environment ensures that students feel heard and supported—something even the most advanced AI systems still struggle to replicate authentically.

The I-WE-YOU Framework: Scaffolding Before It Was Cool

In the tech world, there’s much discussion about how AI should gradually introduce complexity to users. Teachers have long employed the “I-WE-YOU” framework, which starts with teacher demonstration, moves to guided practice, and culminates in independent work. This structured approach—gradually transferring responsibility—helps students master new skills, mirroring how the best AI systems are designed to provide decreasing levels of support as users grow more proficient.

What AI Can Learn from Teachers

As we rush to embrace AI in education, perhaps it’s worth pausing to acknowledge that many of the so-called “revolutionary” adaptive learning principles have been practised by teachers for generations. The best AI systems are trying to replicate what good teachers have always done:

• Recognise individual differences
• Provide tailored support
• Adjust in real-time based on feedback
• Build confidence through appropriate challenges
• Create personalised learning experiences

The key difference? Teachers do all of this with empathy, intuition, and a deep understanding of human learning that goes beyond mere data points.

The Future: Algorithms and Teachers Together

Rather than viewing AI as a replacement for differentiated teaching, we should see it as a tool to amplify what teachers already do well. AI can handle the routine aspects of differentiation—such as generating varied practice materials or tracking progress—while teachers focus on the human elements that algorithms can’t replicate: building relationships, inspiring curiosity, and nurturing confidence. Differentiation is really about “choice and personalisation”—something teachers have been delivering long before it became a tech industry buzzword.

So, the next time you hear about a revolutionary new adaptive learning algorithm, remember: teachers have been running sophisticated adaptive learning systems in their classrooms all along. They just didn’t need to call it artificial intelligence—because there’s nothing artificial about the intelligence of a teacher who knows their students well.