AI’s Power to Pace Learning

Recently, AI in education has been framed as a gas pedal, accelerating learning, compressing knowledge timelines, and delivering instant expertise. The dominant narrative suggests that the primary advantage of Large Language Models (LLMs) is their ability to speed up the process of education, providing quick answers and enabling learners to consume vast amounts of information in record time.

But this view is incomplete. What if the real strength of LLMs isn’t just speed, but their ability to facilitate slow, deep, and enriched learning? What if, instead of merely accelerating knowledge acquisition, LLMs could be designed to optimize cognitive depth, not just cognitive velocity?

The Rate of Learning Over Time

Learning has a velocity—a rate of change over time. Borrowing from mathematics, we can describe this as dE/dt, where E represents knowledge acquisition. But AI isn’t just a gas pedal, it has the power to modulate learning speed dynamically—accelerating when needed and slowing down for deeper reflection. The key isn’t just speed, but adaptive control—the ability to adjust velocity for optimal engagement and retention.

Traditionally, slow learning has been equated with conventional educational methods—deep reading, Socratic dialogue, and iterative refinement of understanding over time. Conversely, fast learning is often associated with modern AI-powered tools, which promise rapid knowledge transfer at unprecedented scales. However, this binary division is misleading.

LLMs do not exist at a single speed. In fact, they operate across an entire spectrum of educational velocities, dynamically adjusting to the learner’s needs. They can provide instant overviews of a topic or engage in highly expanded articulations, revealing nuance, depth, and alternative perspectives. In this way, LLMs can actually facilitate slow learning through their rapid capabilities, creating a paradox where the fastest tool available is also the best at enabling depth and reflection.

Modulated Learning Power Matters More Than Ever

In a world obsessed with instant results, the ability to sit with complexity is becoming rare. Yet, some of the most profound intellectual growth happens in modulation—through deliberate exploration, extended dialogue, and playful iteration.

• Surface-Level Understanding: Speed alone can lead to shallow learning, where information is absorbed but not deeply understood or retained.
• Loss of Intellectual Patience: The habit of rapid answer-seeking may erode cognitive endurance, making learners less willing to grapple with challenging concepts.
• Diminished Creativity and Playfulness: Modulated learning fosters serendipity, curiosity, and creative synthesis—elements that pure efficiency-driven models may overlook.
• The Attention Economy's Trap: In a world where TikTok clips and bite-sized content dominate, there’s a risk that education becomes a passive act of skimming rather than a deep, engaged process. LLMs can either amplify this problem or help counteract it.

LLMs as Cognitive Pacing Machines

Rather than just serving as accelerators, LLMs should be seen as cognitive pacing machines, capable of dynamically tuning the speed of learning.

• Slow Learning for Hands-On Skills: Cooking is best learned gradually, through hands-on experience. An LLM could assist by breaking down complex recipes into step-by-step explanations, adjusting its guidance based on the learner’s experience level.
• Balanced Learning for Structured Subjects: Algebra benefits from a structured approach, where concepts build progressively. An LLM can reinforce learning by guiding problem-solving at an adaptive pace, ensuring mastery of foundational concepts before advancing.
• Fast Learning for Memorization and Recall: Driving directions require rapid, precise learning—too much delay, and the information loses relevance. An LLM can efficiently summarize key routes or landmarks, providing quick, digestible instructions without unnecessary complexity. This adaptability creates what I call the Goldilocks Zone of Learning—where cognitive load isn’t too high or too low, but just right for optimal engagement and retention.

The Power to Adjust Learning Speed

What if we redefined education not as a race to the finish line, but as a calibrated process? The true power of AI in learning is not just increasing the speed of learning, but controlling it dynamically based on context—knowing when to accelerate and when to slow down. Designing AI for Depth, Not Just Speed

• Intentional Calibration: Could future LLMs include a "reflect mode," where instead of just answering, the AI challenges users with counterpoints and deeper questions?
• Guided Intellectual Meandering: AI could be programmed to introduce productive friction, prompting learners to grapple with concepts rather than simply absorbing them.
• Balancing Velocity and Depth: The best learning happens at varying speeds—sometimes fast, sometimes slow. AI should empower learners to control their own learning pace, rather than just pushing acceleration.
• Adding a sense of joy and wonder: Education must not be relegated to a burden. From the joy of discussing a great book to the messiness of cooking, learning with an LLM can be deeply satisfying.

The True Power of AI in Learning

The future of AI-driven education isn’t just about making learning faster. It’s about making learning better—richer, more engaging, and more intellectually fulfilling. LLMs, when used effectively, can help restore the balance between speed and depth, enabling a new kind of cognitive fluency that adapts to the complexities of human learning.

As we rethink the role of AI in education, the real question is not how fast we can learn, but rather, how well we can learn. The future of learning isn’t just about speed—it’s about precision, depth, and human connection.