Can AI Really Accelerate Learning? The Hidden Cost of Skipping the Slow Growth Phase

In recent teaching experiences, many students use AI to complete assignments, whether summarizing papers or building models, producing seemingly reasonable answers in minutes instead of the expected one to two hours.

However, output without mental effort lacks meaning; AI‑driven work does not foster growth.

This discussion focuses on learning: "unearned" results cannot be evaluated for process and fail to promote development.

While I understand students' pursuit of efficiency (I share it), the apparent high efficiency raises the question of its hidden costs.

It reminds me of the exponential function. An important property of an exponential function (base > 1) is that growth starts slowly, then enters an acceleration phase where the speed increases dramatically. True rapid progress requires a gradual accumulation process. The initial growth may seem negligible, but over time it leads to a remarkable leap.

Learning follows a similar exponential pattern. Early on, students may feel progress is slow, especially with complex concepts, investing much time and effort for limited gains. Yet, as knowledge and skills accumulate, growth accelerates, eventually producing a qualitative leap. If students skip this accumulation—using AI to obtain answers quickly—they miss the crucial slow‑growth stage and cannot achieve later breakthroughs.

AI‑generated answers may appear correct, but students miss the full process from cognition to understanding to application. This early stage, though slow, is critical because real growth depends not just on the final result but on the accumulation process. The thinking, exploration, mistakes, and reflection during this phase drive long‑term development.

Another insight from exponential functions is that early investment is indispensable for later rapid growth. In other words, the "slow" phase in learning is meaningful; it lays the foundation for future "fast" progress. Patiently investing time to grasp knowledge leads to an eventual exponential improvement, echoing the principle of compound interest.

The principle of compound growth shows that early accumulation and continual effort yield unexpected returns. For learning, this return manifests not only in grades but also in enhanced thinking abilities and deeper problem understanding—capabilities gained through sustained accumulation.

Thus, while AI can help complete assignments swiftly, it cannot replace the essential accumulation phase. The "speed" AI offers is superficial; without deep thought and understanding, the core goal of learning—internalizing knowledge and improving application skills—remains unattainable, just as exponential functions demonstrate that true rapid progress emerges only from slow, steady buildup.