The Evolutionary Trap of AI in Writing Instruction

Have you ever wondered why nature permits such staggering inefficiency in the biological world? A genome is rarely tidy; it is cluttered with repetition, dormant viruses, and errors. Yet, that chaos is the very thing that allows a species to survive a sudden shift in the environment. Efficiency is brittle. Redundancy is resilient.

We are currently witnessing a mass extinction of friction in the classroom. A new Critical Interpretive Synthesis, analysing 47 peer-reviewed studies from 2015 to 2025, examines the impact of Large Language Models (LLMs) on second-language learners. The findings are unsettling. The researchers identify a phenomenon they call AI Dependency Syndrome (ADS). This is not merely about cheating; it is about the atrophy of thought.

The Hidden Cost of AI in Writing Instruction

The study highlights a series of functional trade-offs. On the surface, the output looks pristine. Grammatical errors vanish. Fluency spikes. But below the waterline, the learner’s metacognitive judgment—the internal voice that asks, 'Is this actually what I mean?'—begins to dissolve. The synthesis suggests that when students outsource the struggle of articulation, they do not just lose the ability to write; they lose the confidence to think without a safety net.

Consider the evolutionary principle of 'use it or lose it'. In biology, maintaining an organ costs energy. If a population of fish ends up in a dark cave, they do not keep their eyes. Evolution ruthlessly edits out the expensive tissue that serves no immediate purpose. The brain operates on a similar budget. If the cognitive load of structuring an argument is consistently offloaded to an algorithm, the neural pathways responsible for that architecture may weaken.

The authors note three mechanisms driving this dependency: a loss of confidence in unaided production, a bias towards 'algorithmic approval', and the internalisation of AI norms. Learners stop trusting their own messy, human syntax in favour of the machine's sterile average. They trade anxiety for comfort. But anxiety is often the signal that learning is happening.

This is not a call to ban the technology. Rather, the paper argues for 'intentional pedagogical design'. We must recognise that while AI in writing instruction can smooth out the rough edges of a draft, those rough edges are often where the actual thinking lives. If we polish them away too early, we may be left with something shiny, perfect, and entirely hollow.