Concern 563 · Working draft · editorial review in progress
Skill atrophy in writing
Writing-skill atrophy is the possibility that repeatedly outsourcing drafting and revision leaves a person with less practice expressing ideas independently.
Understanding the concern
Writing is more than producing clean sentences. It involves choosing what matters, organizing an argument and noticing where a reader may be confused. If an assistant does all those steps, a finished document can hide how little the writer practiced them.
The evidence does not support saying that AI inevitably weakens writing. Controlled studies have also found improvements on later unaided writing tasks when people learned from AI examples. The difference may lie in how the tool is used. Asking why a revision works, comparing alternatives and writing independently at intervals can preserve learning alongside assistance.
An example
A learner submits polished paragraphs but cannot explain the argument or revise it without the tool.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
Evidence and its limits
The preprint provides counterevidence to inevitable deskilling in specific writing tasks. Long-term effects across all forms of writing remain unsettled.
Lira and colleagues · preprint revised February 2026 · Research preprint
Coach not crutch: Evidence that AI can improve writing skill despite reducing effort ↗ (opens in a new tab)Controlled writing studies found learning gains in unaided tests; evidence against inevitable deskilling, not proof for every writing task.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
How could an AI writing session leave someone better able to write on their own afterward?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Dignity
Dignity means recognizing each person's worth regardless of usefulness, status, or a system's score. People deserve respect and a voice in matters that affect them.
Explain important decisions, avoid humiliating treatment, and keep room for a person's story. Do not treat a prediction as a complete account of who someone is.
Ask together: Does this use treat each person as someone whose experience matters?
Listening
Listening means trying to understand someone's experience before preparing an answer. People who use a service, work with it, or are judged by it may notice problems its designers miss.
Invite people to describe what happened in their own words. Repeat back what you understood and let them correct it. A quiet person or a complaint that is hard to measure may still reveal something important.
Ask together: Whose experience have we not heard yet?
Seeing potential
Seeing potential means recognizing people's capacity to learn, contribute, and grow. It resists reducing someone to a score or to the tasks a machine can perform faster.
Use tools to support people's development. Give learners time, feedback, and real opportunities to practice. A prediction about someone's performance should not close off their future.
Ask together: How could this help people develop abilities they value?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
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