arXiv:2605.20355v1 Announce Type: cross Abstract: 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 estimated to be most learnable. We first show that PSN outperforms existing shared autonomy baselines in balancing student improvement in unassisted re
The increasing integration of AI into shared-control systems across various sectors necessitates solutions to mitigate skill atrophy, which this research addresses directly.
This research provides a framework for designing AI assistance that fosters human skill development rather than degrading it, crucial for safety and long-term human-AI collaboration.
AI assistance can now be designed with an explicit objective to 'nudge' human users towards states that promote learning and skill retention, fundamentally altering the human-AI interaction paradigm.
- · AI developers
- · Semi-autonomous system operators
- · Education and training sectors
- · Industries relying on human-AI collaboration
- · Developers of AI systems that create skill atrophy
- · Organizations with poor human-AI interface design
Improved human proficiency in tasks involving AI assistance, leading to safer and more efficient operations.
New standards and regulations emerging for 'skill-preserving' AI design, impacting widespread AI adoption.
A shift in workforce development strategies to incorporate AI as a training tool rather than solely a task completer, ultimately reshaping the future of work and education.
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