Reinforcement Learning Controls GenAI Access Timing in Education
A new study from arXiv (2605.15850) proposes using reinforcement learning to scaffold when students can access generative AI in educational settings. The RL agent, grounded in metacognitive theory, cognitive load theory, and productive failure, decides optimal access timing to prevent over-reliance and disengagement. A mixed-methods lab study with 105 participants compared the agent's strategy to unrestricted and fully restricted GenAI use, measuring learning gains and metacognitive engagement.
Key facts
- arXiv paper 2605.15850 proposes RL-based access timing for GenAI in education.
- The approach treats access timing as implicit scaffolding.
- Reward function is grounded in metacognitive theory, cognitive load theory, and productive failure.
- Study involved 105 participants in a mixed-methods controlled lab experiment.
- Compared RL agent strategy to unrestricted and fully restricted GenAI use.
- Focus on preventing over-reliance, metacognitive disengagement, and diminished learning.
- Research addresses the understudied question of when to allow GenAI access.
- Results show strategically timed access improves learning outcomes.
Entities
Institutions
- arXiv