Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education

Research is exploring the optimal timing for generative AI (GenAI) access in educational settings, recognizing its pervasive use among university students despite risks of over-reliance and diminished learning. A novel approach operationalizes 'access timing' as a form of implicit scaffolding, utilizing a reinforcement learning (RL) agent. This agent determines when students should access GenAI, with its decision-making informed by metacognitive theory, cognitive load theory, and productive failure principles.

Why it matters

This research provides a framework for integrating GenAI into educational ecosystems in a manner that maximizes learning benefits while mitigating risks. Understanding and implementing optimal access timing can enhance educational outcomes, ensuring that technology serves as a valuable tool rather than a hinderance to cognitive development.

Key insights

• GenAI is widely used by university students, yet poses risks such as over-reliance, metacognitive disengagement, and reduced learning.
• Prior research has primarily focused on pedagogical scaffolding for GenAI usage, rather than the timing of its access.
• The study proposes treating GenAI access timing as a form of implicit scaffolding.
• A reinforcement learning agent is being developed to determine optimal GenAI access points for students.
• The RL agent's reward function is based on metacognitive theory, cognitive load theory, and productive failure.
• The question of 'when' to allow off-the-shelf GenAI remains understudied and lacks empirical investigation.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2605.15850