AI to Learn 2.0: a New Governance Framework for Opaque AI in Learning Domains

A new paper on arXiv proposes a governance framework for opaque AI systems in learning-intensive domains. The framework, called AI to Learn 2.0, aims to address the problem of proxy failure, where AI-generated outputs are judged on superficial characteristics rather than their actual quality. This can lead to biased or misleading results. The authors argue that current governance frameworks are inadequate for evaluating AI-assisted learning, as they rely on simplistic metrics that can be easily gamed by AI systems. AI to Learn 2.0 provides a more comprehensive approach, with a maturity rubric that assesses the quality of AI-assisted learning outcomes. The framework is designed to be flexible and adaptable to different learning contexts, making it a valuable tool for educators, researchers, and policymakers. By providing a more nuanced evaluation framework, AI to Learn 2.0 can help ensure that AI-assisted learning is used responsibly and effectively.