The paper proposes AI to Learn 2.0, a deliverable-oriented governance framework for AI-assisted work in learning-intensive settings. The framework addresses the problem of proxy failure, where a polished artifact can be useful while no longer serving as credible evidence of the human understanding, judgment, or transfer ability that the work is supposed to cultivate or certify. The framework reorganizes adjacent ideas around the final deliverable package, distinguishing artifact residual from capability residual, and operationalizes the result through a five-part package, a seven-dimension maturity rubric, and gate thresholds on critical dimensions. The paper provides worked scoring across contrastive cases, including coursework substitution, a symbolic-regression governance contrast, teacher-audited national-exam practice forms, and a self-hosted lecture-to-quiz pipeline with deterministic quality control, showing how the framework separates polished substitution workflows from bounded, auditable, and handoff-ready AI-assisted workflows. The framework is proposed as a governance instrument for structured third-party review where capability preservation, accountability, and validity boundaries matter.
AI to Learn 2.0: A Governance Framework for Opaque AI in Learning-Intensive Domains
A new governance framework for AI-assisted work in learning-intensive settings addresses the problem of proxy failure and provides a deliverable-oriented approach to evaluating AI-assisted outputs.
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