AI to Learn 2.0: A Deliverable-Oriented Governance Framework and Maturity Rubric for Opaque AI in Learning-Intensive Domains
Summary: arXiv:2604.19751v1 Announce Type: new
Abstract: Generative AI is entering research, education, and professional work faster than current governance frameworks can specify how AI-assisted outputs should be judged in learning-intensive settings. The central problem is proxy failure: 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.
This paper proposes AI to Learn 2.0, a deliverable-oriented governance framework for AI-assisted work. Rather than claiming element-wise novelty, it reorganizes adjacent ideas around the final deliverable package, distinguishes artifact residual from capability residual, and operationalizes the result through a five-part package, a seven-dimension maturity rubric, gate thresholds on critical dimensions, and a companion capability-evidence ladder.
Key Features of AI to Learn 2.0
AI to Learn 2.0 offers a structured framework that allows for the integration of opaque AI in various phases of the learning process. Here are the key features:
- Flexibility in AI Utilization: The framework permits opaque AI during exploration, drafting, hypothesis generation, and workflow design.
- Deliverable Usability: The final deliverable must be usable, auditable, transferable, and justifiable without reliance on the original large language model or cloud API.
- Human-Attributable Evidence: In learning-intensive contexts, it requires context-appropriate human-attributable evidence of explanation or transfer.
Operationalization of the Framework
The framework operationalizes its principles through several components:
- Five-Part Package: Organizes the deliverable into distinct, assessable components.
- Seven-Dimension Maturity Rubric: Evaluates the maturity level of the AI-assisted outputs across various dimensions.
- Gate Thresholds: Establishes critical thresholds on dimensions to ensure quality and accountability.
- Capability-Evidence Ladder: Aids in assessing the evidence of capability preservation.
Practical Applications and Case Studies
The effectiveness of AI to Learn 2.0 has been demonstrated through various practical applications. Key case studies include:
- Coursework Substitution: Evaluating how AI can assist in substituting traditional coursework while maintaining educational integrity.
- Symbolic-Regression Governance Contrast: Comparing traditional and AI-assisted methods in symbolic regression tasks.
- Teacher-Audited National Exam Practice Forms: Assessing the effectiveness of AI-generated exam practice materials under teacher supervision.
- Self-Hosted Lecture-to-Quiz Pipeline: Implementing a deterministic quality control system for converting lectures into quizzes.
Conclusion
AI to Learn 2.0 is proposed as a governance instrument for structured third-party review where capability preservation, accountability, and validity boundaries matter. This framework addresses the pressing need for effective governance in the rapidly evolving landscape of AI-assisted learning, ensuring that outputs are reliable and reflective of genuine human capabilities.