AI to Learn 2.0: A Deliverable-Oriented Governance Framework and Maturity Rubric for Opaque AI in Learning-Intensive Domains

Computer Science > Artificial Intelligence [Submitted on 16 Mar 2026] AI to Learn 2.0: A Deliverable-Oriented Governance Framework and Maturity Rubric for Opaque AI in Learning-Intensive Domains Seine A. Shintani 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. AI to Learn 2.0 allows opaque AI during exploration, drafting, hypothesis generation, and workflow design, but requires that the released deliverable be usable, auditable, transferable, and justifiable without the original large language model or cloud API. In learning-intensive contexts, it additionally requires context-appropriate human-attributable evidence of explanation or transfer. 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, shows how the framework separates polished substitution workflows from bounded, auditable, and handoff-ready AI-assisted workflows. AI to Learn 2.0 is proposed as a governance instrument for structured third-party review where capability preservation, accountability, and validity boundaries matter. Comments: 10 pages, 2figures Subjects: Artificial Intelligence (cs.AI); Computers and Society (cs.CY) ACM classes: I.2.7; K.3.1; K.4.1 Cite as: arXiv:2604.19751 [cs.AI]   (or arXiv:2604.19751v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.19751 Focus to learn more Submission history From: Seine A. Shintani [view email] [v1] Mon, 16 Mar 2026 11:44:43 UTC (177 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CY References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)