This repository contains research and documentation on the transformation of human capital and educational systems in the era of artificial intelligence. The work presents a comprehensive theoretical framework for understanding how AI accelerates different modes of learning and value creation.
The core contribution is a taxonomy of five fundamental Learning Economies that represent distinct modes of value creation through knowledge acquisition and application:
- Breadth - diversification across multiple domains
- Depth - acceleration of mastery within fields
- Subset - vocational mobility through rapid retraining
- Superset - creative destruction through field-level reinvention
- Network-form - value creation through multi-scale knowledge routing
Central to this framework is Resonance - the universal transfer mechanism enabling cross-domain knowledge flow and synthesis across all five economies.
See our presentation on this at GuildCon 2025, at Hackers Guild PGH.
Main theoretical framework document
This comprehensive paper presents the foundational theory of Learning Economies, examining how AI transforms human capital and educational systems. Key contributions include:
- Taxonomy of Five Learning Economies: Detailed analysis of Breadth, Depth, Subset, Superset, and Network-form economies
- Resonance as Transfer Mechanism: How cross-domain knowledge flow enables learning mobility
- AI as Learning Accelerator: Analysis of how AI amplifies existing economic principles rather than creating new ones
- Economic Implications: Labor market transformations, new forms of knowledge capital, and systemic effects
- Practical Applications: Implications for learners, educators, and policymakers
The document includes extensive historical examples, metrics for each economy, and a unified production function for learning value.
Implementation framework aligned with NICE cybersecurity workforce standards
This document translates the Learning Economies theory into testable hypotheses using the NIST 800-181 (NICE) framework. It provides:
- 14 Testable Hypotheses: Concrete, measurable interventions for each Learning Economy and meta-factor
- NICE Framework Alignment: Maps to cybersecurity workforce roles and competencies
- Quantitative Measures: Specific metrics, data sources, and success criteria
- Evaluation Protocols: RCT designs, statistical methods, and ethical considerations
Each hypothesis includes Work Role/Category, testable statements, population/intervention/comparator groups, and measurable outcomes suitable for pilot studies or A/B tests.
Service architecture overview
A high-level description of an online service that would coordinate agentic support for teachers and students using the Learning Economies framework. The service would:
- Help students identify their alignment with different Learning Economies
- Support teachers in developing personalized education plans
- Connect students to relevant testable hypotheses
- Facilitate Learning Economies agent conversations
Note: This document is marked as highly incomplete and needs expansion.
Template for hypothesis formulation
A structured format for creating testable hypotheses, including:
- Work Role/Category and Specialty Area
- Testable statements
- Population/Intervention/Comparator definitions
- Quantitative measures and data sources
- Knowledge, Skills, and Abilities (KSAs) targeted
- Related tasks and success criteria
Note: This document contains TODO items and needs additional context.
Presentation outline
Brief outline for a presentation covering:
- AI corporate ecosystem interconnection mapping
- Bubble vs non-bubble analysis
- Additional topics (marked as incomplete)
Proposal summary
Short proposal for creating a demo of the Learning Economies system, noting the need to scope the ontology of curriculum.
Real-world implementation of the Subset Learning Economy
Detailed analysis of Arizona State University's microcredential model as a practical implementation of Subset Economy principles:
- Modular, stackable credentials supporting career transitions
- Rapid retraining compressed from years to months
- Industry-aligned, digitally-badged competencies
- Clear pathways to larger credentials
- Lifelong learning adaptation for working professionals
This document provides concrete evidence that the Subset economy is already being implemented successfully, offering a roadmap for other institutions.
Research notes and observations
Collection of research notes covering:
- Chinese vs US education strategies and breadth approaches
- Social media influence on learning motivation
- Cost and value models for degree programs
- Student outcome tracking
- Curriculum design principles
- Relationship to NIST standards (800-161 and 800-181)
- ASU microcredential system as Subset economy model
Core Economies:
- Breadth: Multi-subject mastery and parallel domain competency
- Depth: Time-to-competence optimization and acceleration
- Subset: Career mobility through rapid retraining
- Superset: Field reinvention and creative destruction
- Network-form: Multi-scale knowledge routing and synthesis
Meta-Factors:
- Resonance: Cross-domain transfer mechanism
- Scaffolding: Structured learning supports
- Amplification: Learning speed acceleration
- Compression: Dense knowledge representation
- Redundancy: Overlapping skills for resilience
- Liquidity: Knowledge asset convertibility
- Inheritance: Intergenerational knowledge transfer
Rather than creating a new "symbiosis" learning economy, AI serves as an accelerator of existing Learning Economy principles through:
- Differential capabilities in speed, scale, and pattern recognition
- Multi-scale granule routing optimization
- Dynamic pathway generation between knowledge domains
- Personalized topology adaptation
- Design experiences that develop competency across all five economies
- Strengthen Resonance capabilities in students
- Transition from knowledge transmission to Learning Economy Architecture
- Become Learning Economy Orchestrators
- Develop meta-competencies in economy navigation
- Master fluid movement between learning modes
- Focus on Learning Economy Infrastructure development
- Build institutional foundations for rapid learning acceleration
- Create competitive advantage through learning velocity
This repository represents ongoing research into the transformation of education and human capital in the AI era. The work combines theoretical economic analysis with practical implementation frameworks, aiming to provide both conceptual understanding and actionable guidance for educational transformation.
This appears to be a personal research repository. For questions or collaboration opportunities, please refer to the individual document authors.
https://scholar.google.com/scholar?hl=en&as_sdt=0%2C39&q=Ai+in+education&btnG=
Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges https://www.mdpi.com/2414-4088/9/8/84
Last updated: Based on repository contents as of current state