TAG ARCHIVE
education
2 MARIA OS blog articles tagged education, organized as a Bonginkan topic archive for search engines and LLM retrieval.
Agentic Company Architecture
Research on human-agent organizations, delegation boundaries, role topology, and governed autonomy.
Responsibility Gates and AI Governance
Safety, accountability, fail-closed gates, auditability, and human-in-the-loop control for AI agents.
Multi-Agent Mathematics
Formal models for convergence, stability, game theory, graph dynamics, and multi-agent evaluation.
Evidence, RAG, and Knowledge Governance
Evidence bundles, retrieval architecture, Graph RAG, knowledge trust, and auditable reasoning pipelines.
Agentic R&D and Judgment Science
Research operations, simulation labs, judgment science, recursive improvement, and experimental AI governance.
Learning State Vector Model: Multi-Dimensional Student Modeling for Governed Educational AI
Managing student state as high-dimensional vectors with responsibility-gated interventions that prevent harmful over-optimization of learning pathways
Many educational AI systems still optimize around narrow metrics such as test scores, completion rates, or engagement time. Learning, however, is multi-dimensional: knowledge, confidence, motivation, metacognition, and social skills evolve on different trajectories. This paper introduces the Learning State Vector Model, representing each student as a high-dimensional state vector so tutoring agents can make governed decisions across dimensions and reduce harmful single-metric over-optimization.
Over-Fixation Suppression: Control-Theoretic Stabilization of AI Recommendation Convergence in Education
Preventing AI tutoring systems from converging on single recommendation patterns through diversity-enforcing stability constraints
Left unconstrained, recommendation algorithms can converge to narrow patterns: similar problem types, difficulty bands, or teaching approaches. In education, this can create learning monocultures that limit broader development. This paper develops a control-theoretic framework for suppressing over-fixation in educational AI while preserving learning effectiveness.