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kanaria007
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posted an update about 11 hours ago
✅ New Article: *Measuring What Matters in Learning* (v0.1) Title: 📏 Measuring What Matters in Learning: GCS and Metrics for Support Systems 🔗 https://huggingface.co/blog/kanaria007/measuring-what-matters-in-learning --- Summary: Most “AI for education” metrics measure *grades, time-on-task, and engagement*. That’s not enough for *support systems* (tutors, developmental assistants, social-skills coaches), where the real failure mode is: *the score goes up while the learner breaks*. This guide reframes learning evaluation as *multi-goal contribution*, tracked as a *GCS vector* (mastery, retention, wellbeing/load, self-efficacy, autonomy, fairness, safety) — and shows how to operationalize it without falling into classic metric traps. > If you can’t measure wellbeing, fairness, and safety, > you’re not measuring learning — you’re measuring extraction. --- Why It Matters: • Moves beyond “grading” into *support metrics* designed for real learners • Makes *wellbeing, autonomy, fairness, and safety* first-class (not afterthoughts) • Separates *daily ops metrics* vs *research evaluation* vs *governance/safety* • Turns “explainability” into *answerable questions* (“why this intervention, now?”) --- What’s Inside: • A practical *GCS vector* for learning & developmental support • How core metrics translate into education contexts (plan consistency, trace coverage, rollback health) • A tiered metric taxonomy: *Ops / Research / Safety* • Parent-facing views that avoid shaming, leaderboards, and over-monitoring • Pitfalls and failure patterns: “optimize test scores”, “maximize engagement”, “ignore fairness”, etc. --- 📖 Structured Intelligence Engineering Series Formal contracts live in the evaluation/spec documents; this is the *how-to-think / how-to-use* layer.
updated a dataset about 11 hours ago
kanaria007/agi-structural-intelligence-protocols
posted an update 1 day ago
✅ New Article: *PoC Architecture for Education & Developmental Support* Title: 🎓 Building an SI-Core Wrapped Learning Companion - PoC architecture for education and developmental support 🔗 https://huggingface.co/blog/kanaria007/poc-architecture-for-education-development-support --- Summary: Most “AI tutors” are built as *LLM-first* systems. This article flips the default: * The LLM is treated as an *untrusted proposal engine* * *SI-Core owns* observation, consent, ethics, memory, and rollback * Teachers and guardians get *real oversight*, not just chat transcripts Scoped intentionally to *one subject × a small cohort (10–30 learners)*, this is a PoC you can actually ship—and audit. > Don’t ask: “Can an AI replace teachers?” > Prove: “Can we make an AI companion *safe, explainable, and governable* for real learners?” --- Why It Matters (for AI on real stacks): • *Consent & accommodations* are first-class (especially for minors / neurodivergent learners) • *Ethics decisions are logged* (ALLOW / DENY / ESCALATE) with traceable reasoning • “*Why this?*” explanations are built in for learners—and deeper inspection for adults --- What’s Inside: • A minimal reference architecture (frontend → SI-Gate → ethics/memory/logging → LLM APIs) • Non-negotiables for the pilot (SI-wrapped LLM, Effect Ledger, ethics overlay, dashboards) • Failure modes + safe-mode behavior • Implementation checklist + rough effort/cost ballparks (kept explicitly non-normative) --- 📖 Structured Intelligence Engineering Series A deployable pattern for taking today’s LLM tutor ideas and making them *auditable, overrideable, and rollback-safe*.
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