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posted an update about 2 hours 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*.
published an article about 2 hours ago
Building an SI-Core Wrapped Learning Companion - *PoC architecture for education and developmental support*
posted an update 2 days ago
✅ New Article: *SI-Core for Individualized Learning & Developmental Support* Title: 🎒 SI-Core for Individualized Learning and Developmental Support - From Raw Logs to Goal-Aware Support Plans 🔗 https://huggingface.co/blog/kanaria007/individualized-learning-and-developmental-support --- Summary: Most “AI in education/support” stacks optimize shallow outputs (scores, clicks) and lose the *why*: goals, trade-offs, and safety. This guide reframes learning & developmental support as an *auditable, multi-goal system*—where every intervention is logged as an effect, evaluated against goal trajectories, and constrained by runtime ethics. > Learners aren’t numbers to optimize — > they’re agents with goals, dignity, and long histories. --- Why It Matters: • Turns tutoring/support into *goal-aware planning*, not content roulette • Makes decisions *explainable* (“Why this activity?”) with evidence trails • Adds *runtime ethics* for vulnerable learners (fatigue, dignity, bias, consent) • Enables improvement over time via *governed pattern learning*, not silent drift --- What’s Inside: • Goal surfaces + how to define “success” without collapsing into a single score • Effect Ledger design: *what we did, why, under which constraints, and what happened* • Practical ethics constraints for children / developmental differences • Human-in-the-loop workflows: dashboards, contestation, approvals • Integration patterns: assessments, IEP/MTSS/RTI, privacy/erasure alignment • A phased migration path from today’s LLM tutors to SI-wrapped support systems --- 📖 Structured Intelligence Engineering Series This isn’t “AI replaces teachers/therapists.” It’s *AI that can be supervised, questioned, audited, and improved safely*—in the places where that matters most.
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