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kanaria007/agi-structural-intelligence-protocols
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✅ New Article: *Designing, Safeguarding, and Evaluating Learning Companions* (v0.1) Title: 🛡️ Designing, Safeguarding, and Evaluating SI-Core Learning Companions 🔗 https://huggingface.co/blog/kanaria007/designing-safeguarding-and-evaluating --- Summary: Most “AI tutoring” talks about prompts, content, and engagement graphs. But real learning companions—especially for children / ND learners—fail in quieter ways: *the system “works” while stress rises, agency drops, or fairness erodes.* This article is a practical playbook for building SI-Core–wrapped learning companions that are *goal-aware (GCS surfaces), safety-bounded (ETH guardrails), and honestly evaluated (PoC → real-world studies)*—without collapsing everything into a single score. > Mastery is important, but not the only axis. > *Wellbeing, autonomy, and fairness must be first-class.* --- Why It Matters: • Replaces “one number” optimization with *goal surfaces* (and explicit anti-goals) • Treats *child/ND safety* as a runtime policy problem, not a UX afterthought • Makes oversight concrete: *safe-mode, human-in-the-loop, and “Why did it do X?” explanations* • Shows how to evaluate impact without fooling yourself: *honest PoCs, heterogeneity, effect sizes, ethics of evaluation* --- What’s Inside: • A practical definition of a “learning companion” under SI-Core ([OBS]/[ID]/[ETH]/[MEM]/PLB loop) • GCS decomposition + *age/context goal templates* (and “bad but attractive” optima) • Safety playbook: threat model, *ETH policies*, ND/age extensions, safe-mode patterns • Teacher/parent ops: onboarding, dashboards, contestation/override, downtime playbooks, comms • Red-teaming & drills: scenario suites by age/context, *measuring safety over time* • Evaluation design: “honest PoC”, day-to-day vs research metrics, ROI framing, analysis patterns • Interpreting results: *effect size vs p-value*, “works for whom?”, go/no-go and scale-up stages --- 📖 Structured Intelligence Engineering Series
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