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

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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

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📖 Structured Intelligence Engineering Series
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