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Responsible AI for Healthy and Thriving Learners — Principles, Practice and Policy

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  1. Policy, Principles and Practical Implementation
    5 Topics
  2. Foundations: Key Definitions and How to Use This Course
    3 Topics
  3. Responsible AI Innovation for Young People and Educators
    6 Topics
  4. Navigating the Boundary: Educational AI vs. Health Services
    5 Topics
  5. AI’s Impacts on Young People’s Well‑Being
    5 Topics
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Photorealistic editorial scene of a diverse three-person tech team gathered around a transparent interactive tabletop displaying a clear model-explainability flow with highlighted features and score weights, a visible provenance chain of source thumbnails with metadata labels and QR codes, and labeled content tags. Foreground shows a clipboard stamped "Provenance Verified" with a "Bias Audit" checklist, a magnifying glass over dataset entries, and a tablet open to "Labeling Guidelines." Background posters read "Explainability" and "Content Integrity" beside a subtle balanced-scales icon; warm natural window light, shallow depth of field, and crisp 50mm editorial framing convey a professional, trustworthy audit atmosphere.

How to require and assess explainability, content provenance, labeling and concrete anti‑bias practices from vendors and teams.