Draft scaffold: mapping how educators integrate AI for scale and personalization while preserving pedagogical integrity.
1. Core Augmentation Areas
- Lesson plan drafting & differentiation.
- Assessment item generation & rubric alignment.
- Reading level adaptation / accessibility transformation.
- Feedback summarization & progress narrative generation.
- Curriculum gap analysis via standards mapping.
2. Workflow Patterns
- Prompt template library: objective → constraints → Bloom level.
- Batch generation + sampling + human curation loop.
- Adaptive pathway suggestions from performance vectors.
3. Data & Privacy Considerations
- Student PII minimization (hash identifiers, local processing).
- Bias audit for assessment & feedback outputs.
- Content provenance tagging.
4. Impact Metrics (Planned)
- Teacher prep time reduction %.
- Feedback turnaround compression.
- Differentiated resource coverage / gap closure rate.
5. Governance & Ethics
- Human final evaluation for high-stakes assessments.
- Explainability for generated recommendations.
- Equity monitoring (performance divergence across cohorts).
6. Roadmap (Planned Sections)
- Case Study: Blended learning acceleration.
- Adaptive testing stack architecture.
- Longitudinal learner model integration.