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k12-education

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Ordinal regression and mediation analysis examining the gap between AI adoption and advocacy among K-12 educators. Part 3 of a three-paper series using JASP and Python. N=189 teachers in Jordan.

  • Updated Dec 12, 2025
  • Python

AI product case study — ideation to live production. Gemini 3 Pro · Claude Sonnet · Neo4j GraphRAG · Vertex AI · Google ADK · React + TypeScript + Tailwind · Cloud Run · Firebase · Stripe · Playwright. Live at explanova.ai

  • Updated Apr 20, 2026

Random Forest vs nested logistic regression comparing predictors of AI adoption among K-12 educators (N=189). Beliefs about student learning dominate all predictors (importance=0.415). 97.3% accuracy, AUC=1.000. JASP for stats/ML, Python for visualization. DOI: 10.5281/zenodo.17519163

  • Updated Dec 11, 2025
  • Python

Hierarchical cluster analysis of AI implementation barriers among K-12 educators (N=189). Person-centered approach reveals 8 distinct constraint profiles with inverse cost-skill relationship (φ=-.246). JASP for statistics, Python for visualization. Published: DOI 10.5281/zenodo.17519099

  • Updated Dec 12, 2025
  • Python

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