Transparent, Educator-First AI That Supports Fair, Proactive Decisions

Our AI Vision​

We're building AI capabilities that support educators, not replace them. Our approach prioritizes:

• Insights, not verdicts — AI suggests, humans decide
• Transparency — Clear reasons for every recommendation
• Fairness — Built-in equity checks and bias monitoring
• Privacy — FERPA-aligned data handling and opt-in pilots
• Phased rollout — Start with analytics, graduate to supervised models

🎯 Behavior Tracking & Pattern Recognition​

• Early-warning signals — Simple reason codes and contextual insights; educators always decide.
• Pattern insights — Recurring contexts, co-occurring factors, and trend analysis.
• Equity & bias monitoring — Disproportionality flags, plain-language summaries, and progress tracking.
• Workload reduction — Draft snapshots and meeting notes for human review.
• Explainability — Reason codes, confidence ranges, and audit trails.

🌟 PBIS Personalization & Positive Reinforcement​

• Recognition recommender — Suggest students based on engagement and recognition gaps; opt-in nudges.
• Timing optimization — Recommend effective moments without interrupting instruction.
• Fairness guardrails — Monitor distribution and prompt equitable recognition; no quotas.
• Engagement forecasts — Anticipate dips and recommend light-touch actions.
• Staff-friendly nudges — Weekly digest; opt-in; never spam.

🛡️ Our AI Approach: Competitive Advantages​

• Educator-first — Insights, not verdicts; humans have final say.
• Transparent & auditable — Reason codes, confidence ranges, change logs, and full audit trail.
• Fairness by design — Equity checks, bias monitoring, and evidence-based practices.
• Privacy-respecting — FERPA-aligned data, opt-in pilots, no external student identifiers.
• Phased rollout — Start with analytics; graduate to supervised models.