Settings & Responsible AI
Configure your experience, preview what's on the roadmap, and review the commitments that keep this platform trustworthy: human oversight, transparency, privacy, and ethical use of predictive analytics. All data is fictional sample data; AI is simulated and decision-support only.
🎨 Appearance
Easier on the eyes in low light. Saved to this device.
An accessibility toolbar is available on every page (text size, contrast, spacing, and more) via the floating accessibility controls — supporting readable, inclusive access for all staff.
🧭 Default View
Presentational preferences for how the platform opens (demonstration only).
🔌 Data Integration
Planned secure, consent-aware connectors. None are active in this demonstration.
- Student Information System (SIS) Soon
- Assessment platform Soon
- Learning Management System (LMS) Soon
- Attendance system Soon
- Behavior / PBIS system Soon
- Counseling / wellness platform Soon
- Single Sign-On (SSO) Soon
✨ Future AI Features
On the roadmap — each will ship with the same responsible-AI guardrails described below.
- Predictive graduation pathways Soon
- AI attendance assistant Soon
- Natural language reporting Soon
- Voice assistant Soon
- Regional benchmarking Soon
- District analytics Soon
- Mobile notifications Soon
- Community agency referrals Soon
- API integrations Soon
⚖️ Responsible AI & Ethics
This platform treats AI as decision-support, never a decision-maker. Predictive analytics surface students who may benefit from earlier support — they do not judge, label, or determine outcomes. These commitments are built into every feature.
👤 Human oversight
A qualified educator or counselor reviews every AI suggestion and makes all decisions about students. AI cannot place, refer, or act on its own.
🔍 Transparency
AI output is clearly labeled as simulated/decision-support, with sources and limitations stated. Users always know when they are seeing AI-generated content.
🧠 Explainability
Every risk indication shows its contributing factors and a confidence level, so educators can question, validate, or override it.
🔐 Privacy & student data protection
Data minimization, role-based access, and alignment with student-privacy law (e.g., FERPA-style protections). In this demo, nothing leaves the browser.
⚖️ Bias monitoring
Models and indicators are reviewed for disparate impact across student groups, with ongoing fairness checks and correction.
🛡️ Data security
Encryption in transit and at rest, least-privilege access, audit logging, and secure integrations are required for any production deployment.
✅ Consent considerations
Sensitive data (e.g., well-being) is collected voluntarily where appropriate, with informed consent and assent, and used only for the stated supportive purpose.
📈 Appropriate use of predictive analytics
Predictions open doors to support — never close them. They are not used for punitive tracking, ranking, or denying opportunity.
🤝 Ethical decision-making
Decisions center the student's best interest, dignity, and growth, guided by professional judgment and clear policy.
🚦 Safeguards against over-reliance on AI
Training, "educator decides" prompts, confidence disclosures, and review steps keep humans firmly in the loop and prevent automation bias.
🗺️ Implementation Guide
Written for districts, ministries, leadership teams, and student-support services planning a responsible rollout.
- Implementation roadmap. Begin with readiness and goal-setting, sequence integrations, and phase features so support capacity grows alongside the data.
- Data governance. Establish a governance team, data dictionary, retention schedule, and clear ownership and access policies before connecting any system.
- Privacy. Complete a privacy impact assessment, align to applicable student-privacy law, and define consent, minimization, and de-identification practices.
- Responsible-AI framework. Adopt the human-oversight, transparency, explainability, and bias-monitoring commitments above as binding policy with named accountable owners.
- Professional development (PD). Train staff to interpret indicators, question AI, protect privacy, and respond with supportive, evidence-based interventions.
- Technology integration. Connect SIS, assessment, LMS, attendance, behavior, and wellness sources via secure SSO; validate data quality before going live.
- Pilot. Run a small, well-supported pilot (a few grades or a campus), gather educator and family feedback, and refine indicators and workflows.
- Scaling. Expand in waves with shared playbooks, coaching, and capacity checks so each new site is ready to act on what the data surfaces.
- Success metrics. Track student outcomes (attendance, on-track, well-being, equity gaps) plus process metrics (timely interventions, family engagement) — not just usage.
- Continuous improvement. Review fairness, accuracy, and impact on a regular cadence; retire or adjust what isn't helping students.
🗃️ Data
This demonstration stores nothing on a server. Your theme choice, AI Risk Engine history, and usage counts live only in this browser's local storage.
Local keys used on this device: ess:theme ess:hist ess:stats. Clearing removes ess:hist and ess:stats; your theme preference stays until you toggle it.
All data shown is realistic fictional sample data created for demonstration. AI is simulated client-side and is decision-support only — educators make every decision.