AI Readiness Checklist for Education & EdTech

SIS data is accessible via APIs for learning analytics and intervention models.

LMS and content interactions are captured in xAPI or SCORM format for adaptive learning.

Student data governance ensures FERPA compliance with role-based access and audit controls.

Formative and summative assessment data flows into a structured analytics warehouse.

Educational content is tagged with standardized learning objectives and skill taxonomies.

Student engagement signals (attendance, participation, platform usage) are tracked and consolidated.

Learning outcomes are linked to input variables for predictive retention and success models.

Video, audio, and text-based learning interactions are captured for multimodal AI analysis.

Cloud infrastructure meeting education & edtech security and compliance requirements is operational.

A secure API gateway manages access to AI services with authentication and rate limiting.

Infrastructure for deploying and serving ML models is available with appropriate latency.

Continuous integration and deployment pipelines support AI model versioning and rollback.

Sufficient compute resources (GPU/TPU) are available for model training and inference.

A centralized model registry tracks model versions, metadata, and deployment history.

Model performance monitoring tracks accuracy, drift, latency, and resource usage.

Data encryption in transit and at rest meets FERPA (Family Educational Rights and Privacy Act) requirements.

Domain experts who understand AI capabilities are identified to champion adoption and validate outputs.

Data scientists with education & edtech domain expertise are on staff or available.

Staff across the organization receive training on AI tools, limitations, and responsible use.

AI projects include domain experts, data scientists, engineers, and compliance.

MLOps engineers manage model deployment, monitoring, and lifecycle in production.

Product managers translate business needs into AI requirements and manage AI roadmaps.

Teams can evaluate and manage AI vendors for education & edtech applications.

The organization invests in AI education through conferences, certifications, and knowledge sharing.

A formal AI policy defines acceptable use, risk levels, and approval workflows aligned with FERPA (Family Educational Rights and Privacy Act).

An ethics framework addresses fairness, transparency, and accountability for education & edtech AI.

AI models undergo structured validation testing before deployment to production.

Regular bias audits evaluate AI models across relevant demographic and operational dimensions.

An incident response plan addresses AI model failures, biased outputs, and data issues.

Structured change management guides organizational transitions from AI implementation.

Vendor AI solutions undergo proper evaluation for quality, compliance, and data handling.

Post-deployment monitoring tracks model performance, drift, and business impact continuously.

High-impact AI use cases for education & edtech are documented with expected outcomes.

An ROI model quantifies AI impact including Student completion and retention rates and Learning outcome improvement (pre/post assessment).

C-suite executives actively champion AI initiatives with dedicated budget and resources.

A 2-3 year AI roadmap aligns with the organization's education & edtech strategic plan.

AI maturity is benchmarked against education & edtech industry peers and leaders.

AI initiatives directly map to improving key stakeholder metrics and satisfaction.

Organization embraces AI experimentation with acceptance of measured failures.

Strategic partnerships with AI vendors, cloud providers, and domain experts are established.