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Education AI Strategy: opportunities, use cases & the operating model
AI's durable value in education is in outcomes and operations: adaptive learning that lifts mastery and retention, enrolment and at-risk prediction, and freeing educator time from administrative load.
Where AI creates value
Adaptive learning — Personalized pathways to improve mastery outcomes
At-risk prediction — Early-alert models to lift retention and completion
Enrolment and yield — Recruitment targeting and melt prediction
Educator productivity — Lesson planning, grading and feedback automation
Academic integrity — Assessment redesign and AI-use detection limits
Student support — Advising, financial aid and services navigation
Top AI use cases
- Adaptive courseware that adjusts difficulty and sequencing to raise mastery and pass rates
- Early-alert models flagging at-risk students for advising intervention to improve retention
- Enrolment yield and summer-melt prediction to focus recruitment and financial aid outreach
- Automated formative feedback and rubric-based grading to reclaim educator instructional time
- AI advising assistants that guide students through registration, aid and support services
What has to change (operating model)
- Assessment redesign toward authentic, AI-resilient tasks as integrity policies are rewritten
- Student data governance under FERPA and GDPR extended to AI vendors and model training
- Faculty roles shift toward facilitation as AI absorbs grading and content-generation load
- Equity and bias oversight for adaptive and admissions algorithms affecting student outcomes
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