Adaptive Assignments
The Problem
When teachers assign a lesson quiz, three types of remediation are auto-assigned:
Reteach to Build Understanding
Build Mathematical Literacy
Additional Vocabulary Support
These are delivered as static PDFs to all students, regardless of which specific skills or concepts they struggled with. As a result, teachers feel these resources are too generic and not aligned to the actual student errors. This forces many teachers to turn to external platforms like IXL or i-Ready to deliver more targeted reteaching support.
Leveraging AI & The Realize Platform
AI can help transform this process by using lesson quiz data to intelligently "bucket" student needs across three domains (our core program components) of mathematical proficiency; Quiz standards can be tagged into the three instructional categories (based on existing Topic Overview lenses), enabling precise remediation tailored to each learner’s gaps.
Why this works:
Scalability: MODERATE to HIGH
The classification logic can be rules-based to start, and refined over time using performance data.
Our content already supports conceptual, procedural, and application-based tasks—it’s about activating them through tagging and smarter AI routing.
Provides teachers with richer, more meaningful data while saving time—and gives students the exact practice they need, not review worksheets that need grading and more remediation.
