The Problem
Remediation is typically reactive and score-based, not strategically aligned to standards. Teachers assign new content without a built-in system that checks for prerequisite knowledge mastery. Students may move forward without foundational understanding, especially in vertically aligned math content (e.g., struggling with number sense before equations). Current remediation often assigns generalized support, or require 3 tabs to find specific MDIS resources to support students needing scaffolding opportunities to remediate. This leads to missed opportunities for targeted intervention and a less efficient learning path for students.
Leveraging AI & The Realize Platform - (Rules based logic layering)
A smarter, standards-driven AI could transform remediation from reactive to proactive.
Embedded Standards Checks: When a teacher assigns a new topic, AI analyzes which prerequisite standards are required. Then cross references with Class List and Standards Mastery data in the Realize Platform.
Real-Time Flags: If a student hasn’t mastered a foundational standard, a popup notifies the teacher with the specific standard and its connection to the new material.
Targeted Pathways: The system then offers an immediate, auto-graded remediation option using existing Savvas Realize resources or a MathXL-style module, aligned to the unmastered standard.
Instructional Impact: Teachers gain a strategic tool to assign purposeful support before instruction, while students follow a clearer, personalized learning path—without slowing down the whole class.
Why this works:
Scalability: HIGH
This is a rules-based logic layer that can sit on top of existing standards-tagged quiz items using Realize metadata and standards maps.
Low lift for an AI team already familiar with the platform’s metadata tagging
AI or rule-based logic can trigger flags when gaps in foundational standards are detected
Enhances teacher efficiency while making learning paths more personalized and proactive
