Special Education Eligibility Identification Support

Supports data collection and analysis to improve the accuracy and timeliness of identifying students who may qualify for special education services, helping reduce inappropriate placements.

The Problem

“Special Education Eligibility Identification Support”

Organizations face these key challenges:

1

Student data is fragmented across SIS, assessment, attendance, behavior, and intervention systems

2

Manual review is slow and difficult to scale across large districts

3

Referral quality varies by school, staff experience, and documentation quality

4

Important early warning patterns are missed when signals are reviewed in isolation

5

Teams need defensible, explainable recommendations due to legal and compliance sensitivity

6

False positives and false negatives both carry high educational and legal consequences

Impact When Solved

Faster identification of students who may need evaluationMore consistent screening across schools and reviewersReduced inappropriate placements through evidence-based decision supportLower administrative burden for psychologists, case managers, and MTSS teamsImproved auditability of referral rationale and supporting evidence

The Shift

Before AI~85% Manual

Human Does

  • •Review every case manually
  • •Handle requests one by one
  • •Make decisions on each item
  • •Document and track progress

Automation

  • •Basic routing only
With AI~75% Automated

Human Does

  • •Review edge cases
  • •Final approvals
  • •Strategic oversight

AI Handles

  • •Automate routine processing
  • •Classify and route instantly
  • •Analyze at scale
  • •Operate 24/7

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