Special Education Coordinator
Coordinate evaluations and eligibility determinations
What You Do Today
Manage the referral-to-evaluation pipeline—reviewing referrals, assigning evaluators, ensuring assessments are completed within timeline, and facilitating eligibility team meetings.
AI That Applies
AI automates referral tracking, matches evaluators to caseloads based on specialization and availability, and pre-populates evaluation reports with existing student data.
Technologies
How It Works
The system ingests specialization and availability as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
Evaluation logistics become more efficient with automated scheduling and pre-populated reports, reducing the administrative burden on evaluators.
What Stays
Conducting student evaluations, interpreting complex assessment results, and making eligibility determinations that account for cultural and linguistic factors require specialized professional judgment.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for coordinate evaluations and eligibility determinations, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long coordinate evaluations and eligibility determinations takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your department chair or principal
“What data do we already have that could improve how we handle coordinate evaluations and eligibility determinations?”
They influence which ed-tech tools get approved and funded
your instructional technologist
“Who on our team has the deepest experience with coordinate evaluations and eligibility determinations, and what tools are they already using?”
They support the tech stack and can show you capabilities you don't know exist
your school counselor
“If we brought in AI tools for coordinate evaluations and eligibility determinations, what would we measure before and after to know it actually helped?”
They see the student impact side of AI-adaptive tools
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.