AI Intervention Evidence Review

Evaluates proposed AI-enabled educational interventions against evidence, instructional goals, and school strategy to support accreditation-aligned adoption decisions and avoid hype-driven selection.

The Problem

“AI Intervention Evidence Review for Accreditation-Aligned School Adoption Decisions”

Organizations face these key challenges:

1

Vendor claims are difficult to verify against independent evidence

2

Review criteria vary by reviewer and meeting context

3

Instructional goals, school strategy, and accreditation standards are not consistently linked to adoption decisions

4

Research evidence is scattered across PDFs, websites, and internal documents

Impact When Solved

Cuts review preparation time for each intervention from days to hoursStandardizes evidence scoring across schools, departments, and reviewersCreates accreditation-ready documentation of adoption rationaleReduces spend on low-evidence or poorly aligned AI tools

The Shift

Before AI~85% Manual

Human Does

  • •Collect vendor proposals, research studies, pilot reports, and strategy documents for review
  • •Read materials and compare intervention claims to instructional goals, school priorities, and accreditation expectations
  • •Discuss evidence quality, implementation readiness, and fit in committee meetings
  • •Draft recommendation memos and document adoption rationale for approvals and records

Automation

    With AI~75% Automated

    Human Does

    • •Set review criteria, weighting, and decision thresholds for intervention evaluation
    • •Review AI-generated scorecards, risk flags, and recommendation packets
    • •Resolve ambiguous cases, challenge weak assumptions, and request deeper review where needed

    AI Handles

    • •Ingest proposals, research, pilot plans, and internal strategy documents into a structured review record
    • •Extract intervention claims and map them to instructional goals, strategic priorities, accreditation expectations, and review rubrics
    • •Assess evidence strength, implementation feasibility, privacy readiness, and alignment gaps across proposals
    • •Generate standardized scorecards, recommendation memos, standards alignment matrices, and auditable decision documentation

    Operating Intelligence

    How it works

    AI runs the first three steps autonomously.

    Humans own every decision.

    The system gets smarter each cycle.

    Confidence95%
    ArchetypeRecommend & Decide
    Shape6-step converge
    Human gates1
    Autonomy
    67%AI controls 4 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapeconverge

    Step 1

    Assemble Context

    Step 2

    Analyze

    Step 3

    Recommend

    Step 4

    Human Decision

    Step 5

    Execute

    Step 6

    Feedback

    AI lead

    Autonomous execution

    1AI
    2AI
    3AI
    5AI
    gate

    Human lead

    Approval, override, feedback

    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

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