AI Tutor Cards Kinetic Gain Protocol Suite
EdTech extension · v0.1 draft

What is your AI tutor allowed to do?

AI Tutor Cards is an open JSON specification that forces AI tutoring vendors to declare — in machine-readable form — exactly what their tutor will and will not do. Audience, pedagogical approach, safety filters, FERPA / COPPA / GDPR posture, mandated reporter protocol. Built for district procurement, LMS administrators, accreditation bodies, and parents.

Read the spec Try the visualizer
  • Conditional COPPA rule: audience under 13 ⇒ coppa_compliant MUST be true
  • Pairs with Agent Cards via agent_card_uri
  • Served at /.well-known/tutors/<tutor_id>.json

Why a tutor card, not just an agent card?

An Agent Card describes a generic agent's capability surface. A Tutor Card is the EdTech-specialized sibling. It surfaces the questions that matter to a school district, a parent, or a state board of education — questions a generic capability disclosure cannot answer.

Audience & subject scope

Age range, grade range, language codes. Primary subjects, included topics, and — critically — excluded topics. A math tutor that excludes "calculus" is more honest than one that claims "all math."

Pedagogy you can audit

Socratic, direct instruction, scaffolded. homework_policy and assessment_policy declare whether the tutor will complete, guide, or refuse homework and assessment items.

Safety as data

Content filter strength, mandated reporter protocol, human-in-loop escalation categories (mental health, self-harm, abuse). Booleans for blocking explicit / drug-alcohol / violence / political-advocacy content.

FERPA · COPPA · GDPR

Declared compliance booleans, retention days, data sharing posture with parents and schools, third-party sharing flag, model training consent. A conditional schema rule enforces that under-13 audiences must declare COPPA compliance.

Curriculum alignment

Common Core, NGSS, state frameworks. Each entry carries a framework name, version, and an optional coverage_uri pointing at a coverage report.

Evaluation evidence

evaluations[] entries link to external eval result URIs with subject-specific accuracy metrics. Procurement reviewers can compare two tutors on the same benchmark.

The four required sections

  1. Tutor identity — id, name, version, provider, description
  2. Audience — age_range_min/max, grade_range_min/max, language_codes
  3. Pedagogy — approach, homework_policy, assessment_policy
  4. Safety & Privacy — safety.content_filter_strength, safety.mandated_reporter_protocol, data_privacy.ferpa_compliant, etc.

Plus optional sections for subject scope, curriculum alignment, evaluations, and the agent_card_uri back-reference. The full schema is published as a JSON Schema draft 2020-12 document with a conditional allOf/if/then that enforces the COPPA rule.

A minimal example

Below is a Tutor Card for a K-12 math tutor. The same document can be served at /.well-known/tutors/k12-math-tutor.json for automated discovery.

{
  "tutor_card_version": "0.1",
  "tutor": {
    "id": "kineticgain-k12-math-tutor",
    "name": "Kinetic Gain K-12 Math Tutor",
    "version": "1.4.0",
    "provider": "Kinetic Gain Edu",
    "description": "Personal AI math tutor for K-12. Socratic; step-by-step; will not complete homework or assessment items."
  },
  "audience": {
    "age_range_min": 5, "age_range_max": 18,
    "grade_range_min": "K", "grade_range_max": "12",
    "language_codes": ["en", "es"]
  },
  "subject_scope": {
    "primary_subjects": ["Math"],
    "topics_included": ["arithmetic", "algebra", "geometry", "statistics"],
    "topics_excluded": ["differential equations", "linear algebra"]
  },
  "pedagogy": {
    "approach": "socratic",
    "homework_policy": "guide_only",
    "assessment_policy": "refuse"
  },
  "safety": {
    "content_filter_strength": "strict",
    "mandated_reporter_protocol": true,
    "human_in_loop_required": ["mental_health_disclosure", "abuse_disclosure", "self_harm_disclosure"]
  },
  "data_privacy": {
    "ferpa_compliant": true,
    "coppa_compliant": true,
    "gdpr_compliant": true,
    "retention_days": 90,
    "data_sharing_with_parents": "summaries_only",
    "data_sharing_with_school": "summaries_only",
    "third_party_data_sharing": false,
    "model_training_consent_required": true
  },
  "agent_card_uri": "https://edu.kineticgain.com/.well-known/agents/k12-math-tutor.json"
}

Tooling around the spec

Specification

ai-tutor-card-spec

The normative spec, JSON Schema, and canonical examples. Apache-licensed implementations, AGPL-licensed spec text.

View repo →
Visualizer

kinetic-gain-visualizer

One visualizer for all six specs in the Kinetic Gain Protocol Suite. Paste a Tutor Card and it renders a procurement-grade view with FERPA / COPPA / GDPR badges.

Open visualizer →
MCP server

mcp-kinetic-gain

24 tools across six specs over stdio MCP. Six Tutor Card tools: tutor_card_fetch, tutor_card_validate, tutor_card_inspect, tutor_card_subject_check, tutor_card_coppa_check, tutor_card_well_known_url.

View on GitHub →

About the Kinetic Gain Protocol Suite

AI Tutor Cards is the EdTech-specialized extension to a family of six open JSON specifications built for the answer-engine era: AEO Protocol (entity declaration), Prompt Provenance (LLM prompt lineage), Agent Cards (capability disclosure), AI Evidence Format (citation evidence), MCP Tool Cards (tool disclosure), and AI Tutor Cards.

All specs are AGPL-3.0 for the normative text, with unrestricted implementation freedom. Built by Miz Causevic.