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."
EdTech extension · v0.1 draft
What is your AI tutor allowed to do?
AI Tutor Cards is an open JSON specification for AI tutoring vendors to declare — in machine-readable form — what their tutor is intended to do and refuse. Audience, pedagogical approach, safety filters, FERPA / COPPA / GDPR posture, mandated reporter protocol. Built for district procurement, LMS administrators, accreditation bodies, and parents.
- COPPA field: under-13 cards require a true declaration; a reviewer still checks legal compliance.
- 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.
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 checks the under-13 COPPA declaration; reviewers must verify actual compliance independently.
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
- Tutor identity —
id,name,version,provider,description - Audience —
age_range_min/max,grade_range_min/max,language_codes - Pedagogy —
approach,homework_policy,assessment_policy - 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
Illustrative, fictional Tutor Card. It does not describe a deployed tutor or establish FERPA, COPPA, or GDPR compliance. A vendor could publish its own card at a /.well-known/tutors/<tutor_id>.json URL 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 MIT-licensed normative spec, JSON Schema, and canonical examples. Reference implementations carry their own licenses.
View repo →Visualizer
kinetic-gain-visualizer
Visualizes supported Suite documents. Paste a Tutor Card to see its declared FERPA / COPPA / GDPR fields in a reviewable view.
Open visualizer →MCP server
mcp-kinetic-gain
v0.10.0 offers 75 tools for 12 Suite specs, DefenseTech checks, and implementation previews 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.
About the Kinetic Gain Protocol Suite
AI Tutor Cards is an EdTech extension in a family of twelve open JSON specifications built for the answer-engine era. Related specs include 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 — with its EdTech siblings Student AI Disclosure and Classroom AI AUP. Front door: suite.kineticgain.com.
The Suite specifications are MIT-licensed; reference implementations carry their own licenses. Built by Miz Causevic.
Executive tools on kineticgain.com
The apex domain hosts four browser-only tool surfaces buyers and operators reach for alongside the Suite specs: /calculators/ — six math-rubric decision calculators (AI build-vs-buy, cloud replatform ROI, compliance cost of delay, security breach exposure, AI use-case prioritizer, vendor renewal decision). /trust/ — the eight-tool Trust Pack (AI System Card Builder, Evidence Locker, Shadow AI Discovery, AI Vendor Intake, AI Incident Tabletop Kit, Executive Risk Register, Subprocessor Disclosure Template, Vendor AI Disclosure Review). /kill-list/ — the eight-category complexity tax audit. /policies/ — the 10-vertical readiness spec aggregator that points back at the specs documented here. Static pages, browser-only, no login, no telemetry.