✎ Student AI Disclosure Kinetic Gain Protocol Suite
EdTech trio · student-side

What did the student
actually use AI for?

Student AI Disclosure is an open JSON spec attached to student-submitted work: AI tools used (with back-refs to Agent / Tutor Cards), 13-value role taxonomy, assistance extent, prompt evidence (full / hashed / omitted), artifact-hash binding, AUP reference, signature + teacher acknowledgment. The student-side of the EdTech trio.

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  • Detect via disclosure_version
  • Well-known: — (travels with the artifact)
  • Cross-referenced from AI Incident Cards on failure

Why disclosure is the lawful state

Most academic-integrity policies treat any AI use as suspicious. This spec inverts the framing: disclosure is the lawful state. A disclosure with ai_used: false is valid. A disclosure with ai_used: true and a list of roles is valid. Missing disclosure on an assignment that requires one is the failure mode — not AI use. The spec punishes omission, not use.

AI usage facts

ai_used bool, tools_used[] with agent_card_uri / tutor_card_uri back-refs, roles[] from a 13-value taxonomy, assistance_extent (minor / substantial / primary_author), optional assistance_pct.

Prompt evidence — three modes

full (literal text preserved), hashed (SHA-256 instead of literal text), omitted (not retained). Hashes reduce text retention but can still reveal common prompts. Schema gates which fields can appear per mode.

Artifact binding

artifact_hash records SHA-256 over the submission bytes. A reviewer can recompute and compare the hash to detect changed bytes.

AUP reference

aup_uri points at a declared Classroom AI AUP. A reviewer must confirm the applicable policy version and facts before deciding whether the use was allowed.

Student declaration + acknowledgment

signed_by_student: true + student_signature_at are required self-reported fields, not a cryptographic signature. An optional teacher_acknowledged block records grader review.

Closes the EdTech trio

Links to Tutor Cards (vendor-side) and Classroom AI AUP (district-side). The three documents give reviewers context; their declared fields alone are not an authorization decision.

The required sections

  1. disclosure_version — must be "0.1"
  2. student — pseudonymous id, optional display_name, grade_or_year, institution_id
  3. assignment — id, title, course_id, LMS name, due_at
  4. ai_used — boolean gating all AI-related fields
  5. tools_used / roles / assistance_extent / prompt_evidence_mode — required when ai_used is true
  6. artifact_hash — SHA-256 binding to submitted bytes
  7. aup_uri + policy_compliant (declared) — AUP join
  8. signed_by_student + student_signature_at — required
  9. teacher_acknowledged (optional) — by + at + note

An illustrative example

This fictional disclosure shows the format; it is not a real student record, school policy decision, signed statement, or verification of the named AI provider.

{
  "disclosure_version": "0.1",
  "disclosure_id": "d-2026-05-12-b4f9c1e8",
  "created_at": "2026-05-12T16:42:00Z",
  "student": { "id": "stu-9c2e44", "grade_or_year": "11" },
  "assignment": {
    "id": "assn-2026-bio-lab-7",
    "title": "Cell Respiration Lab Report",
    "course_id": "course-bio-11-spring-2026",
    "lms": "canvas"
  },
  "ai_used": true,
  "tools_used": [
    { "name": "Claude.ai", "provider": "Anthropic", "version": "claude-sonnet-4-6" }
  ],
  "roles": ["edit", "cite_check"],
  "assistance_extent": "minor",
  "assistance_pct": 8,
  "prompt_evidence_mode": "hashed",
  "prompts": [
    { "id": "p1", "hash": "sha256:c7d1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2c3d4e5f6a7b8c9d0e1",
      "at": "2026-05-12T15:55:00Z" }
  ],
  "artifact_hash": "sha256:1a2b3c4d5e6f7a8b9c0d1e2f3a4b5c6d7e8f9a0b1c2d3e4f5a6b7c8d9e0f1a2b",
  "aup_uri": "https://lincoln-high-district-42.edu/.well-known/ai-aup.json",
  "policy_compliant": { "declared": true,
    "reason": "AUP §3.2 permits AI-assisted grammar review and citation verification for lab reports." },
  "signed_by_student": true,
  "student_signature_at": "2026-05-12T16:42:00Z"
}

Tooling around the spec

Specification

student-ai-disclosure-spec

Normative spec, JSON Schema 2020-12, canonical examples. MIT-licensed spec text, schema, and examples; reference implementations licensed separately.

View repo →
Visualizer

kinetic-gain-visualizer

Unified visualizer for supported Suite documents. Auto-detects via disclosure_version and renders a procurement-grade view.

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MCP server

mcp-kinetic-gain

v0.10.0 offers 75 tools for 12 Suite specs, DefenseTech checks, and implementation previews. Drops into Claude Desktop / Cursor / any MCP-compatible client via stdio with one config entry.

View on GitHub →

About the Kinetic Gain Protocol Suite

Student AI Disclosure is one of twelve open JSON specifications in the Kinetic Gain Protocol Suite. Five core specs plus the EdTech trio, the HealthTech extension, the cross-cutting Incident Card, the AI Procurement Decision Card, and the AI Claims Decision Card. Front door: suite.kineticgain.com.

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.