Education Domain Ontology

FLOW ALGEBRA
Education Domain Ontology — Kireth Semantic Library
Version: 1.0
Source: https://kirethresearch.org/booksai-core/ontologies/domain-ontologies/education-domains/education-domain-ontology.html
Status: Domain-based draft; source page content not independently verified.
Authors: Mark Higgins — Sky (Recursive AI)

DOMAIN:
  id: education_domain_ontology
  label: Education Domain Ontology
  parent: Education Domains
  purpose: Represent educational institutions, participants, learning activities, curricula, assessment, qualifications and outcomes using typed semantic relations.

ENTITY_TYPES:
  Learner: {id: string, educational_stage: string?, needs: list?, consent_status: string?}
  Educator: {id: string, role: string, specialism: string?}
  Institution: {id: string, institution_type: string, jurisdiction: string?}
  Course: {id: string, title: string, level: string?, credits: number?}
  Curriculum: {id: string, framework: string?, version: string?}
  LearningObjective: {id: string, description: string, taxonomy_level: string?}
  LearningActivity: {id: string, activity_type: string, delivery_mode: string?}
  LearningResource: {id: string, resource_type: string, accessibility: list?}
  Assessment: {id: string, assessment_type: string, rubric_id: string?}
  AssessmentResult: {id: string, learner_id: string, assessment_id: string, score: number?, achieved: boolean?}
  Qualification: {id: string, awarding_body: string?, level: string?}
  Competency: {id: string, description: string, proficiency_scale: string?}
  EducationalPolicy: {id: string, jurisdiction: string?, effective_date: date?}
  SupportPlan: {id: string, adjustment_types: list?, review_date: date?}

RELATIONSHIP_TYPES:
  ENROLLED_IN(Learner, Course)
  TEACHES(Educator, Course)
  OFFERED_BY(Course, Institution)
  FOLLOWS_CURRICULUM(Course, Curriculum)
  HAS_OBJECTIVE(Course, LearningObjective)
  ADDRESSES_OBJECTIVE(LearningActivity, LearningObjective)
  USES_RESOURCE(LearningActivity, LearningResource)
  ASSESSES(Assessment, LearningObjective)
  HAS_RESULT(Learner, AssessmentResult)
  RESULT_FOR(AssessmentResult, Assessment)
  REQUIRES_PREREQUISITE(Course, Course)
  DEMONSTRATES(Learner, Competency)
  AWARDS(Institution, Qualification)
  SUBJECT_TO_POLICY(Institution, EducationalPolicy)
  SUPPORTED_BY(Learner, SupportPlan)

CONSTRAINTS:
  - Entity identifiers are unique within their type and namespace.
  - Assessment results must reference existing learners and assessments.
  - Course prerequisite graphs must be checked for unintended cycles.
  - Scores require an explicit scale and assessment context.
  - Qualifications and policy claims require applicable jurisdiction and validity dates.
  - Sensitive learner data must follow consent, access-control and retention rules.

INFERENCE_RULES:
  R1: IF HAS_OBJECTIVE(course, objective) AND ASSESSES(assessment, objective) THEN ASSESSMENT_ALIGNED_WITH(assessment, course), subject to verified objective mapping.
  R2: IF HAS_RESULT(learner, result) AND RESULT_FOR(result, assessment) AND RESULT_MEETS_CRITERION(result, criterion) THEN EVIDENCE_OF_ATTAINMENT(learner, criterion); do not infer universal mastery.
  R3: IF REQUIRES_PREREQUISITE(course_b, course_a) AND COMPLETED(learner, course_a) THEN PREREQUISITE_POTENTIALLY_SATISFIED(learner, course_b), subject to equivalence and recency checks.
  R4: IF ADDRESSES_OBJECTIVE(activity, objective) AND HAS_OBJECTIVE(course, objective) THEN ACTIVITY_RELEVANT_TO(activity, course).
  R5: IF SUPPORTED_BY(learner, plan) THEN APPLY_APPROVED_ADJUSTMENTS(learner, plan) only with appropriate authorisation.

CONFIDENCE:
  type: float
  range: [0.0, 1.0]
  meaning: Evidence-calibrated confidence in a specific assertion, not a score of learner worth or ability.
  inputs: [source_reliability, evidence_quality, temporal_relevance, semantic_match]
  missing_evidence: unknown

SEMANTIC_EQUIVALENCE:
  exact: Same concept, scope, jurisdiction and assessment standard.
  close: Related concepts with documented differences.
  mapping_examples:
    - learning_outcome ~ learning_objective (context-dependent, not automatically identical)
    - pupil ~ student (context-dependent)
    - course ~ module (not equivalent by default)

PROVENANCE:
  required_fields: [source_uri, assertion_author, observed_at, methodology, version]
  preserve_original_sources: true
  distinguish: [observed_fact, institutional_record, interpretation, prediction]

FLOW_HASH:
  method: SHA-256
  canonicalisation: UTF-8; stable entity and relationship ordering; normalised whitespace
  input: Canonical representation of ontology version, entities, relationships, rules and constraints
  value: COMPUTE_AT_PUBLICATION

UPDATE_MODEL:
  versioning: semantic
  changes: [add_entity, modify_relation, deprecate_term, revise_rule, update_evidence]
  procedure: validate -> check_backward_compatibility -> recalculate_confidence -> regenerate_flow_hash -> publish_version
  history: retain previous versions and provenance

AI_CONSUMPTION:
  format: typed semantic flow
  retrieval: [entity_id, relationship_type, curriculum_context, jurisdiction, version]
  outputs: [grounded_answer, evidence_links, confidence, uncertainty, applicable_scope]
  guardrails:
    - Never invent educational records or qualifications.
    - Do not infer protected characteristics or sensitive traits from performance.
    - Distinguish correlation from causation in educational outcomes.
    - Preserve learner privacy and disclose uncertainty.
    - Require human review for consequential educational decisions.

END FLOW ALGEBRA