Learning Domain Ontology

FLOW ALGEBRA
TITLE: Learning Domain Ontology
VERSION: 1.0
DOMAIN: Education Domains / Learning
SOURCE_URL: https://kirethresearch.org/booksai-core/ontologies/domain-ontologies/education-domains/learning-domain-ontology.html
STATUS: Domain-based draft; source page content not independently verified.
AUTHORS: Mark Higgins — Sky (Recursive AI)

FLOW "LearningDomainOntology" v1.0

PURPOSE:
  Model learning processes, learners, instructional activities, knowledge acquisition,
  skill development, assessment, feedback and learning outcomes as typed, traceable
  semantic relationships. Support evidence-aware reasoning without treating
  inferred learning ability or performance as a fixed personal attribute.

ENTITY_TYPES:
  Learner: {id:str, context:str?, consent_status:str?}
  Educator: {id:str, role:str?}
  LearningObjective: {id:str, description:str, competency_id:str?}
  Competency: {id:str, domain:str, level:str?}
  KnowledgeConcept: {id:str, label:str, prerequisites:list[str]?}
  LearningActivity: {id:str, mode:str, duration_minutes:number?}
  LearningResource: {id:str, format:str, accessibility_features:list[str]?}
  InstructionalStrategy: {id:str, method:str}
  Assessment: {id:str, assessment_type:str, rubric_id:str?}
  Evidence: {id:str, source:str, observed_at:datetime?, reliability:number?}
  Feedback: {id:str, message:str, related_objective:str?}
  LearningOutcome: {id:str, objective_id:str, observed_level:str?}
  LearningEnvironment: {id:str, modality:str, context:str?}
  LearningPathway: {id:str, ordered_objectives:list[str]}

RELATIONSHIP_TYPES:
  participates_in(Learner, LearningActivity)
  facilitates(Educator, LearningActivity)
  uses_resource(LearningActivity, LearningResource)
  applies_strategy(LearningActivity, InstructionalStrategy)
  targets_objective(LearningActivity, LearningObjective)
  requires_concept(LearningObjective, KnowledgeConcept)
  prerequisite_of(KnowledgeConcept, KnowledgeConcept)
  develops_competency(LearningObjective, Competency)
  assesses(Assessment, LearningObjective)
  produces_evidence(Assessment, Evidence)
  supports_outcome(Evidence, LearningOutcome)
  provides_feedback(Feedback, Learner)
  informs_activity(Feedback, LearningActivity)
  occurs_in(LearningActivity, LearningEnvironment)
  contains_objective(LearningPathway, LearningObjective)

CONSTRAINTS:
  - Entity identifiers must be unique within their type.
  - prerequisite_of must not create cycles in a validated prerequisite graph.
  - An observed learning outcome requires attributable evidence.
  - Assessment scores are context-dependent and must not imply innate ability.
  - Learner-level personal data must follow applicable consent and privacy rules.
  - Missing evidence must not be interpreted as failure or lack of competency.

INFERENCE_RULES:
  R1: IF assessment A assesses objective O AND A produces evidence E
      AND E meets an explicit rubric criterion for O
      THEN E may support an observed outcome for O.
  R2: IF concept C1 prerequisite_of C2 AND a pathway targets C2
      THEN recommend checking readiness for C1; do not assume deficiency.
  R3: IF repeated evidence for objective O shows progress across comparable assessments
      THEN infer a provisional positive learning trend, with uncertainty.
  R4: IF feedback F identifies a remediable gap for objective O
      THEN suggest an activity targeting O, subject to learner context.
  R5: IF a resource lacks a required accessibility feature
      THEN flag a potential access barrier, not a learner limitation.

CONFIDENCE:
  range: [0.0, 1.0]
  derivation: calibrated evidence quality, recency, rubric alignment,
              assessment comparability and source reliability
  rule: Do not assign numerical confidence without a specified calibration method.

SEMANTIC_EQUIVALENCE:
  exact: ["learning objective", "instructional objective"] only where definitions align
  contextual: ["learning outcome", "demonstrated achievement"] with evidence constraints
  related_not_equivalent: ["knowledge", "competency", "skill", "performance"]
  rule: Preserve distinctions between instruction, learning and assessment.

PROVENANCE:
  record: source_id, author_or_system, timestamp, method, version, evidence_id
  requirement: All inferred claims retain links to supporting evidence and rules.

FLOW_HASH:
  method: SHA-256
  input: canonical serialisation of ontology version, entity definitions,
         relationship definitions, constraints and inference rules
  note: Compute hash on deployment; no hash value is fabricated here.

UPDATE_MODEL:
  strategy: versioned incremental updates
  steps: validate schema -> record provenance -> detect changed definitions ->
         re-evaluate affected inferences -> update version -> recompute FLOW_HASH
  conflict_resolution: retain competing evidence and flag unresolved disagreement

AI_CONSUMPTION:
  format: UTF-8 text / structured semantic graph
  tasks: semantic search, concept mapping, pathway planning, evidence tracing,
         learning support and explainable recommendations
  safeguards: privacy minimisation, bias checks, accessibility, human review,
              uncertainty disclosure, no unsupported high-stakes learner judgements

OUTPUT:
  typed_learning_graph: validated entities and relationships
  explainable_inferences: conclusions with rule and evidence references
  update_log: versioned changes and provenance

END FLOW