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EcoWorld Academy — Operational Intelligence Academy (OIA)

Architecture & Learning Experience Specification v1.0

1. Vision

EcoWorld Academy is not a traditional LMS.

It is an ESG Operational Intelligence Academy — an AI-native, telemetry-aware, role-aware learning ecosystem designed to transform ESG knowledge into operational capability.

The Academy serves as:

the public-facing educational arm of the EcoWorld ecosystem,
the learning and enablement layer for ZAYAZ,
a contextual ESG intelligence engine,
and a structured ESG competence infrastructure.

The Academy is designed to:

educate,
reinforce,
operationalize,
diagnose misunderstandings,
and continuously improve ESG competency across organizations and supply chains.

NOTE: The Academy Platform is stored in a different GitHub Repo - ecoworld-academy-content. These descriptions is for informational purposes only.

2. Strategic Positioning

Brand Separation

EcoWorld

Public-facing:

academy
ESG education
certifications
public marketplace
awareness
inbound growth

ZAYAZ

Infrastructure-facing:

ESG computation
workflows
reporting
telemetry
AI orchestration
compliance intelligence

This preserves:

white-label neutrality,
enterprise trust,
partner flexibility,
and modular deployment capabilities.

3. Core Philosophy

Precision Before Automation

The Academy follows the same architectural philosophy as ZAYAZ:

explainability,
traceability,
contextual intelligence,
and operational trust.

The goal is not passive learning.

The goal is: measurable ESG operational capability.

4. Educational Architecture

Learning Progression Model

Each learning module follows a structured orchestration sequence:

Context & relevance
Video orientation
Interactive learning
Retrieval practice
Applied operational exercises
Workflow transfer
AI reinforcement
Spaced recall
Certification

5. Course Structure

Course Hierarchy

Course
 └── Module
      └── Lesson
           └── Knowledge Objects
                ├── Video
                ├── MDX Lesson
                ├── Case Study
                ├── Workbook Exercise
                ├── Quiz
                ├── Tool
                ├── AI Hint
                ├── ESG Reference
                └── Reinforcement Prompt

6. Content Components

6.1 Video Lessons

Purpose:

orientation,
engagement,
conceptual introduction.

Implementation:

Synthesia-generated multilingual videos.
Hosted via Bunny Stream / Vimeo / Cloudflare R2.

Guidelines:

calm pacing,
conversational authority,
concise delivery,
focused on core concepts.

6.2 MDX Knowledge Lessons

MDX is the primary structured learning format.

Purpose:

modularity,
AI readability,
contextual injection,
reusability,
semantic searchability.

Example:

## Scope 3 Transport Emissions

<VideoEmbed />

<ImportantNote />

<CaseStudy />

<KnowledgeCheck />

<FrameworkReference framework="ESRS E1" />

<DownloadTemplate />

<InteractiveScenario />

<QuizPreview />

6.3 Case Studies

Three contextual case studies per module.

Purpose:

applied reasoning,
operational understanding,
ESG judgment training.

Structure:

scenario,
challenge,
stakeholder implications,
remediation analysis,
discussion prompts.

6.4 Participant Workbook

Contains:

reflection prompts,
exercises,
operational activities,
ESG analysis tasks,
implementation planning.

Purpose:

active learning,
retention,
operational transfer.

6.5 Companion Reference Book

Long-form conceptual support material.

Purpose:

deep reinforcement,
professional reference,
post-course support.

6.6 Facilitator Guide

Supports:

trainers,
consultants,
enterprise onboarding teams,
partner facilitators.

Includes:

teaching guidance,
pacing,
discussion prompts,
workshop facilitation,
challenge areas.

6.7 Supplement Tools

Operational templates and ESG workflow tools:

supplier questionnaires,
procurement dashboards,
disclosure templates,
risk trackers,
policy examples.

Purpose: bridge education → operational execution.

7. Quiz & Assessment System

7.1 Quiz Philosophy

Quizzes are:

retrieval practice,
misconception diagnostics,
reinforcement engines,
competency indicators.

The Academy prioritizes:

explanations over scores,
diagnostics over grading,
reinforcement over punishment.

7.2 Quiz Structure

Typical structure:

8–12 lessons per course,
10 questions per lesson,
80–120 questions total.

Scoring:

lesson sub-score,
module sub-score,
final course score.

8. AI Personalization Layer

8.1 AI Diagnostic Feedback

AI analyzes:

incorrect answers,
misconception patterns,
hesitation/time,
repeated failures,
role context,
prior learning history.

The system generates:

targeted explanations,
practical examples,
ESG workflow relevance,
remediation suggestions,
recommended review lessons,
mini-retests.

Example:

“You appear to confuse impact materiality and financial materiality. Review the stakeholder impact example in Lesson 3 and retry Questions 4 and 7.”

8.2 Role-Aware Learning

Learning dynamically adapts based on:

role,
framework,
NACE classification,
organizational maturity,
telemetry signals,
reporting gaps.

Supported roles:

supplier,
verifier,
executive,
sustainability lead,
procurement officer,
auditor,
regulator,
NGO,
administrator.

8.3 ZAYAZ Contextual Integration

Every Academy user receives an E-C-O™ Number.

If a learner is also a ZAYAZ client/user:

AI may reference:
- actual ZAYAZ workflows,
- forms,
- dashboards,
- remediation actions,
- verifier flows,
- telemetry findings.

Example:

“This concept appears in the Scope 3 section of your ZAYAZ supplier reporting workflow.”

Purpose: connect learning directly to operational ESG execution.

9. Spaced Recall & Reinforcement

Reinforcement Schedule

After completion:

Day 1 → quick recall
Day 7 → applied question
Day 21 → operational scenario
Day 45 → competency refresh

Examples:

mini quizzes,
scenario prompts,
AI reminders,
ESG workflow nudges.

Purpose:

long-term retention,
operational memory formation,
continuous ESG capability development.

10. Search & Knowledge Intelligence

Ask EcoWorld Academy

The Academy includes an AI-powered ESG intelligence search system.

Indexed sources:

MDX lessons,
e-books,
workbooks,
case studies,
quiz explanations,
ESG regulations,
framework references,
facilitator notes,
operational templates,
ZAYAZ guidance.

Search Layers

Layer 1 — Keyword Search

Fast exact matching.

Layer 2 — Semantic Search

Meaning-based retrieval.

Layer 3 — Knowledge Graph Search

Relationship-aware ESG intelligence retrieval.

11. Knowledge Graph Architecture

Purpose

The knowledge graph powers:

contextual learning injection,
AI recommendations,
semantic search,
misconception analysis,
spaced recall,
ESG workflow integration.

11.1. MDX Frontmatter Metadata Example

---
content_id: "AC2605-d31211dd_m001_l001"

course_id: "AC2605-d31211dd"
course_name: "Procurement Sustainability & ESRS G1-2"

module_id: "m001"
module_name: "The CSRD Context and Why Procurement Matters"

content_granularity: "module"

lessons:
  - lesson_id: "l001"
    lesson_name: "What Is the CSRD and Who Does It Apply To?"
    anchor: "what-is-the-csrd"
    estimated_duration_minutes: 12
    concepts:
      - csrd
      - reporting_scope
    quiz_bank:
      - "AC2605-d31211dd_m001_l001_g001"

  - lesson_id: "l002"
    lesson_name: "Why Procurement Matters"
    anchor: "why-procurement-matters"
    estimated_duration_minutes: 15
    concepts:
      - procurement
      - supply_chain
    quiz_bank:
      - "AC2605-d31211dd_m001_l002_g001"

module_quiz_bank:
  - "AC2605-d31211dd_m001_g001"

roles:
  - procurement
  - executive
  - sustainability_lead

frameworks:
  - CSRD
  - ESRS

concepts:
  - supply_chain
  - procurement
  - sustainability

spaced_recall:
  enabled: true
  intervals_days: [1, 7, 21, 45]
---

11.2. Frontmatter Metadata Specification

The MDX frontmatter metadata powers the EcoWorld Academy Knowledge Graph, AI personalization layer, semantic search engine, contextual learning injection, spaced recall system, and ESG workflow integration.

These metadata tags are not only descriptive. They are operational intelligence signals used throughout the EcoWorld and ZAYAZ ecosystems.

Core Identity Fields

Field	Purpose
`content_id`	Globally unique identifier for the content object. Used for traceability, AI references, telemetry, and linking across the knowledge graph.
`course_id`	Unique identifier for the course.
`course_name`	Human-readable course title.
`module_id`	Unique identifier for the module within the course.
`module_name`	Human-readable module title.
`lesson_id`	Unique identifier for the lesson/section.
`lesson_name`	Human-readable lesson title.

Quiz & Assessment Fields

Field	Purpose
`quiz_group_id`	Identifier for the associated quiz bank group.
`quiz_group_name`	Human-readable name of the quiz group.
`quiz_bank`	References linked quiz bank identifiers stored in the quiz database.
`module_quiz_bank`	Scored module test

Learning Structure Fields

Field	Purpose
`difficulty_level`	Indicates learner complexity level (e.g. foundation, intermediate, advanced). Used for adaptive learning and recommendation engines.
`estimated_duration_minutes`	Estimated completion time for pacing, planning, analytics, and learning orchestration.
`learning_objectives`	Defines measurable learning outcomes for competency tracking, AI diagnostics, and certification alignment.

Role & Audience Fields

Field	Purpose
`roles`	Defines intended learner roles (e.g. procurement, verifier, sustainability_lead). Used for role-aware learning personalization.
`frameworks`	ESG frameworks relevant to the lesson (e.g. CSRD, ESRS, GRI, TCFD). Supports semantic search and contextual guidance.

Knowledge Graph & Semantic Intelligence Fields

Field	Purpose
`concepts`	Core ESG concepts covered in the lesson. Used for semantic search, AI reasoning, spaced recall, and learning recommendations.
`signals`	ESG operational or compliance signals connected to the lesson. Used for contextual workflow injection and telemetry alignment.
`learning_signal_tags`	Higher-level operational learning signals such as `verifier_ready`, `supplier_risk`, or `scope3_gap`. Enables workflow-triggered educational guidance.
`related_content`	References related lessons, modules, or learning objects to support AI recommendations and knowledge graph traversal.

Operational Context Fields

Field	Purpose
`operational_contexts`	Defines real-world ESG workflows where this content is relevant (e.g. supplier_onboarding, materiality_assessment, verifier_review). Enables contextual learning injection within ZAYAZ workflows.
`evidence_requirements`	Indicates evidence or documentation types associated with the lesson topic. Supports verifier workflows and remediation guidance.

Reinforcement & Retention Fields

Field	Purpose
`spaced_recall`	Controls reinforcement scheduling and memory retention prompts after course completion. Used by the reinforcement engine for long-term knowledge retention.

AI & Knowledge Graph Usage

The metadata structure enables:

semantic ESG search,
AI-guided remediation,
adaptive learning pathways,
spaced recall scheduling,
misconception diagnostics,
telemetry-triggered learning injection,
workflow-aware educational guidance,
ESG competency analytics,
and contextual integration with ZAYAZ operational workflows.

The metadata system forms part of the EcoWorld Operational Intelligence Academy (OIA) knowledge graph architecture.

12. Quiz Database Architecture

Questions are stored in structured databases.

12.1. Core Question Metadata (examples)

Required fields:

quiz_bank
course_id
course_name
module_id
module_name
lesson_id
lesson_name

Additional metadata:

concept tags,
misconception tags,
framework tags,
role tags,
difficulty,
linked content,
spaced recall configuration.

Purpose:

AI diagnostics,
contextual remediation,
adaptive reinforcement,
analytics,
personalization.

12.2. SQL Structure:

quiz_banks

Stores the quiz bank identity.

quiz_bank_id TEXT PRIMARY KEY, -- AC2605-d31211dd_2605-g001
course_id TEXT NOT NULL,
course_name TEXT,
module_id TEXT,
module_name TEXT,
lesson_id TEXT,
lesson_name TEXT,
group_id TEXT, -- g001
version INT DEFAULT 1,
status TEXT DEFAULT 'draft'

quiz_questions

One row per question.

question_id TEXT PRIMARY KEY,
question_type TEXT DEFAULT 'single_choice',
quiz_bank_id TEXT REFERENCES quiz_banks(quiz_bank_id),
question_number INT,
question_text TEXT,
difficulty INT,
learning_signal_tags TEXT[], -- e.g. verifier_ready, disclosure_risk, supplier_risk, scope3_gap, governance_failure, weak_evidence_quality
concept_tags TEXT[],
misconception_tags TEXT[],
primary_misconception TEXT,
secondary_misconceptions TEXT[], -- "confuses impact materiality with financial materiality"
cognitive_skill_level TEXT,
linked_content_id TEXT,
explanation TEXT

quiz_options

One row per answer option.

option_id TEXT PRIMARY KEY,
question_id TEXT REFERENCES quiz_questions(question_id),
option_key TEXT, -- a, b, c, d
option_text TEXT,
is_correct BOOLEAN DEFAULT false,
option_explanation TEXT,
sort_order INT

This supports:

any number of questions
any number of options
one correct answer now
multiple correct answers later
per-option explanations
AI diagnostics
clean analytics
easy imports from JSON/YAML

learning_signal_tags

CREATE TABLE learning_signal_tags (
    learning_signal_tag_id TEXT PRIMARY KEY,
    signal_name TEXT UNIQUE NOT NULL,
    description TEXT,
    category TEXT,
    severity_level TEXT,
    operational_domain TEXT,
    ai_guidance_enabled BOOLEAN DEFAULT true,
    spaced_recall_priority INT DEFAULT 1,
    active BOOLEAN DEFAULT true,
    created_at TIMESTAMP DEFAULT now()
);

Example Entries

signal_name	Purpose
verifier_ready	Learner understands verifier-grade evidence expectations
supplier_risk	Linked to supplier ESG risk mitigation
scope3_gap	Scope 3 reporting deficiency
weak_evidence_quality	Poor ESG evidence quality detected
materiality_confusion	Learner misunderstanding materiality concepts
disclosure_risk	Potential disclosure weakness
governance_gap	Governance-related learning deficiency
policy_missing	Missing policy understanding
taxonomy_alignment	EU taxonomy alignment learning need

Table Value

ZAYAZ telemetry can trigger: Academy interventions

Example:

DaVE detects:
- weak supplier evidence quality

→ emits:
learning_signal_tag = weak_evidence_quality

→ Academy injects:
- verifier evidence module
- supplier remediation guidance
- mini quiz
- refresher lesson

This is where: learning + telemetry + operational ESG intelligence merge together

learning_signal_relationships

Example:

parent_signal
child_signal
relationship_type

This allows:

scope3_gap
 └── supplier_data_gap
      └── transport_emissions_missing

That make AI reason hierarchically.

This becomes the foundation for:

remediation intelligence,
adaptive learning,
ESG workflow guidance,
and competency mapping.

12.3. Use JSON for import/export

Authoring format should look like this:

{
  "quiz_bank_id": "AC2605-d31211dd_m001_g001",
  "course_id": "AC2605-d31211dd",
  "course_name": "Procurement Sustainability & ESRS G1-2",
  "module_id": "m001",
  "module_name": "The CSRD Context and Why Procurement Matters",
  "quiz_group_id": "g001",
  "quiz_group_name": "Module Quiz Bank 1",
  "assessment_scope": "module",
  "difficulty_level": "foundation",
  "frameworks": ["CSRD", "ESRS"],
  "roles": ["procurement", "executive", "sustainability_lead"],
  "passing_score_percent": 70,
  "questions": [
    {
      "question_id": "AC2605-d31211dd_m001_g001_q001",
      "question_number": 1,
      "question_type": "single_choice",
      "difficulty": 2,
      "concept_tags": ["impact_materiality"],
      "misconception_tags": ["materiality_is_only_financial"],
      "learning_signal_tags": ["materiality_confusion"],
      "linked_content_id": "procurement_sustainability_esrs_g1_2_m001",
      "linked_anchor": "what-is-the-csrd",
      "question_text": "What is impact materiality?",
      "correct_answers": ["b"],
      "explanation": "Impact materiality concerns how the organization affects people and the environment.",
      "options": [
        {
          "option_key": "a",
          "option_text": "Only financial risks to the company",
          "is_correct": false,
          "option_explanation": "This describes financial materiality, not impact materiality."
        },
        {
          "option_key": "b",
          "option_text": "The organization’s impacts on people and environment",
          "is_correct": true,
          "option_explanation": "Correct. Impact materiality concerns external impacts caused by the organization."
        },
        {
          "option_key": "c",
          "option_text": "Only emissions from direct operations",
          "is_correct": false,
          "option_explanation": "Impact materiality includes broader sustainability impacts than direct emissions."
        }
      ]
    }
  ]
}

12.4. One course.json per course.

Example: /content/academy/courses/AC2605-d31211dd/course.json

It should describe the course shell:

{
  "course_id": "AC2605-d31211dd",
  "course_name": "Procurement Sustainability & ESRS G1-2",
  "slug": "procurement-sustainability-esrs-g1-2",
  "status": "draft",
  "difficulty_level": "foundation",
  "language": "en",
  "frameworks": ["CSRD", "ESRS"],
  "roles": ["procurement", "executive", "sustainability_lead"],
  "modules": [
    {
      "module_id": "m001",
      "module_name": "The CSRD Context and Why Procurement Matters",
      "mdx_path": "modules/m001-the-csrd-context.mdx",
      "module_quiz_bank": ["AC2605-d31211dd_m001_g001"]
    }
  ],
  "certification": {
    "enabled": true,
    "passing_score_percent": 70,
    "certificate_template_id": "eco_cert_default_v1"
  }
}

13. Certification System

Certificates include:

learner identity,
E-C-O™ Number,
course metadata,
completion score,
competency level,
completion date,
verification QR/link.

Future enhancements:

verifier-linked certificates,
blockchain validation,
public verification endpoints,
CPD tracking.

14. Telemetry & Learning Intelligence

PostHog telemetry tracks:

completion rates,
friction points,
repeated misconceptions,
dropout patterns,
weak competency areas,
search behavior,
spaced recall performance.

Purpose: continuous optimization of ESG learning effectiveness.

15. Long-Term Vision

EcoWorld Academy evolves into:

an ESG operational intelligence layer,
a structured sustainability knowledge graph,
an AI-guided ESG mentor system,
and a contextual ESG capability infrastructure integrated directly into ZAYAZ.

The Academy ultimately becomes: the cognitive layer of the ESG ecosystem.

GitHub Repo Request for Change (RFC)

Architecture & Learning Experience Specification v1.0​

1. Vision​

2. Strategic Positioning​

Brand Separation​

EcoWorld​

ZAYAZ​

3. Core Philosophy​

Precision Before Automation​

4. Educational Architecture​

Learning Progression Model​

5. Course Structure​

Course Hierarchy​

6. Content Components​

6.1 Video Lessons​

6.2 MDX Knowledge Lessons​

6.3 Case Studies​

6.4 Participant Workbook​

6.5 Companion Reference Book​

6.6 Facilitator Guide​

6.7 Supplement Tools​

7. Quiz & Assessment System​

7.1 Quiz Philosophy​

7.2 Quiz Structure​

8. AI Personalization Layer​

8.1 AI Diagnostic Feedback​

8.2 Role-Aware Learning​

8.3 ZAYAZ Contextual Integration​

9. Spaced Recall & Reinforcement​

Reinforcement Schedule​

10. Search & Knowledge Intelligence​

Ask EcoWorld Academy​

Search Layers​

Layer 1 — Keyword Search​

Layer 2 — Semantic Search​

Layer 3 — Knowledge Graph Search​

11. Knowledge Graph Architecture​

11.1. MDX Frontmatter Metadata Example​

11.2. Frontmatter Metadata Specification​

12. Quiz Database Architecture​

12.1. Core Question Metadata (examples)​

12.2. SQL Structure:​

12.3. Use JSON for import/export​

12.4. One course.json per course.​

13. Certification System​

14. Telemetry & Learning Intelligence​

15. Long-Term Vision​

Architecture & Learning Experience Specification v1.0

1. Vision

2. Strategic Positioning

Brand Separation

EcoWorld

ZAYAZ

3. Core Philosophy

Precision Before Automation

4. Educational Architecture

Learning Progression Model

5. Course Structure

Course Hierarchy

6. Content Components

6.1 Video Lessons

6.2 MDX Knowledge Lessons

6.3 Case Studies

6.4 Participant Workbook

6.5 Companion Reference Book

6.6 Facilitator Guide

6.7 Supplement Tools

7. Quiz & Assessment System

7.1 Quiz Philosophy

7.2 Quiz Structure

8. AI Personalization Layer

8.1 AI Diagnostic Feedback

8.2 Role-Aware Learning

8.3 ZAYAZ Contextual Integration

9. Spaced Recall & Reinforcement

Reinforcement Schedule

10. Search & Knowledge Intelligence

Ask EcoWorld Academy

Search Layers

Layer 1 — Keyword Search

Layer 2 — Semantic Search

Layer 3 — Knowledge Graph Search

11. Knowledge Graph Architecture

11.1. MDX Frontmatter Metadata Example

11.2. Frontmatter Metadata Specification

12. Quiz Database Architecture

12.1. Core Question Metadata (examples)

12.2. SQL Structure:

12.3. Use JSON for import/export

12.4. One course.json per course.

13. Certification System

14. Telemetry & Learning Intelligence

15. Long-Term Vision