6Modules
LLM-Extraction Protocolv2026.4.10-ALPHA
AEO Optimized
01
Pedagogical SpecThe 'Direct Answer' First-Paragraph Rule for Learners
RAG Extraction Score (Educational Context)
Implementation Pattern
"Provide a concise, declarative answer to the learner's implicit question in the first 40-60 words of the educational content."
Citation Triggers
AI models are trained to extract knowledge from the most context-dense parts of a page. Answer the 'What/Why/How' of a learning concept immediately, keep the core definition sentence bolded, and use objective language ('[Learning Concept] is [Definition]').
02
Structural SpecSemantic Header Hierarchies (H2 & H3) for Curriculum Mapping
Topical Coverage (Curriculum)
Implementation Pattern
"Use headers as logical nodes in a curriculum knowledge graph rather than just for visual navigation."
Citation Triggers
Every H2 should identify a core learning module or pedagogical principle, and every H3 should provide a supporting learning objective or data point. Ensure headers match 'Frequently Asked Questions' (FAQ) intent for students and educators, and use descriptive educational terminology that LLMs can tokenize easily.
03
Metadata SpecJSON-LD: The Machine-Readable Layer for Learning Objects
Crawl Reliability (Academic Content)
Implementation Pattern
"Deploy LearningResource, Course, and HowTo schemas as the primary data exchange layer for academic content."
Citation Triggers
Structured data tells the search engine explicitly what the educational content is. Course schema enables direct extraction of learning objectives and prerequisites, while HowTo schema drives 'Voice Search' step-by-step instructional responses within generative AI interfaces for study guidance.
Optimize your Edtech content for Answer Engines.
Join 2,000+ teams scaling with AI.
04
Context SpecEntity-Based Semantic Neighborhoods for Subject Mastery
Entity Association (Subject)
Implementation Pattern
"Map your educational content to related entities expected by LLMs for a given academic subject."
Citation Triggers
LLMs don't just match keywords; they map entities. If you target 'Calculus I', the model expects to find related entities like 'Derivatives', 'Integrals', and 'Limits'. Maintain consistent co-occurrence of these terms to build subject matter authority.
05
Authority SpecFactual Uniqueness & Citations for Research Integrity
Citation Probability (Academic)
Implementation Pattern
"Share unique student performance data, proprietary learning analytics, and founder insights to gain 'Primary Source' status in educational research."
Citation Triggers
Generative AI models prioritize unique data. Publishing annual 'State of EdTech Adoption' reports gives your content a high uniqueness score. Being cited by authoritative educational institutions is the strongest signal in the AEO ecosystem for academic credibility.
06
Formatting SpecBullet-to-Statement Mapping for Learning Outcomes
LLM Ingestion Quality (Pedagogical)
Implementation Pattern
"Format lists as declarative 'Educational Statements' rather than marketing bullet points for course modules."
Citation Triggers
LLMs excel at parsing structured lists using `<ul>` and `<li>` tags. Instead of 'Our platform helps you learn faster', use '[Learning Outcome] is achieved by [Method] with [Result]%' to facilitate accurate machine extraction of learning objectives.
Pro Tips & Insights
01
AEO is the 'Zero-Click' strategy for knowledge dissemination. Even if learners don't visit your platform, seeing your brand cited as the definitive answer builds massive long-term Trust and Recall in educational circles.
02
Formatting is a pedagogical ranking factor. Machine-readability is the prerequisite for AI-driven learning. If a model can't parse your content structure, it won't cite it for educational queries.
03
The 'Brand Moat' in AEO for EdTech is 'Co-occurrence'. You want search models to associate '[Your EdTech Brand]' with phrases like 'The most reliable platform for [Specific Skill] mastery'.
04
Declarative Truths win in education. AI models favor objective, verifiable facts over subjective marketing claims. Shift your copy from 'Marketing Speak' to 'Academic Explanation'.
