Building the language foundation for research-informed educational AI.
Supporting multilingual classrooms through curriculum corpora, teacher practice corpora, institutional pilots, and AI systems designed for teachers. Learn how this infrastructure powers our ELLA-AI research platform.
Educational AI Needs Educational Language
Artificial intelligence is rapidly transforming education. Yet most AI systems are trained on general internet text rather than the language teachers use every day in classrooms.
Educational Language Infrastructure is Method Mastery’s long-term research initiative to build the language resources, AI systems, and institutional partnerships needed for educational AI that supports teaching rather than replacing it.
Our work begins with multilingual learners but is designed to expand across subjects, educational settings, and countries.
From corpus evidence to instructional decisions
The initiative connects two forms of evidence: the language students encounter in authentic curriculum materials and the teaching practices educators use to make that content accessible. Together, these inform the Curriculum Amplifier and ELLA-AI decision-support layer, which can then be tested through institutional pilots.
Why Educational Language Infrastructure Matters
Educational Language Infrastructure provides the foundation that allows research-informed AI to understand authentic curriculum, classroom practice, and multilingual learning. Educational AI is only as effective as the educational language that supports it.
Today’s large language models are remarkably capable, but they are not built on curriculum-specific language, classroom implementation practices, or differentiated instructional needs. Teachers therefore spend valuable time adapting generic AI outputs into materials that fit their students.
Our initiative addresses this gap by developing educational language infrastructure specifically designed for teaching and learning.
1. Curriculum Corpora
Purpose
Curriculum corpora provide the educational language foundation for research-informed AI systems.
Current
Our flagship resource is the NYC Urban ELL Curriculum Corpus, a growing collection of authentic middle school science texts developed specifically for multilingual learners.
- 36 NYC middle school science lessons
- Lexically profiled
- Language demands analyzed
- Differentiated instructional support generated
Rather than relying on general web content, we curate authentic curriculum materials, clean and standardize the text, analyze vocabulary and language demands, and organize the data for educational research and AI development.
Next
Future curriculum corpora will expand into:
- English Language Arts
- Mathematics
- Social Studies
- Adult English
- English for Specific Purposes (Healthcare, Hospitality, and Information Technology)
Each corpus is designed to become reusable educational language infrastructure rather than a one-time dataset.
2. Teacher Practice Corpora
Purpose
Teacher expertise is one of the most valuable yet underrepresented resources in educational AI. While curriculum corpora explain what is taught, Teacher Practice Corpora document how experienced educators teach it—including lesson objectives, instructional strategies, scaffolds, assessments, and reflective feedback.
Current
Method Mastery is building a growing collection of teacher-contributed implementation resources that help research-informed AI learn from authentic classroom practice.
Current collections include:
- New York City Middle School Science — classroom resources and instructional materials contributed by Science teacher Michigan Madourie.
- Hong Kong Polytechnic University — Business Communication teaching resources contributed by Professor Frankie Har.
Next
Future contributors from additional institutions will expand the corpus across subjects, educational contexts, and countries.
Teacher Practice Corpora form the bridge between curriculum knowledge and practical classroom decision support.
3. Research-Informed AI Systems
Purpose
Educational language infrastructure becomes valuable when it powers systems that help educators make better instructional decisions. Our research-informed AI systems are designed to support—not replace—teacher expertise.
Current
Current systems include:
- Corpus Cleaner — prepares curriculum texts for research by cleaning, organizing, and standardizing educational materials.
- ELLA-AI — generates differentiated instructional support using curriculum analysis, learner profiles, and teacher goals.
Next
Future systems will continue expanding teacher decision support through curriculum analysis, implementation guidance, instructional recommendations, and classroom feedback.
Each system grows from the same educational language infrastructure while addressing different stages of the instructional process.
4. Institutional Pilot Studies
Purpose
Educational innovation must be tested where teaching actually happens. Method Mastery collaborates with institutional partners to evaluate research-informed AI within authentic educational settings.
Current
Current and planned pilot studies include:
- New York City Middle School Science (USA)
- Hong Kong Polytechnic University
- Taiwan K–12 and higher education partners
- Amsterdam educational collaborations
- Additional pilot opportunities in Florida
Next
Future pilot studies will expand across educational settings, countries, and disciplines while generating evidence that continuously strengthens both the educational language infrastructure and the AI systems built upon it.
Every institutional partnership contributes to ongoing research, validation, and implementation.
5. Knowledge Mobilization
Purpose
Research creates knowledge, but meaningful impact comes from sharing, applying, and continuously improving that knowledge.
Current
Knowledge mobilization currently includes:
- Conferences
- Workshops
- Publications
- Professional Learning
Knowledge generated through curriculum corpora, teacher practice corpora, AI systems, and institutional pilot studies continuously informs future research and educational practice.
Next
As the Educational Language Infrastructure grows, knowledge mobilization will expand through new research partnerships, professional learning opportunities, institutional collaborations, and international dissemination.
Our goal is not simply to publish research, but to improve teaching and learning through an expanding community of educators and institutional partners. Learn more about the Method Mastery Research Philosophy.
Join the Educational Language Infrastructure Initiative
The Educational Language Infrastructure is designed to grow through collaboration.
Who We Partner With
- Schools
- Universities
- Researchers
- Teachers
- Ministries of Education
- Educational technology organizations
Partnership Opportunities
- Curriculum corpus development
- Teacher practice corpora
- Institutional pilot studies
- Research collaborations
- Professional learning
Educational AI should learn from education. Help us build the language infrastructure that makes it possible.