References

Sources behind the open CEFR.AI framework

Frameworks & Standards

Primary frameworks guiding our research methodology:

Global Scale of English (GSE)
Pearson Education Ltd. (2024). The Global Scale of English.
Framework adopted for granular language proficiency measurement, providing numerical alignment with CEFR levels.
Common European Framework of Reference (CEFR)
Council of Europe. (2001). Common European Framework of Reference for Languages: Learning, teaching, assessment.
Foundational framework for language proficiency classification.

Corpora & Datasets

Our research utilizes the following linguistic datasets:

British Council Learn English Corpus
British Council. (2024). Learn English Reading Resources.
learnenglish.britishcouncil.org/skills/reading
Primary source for calibrated ESL reading materials.

Technical Implementation

Core technologies and methodologies (current prototype + next-stage architecture):

Natural Language Processing
- Python Scientific Stack (NumPy, Pandas)
- Spacy NLP Framework
- Custom models for text and task difficulty estimation
Web Framework & Infrastructure
- Flask Framework (Python)
- RESTful API Architecture
- Containerized Deployment

Acknowledgments

CEFR.AI acknowledges the foundational work of researchers and institutions in language assessment. The project now prioritizes transparent, open methods that others can inspect and build on.

Special thanks to the open-source and academic communities supporting reproducible language-learning research.