Arabic AI Text Detector
Developed for
Publishers, education platforms, compliance teams, and platforms fighting synthetic Arabic content
The challenge
- English detectors transfer poorly to Arabic morphology and dialects
- Mislabeling human writing as AI erodes trust
- Operators need measurable accuracy, not vibes
The Solution
An Arabic-first authenticity detector trained and evaluated on Arabic corpora (including AIRABIC-oriented evaluation) to separate AI-generated from human text with high reported accuracy.
Key capabilities include:
- ◇ Arabic-centric features and evaluation
- ◇ Reported ~98.4% AI-vs-human detection on target sets
- ◇ Fit for content moderation and academic integrity pipelines
Impact
Gives platforms a concrete signal for Arabic content authenticity instead of English-only detectors.
Organization benefits:
- Reduce false positives on human Arabic writing
- Support policy and education integrity programs
- Complement governance and safety tooling
Tags
Research Team
Meet Our PIs
Discover the principal investigator behind this project and the expertise that made it possible.
Hamza Salem
Head of PYXON Labs
Leads PYXON Labs research across Arabic AI, edge systems, governance, and applied products that ship into real environments.
// Open Vacancies
Join as a Scientist
Join our team working on Arabic AI Text Detector. Explore opportunities in machine learning, Arabic NLP, computer vision, edge systems, and applied AI research.
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Submit the same scientist application used on PYXON Labs — tell us about your CV and how you’d contribute to Arabic AI Text Detector.
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