Tagging an exam bank without reading every question twice
A platform that uses AI and machine learning to extract tags and keywords from examination questions — turning a manual cataloguing job into a reviewable, repeatable process.
- Sector
- Education
- Core
- NLP & machine learning
- Users
- Educators & administrators
- Loop
- Learns from feedback
Tagging examination questions by topic is the work that makes an assessment bank useful and analysable. It is also slow, inconsistent between people, and never finished — every new paper adds to it.
Without consistent tags, questions cannot be reliably searched, balanced across a syllabus, or analysed for coverage.
ExamPro applies natural language processing to extract relevant tags and keywords from questions automatically, and presents the results in a responsive interface where educators upload papers and review tagging in real time.
The AI/ML engine keeps learning from user interactions and feedback, so accuracy improves against the institution's own material rather than staying fixed at whatever the initial model produced.
- Intelligent tagging NLP extracts relevant tags and keywords from examination questions automatically.
- Real-time review Upload papers and see tagging results immediately, in a responsive web interface.
- Continuous learning The engine improves from user interactions and feedback over time.
- Content analysis Consistent tags make coverage and balance across a syllabus measurable.
A responsive UI for upload and review, an NLP/ML engine for extraction, and a feedback loop that returns reviewer corrections to the model.
Tagging becomes a review task rather than a transcription task, and stays consistent across everyone doing it.
Once questions carry reliable tags, the assessment bank can be analysed rather than just stored.
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