A practical approach to improving the quality of education data:
- material preparation: Selected authoritative textbooks and curriculum standards as
Source2SynthInput source for the module - Complexity control: Adaptation to the target student population
complexityParameters (1-5 levels) to ensure that questions are of moderate difficulty - multi-hop verification: Setting up more than 3 layers of question chain validation logic to ensure the integrity of answer reasoning paths
- manual review: Establishing a closed loop of feedback for teacher engagement and continuous optimization of generative algorithms
In practice, the program can improve the accuracy of the generated data from 75% to 92%.It is recommended to use the system with the providedCurriculumLearningModules enable adaptive difficulty adjustment.
This answer comes from the articleCAMEL-AI: An Open Source Framework for Building Multi-Intelligent Collaborative SystemsThe
May not be reproduced without permission:AI productivity tools " How to optimize the quality of education quiz data generated by CAMEL-AI?
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