Automated Question Validation via Collective Intelligence
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Solution Overview
Problem
Current knowledge validation methods using questions and answers are manual, slow, and biased towards specific individuals or groups, lacking uniformity and reliability due to limited expertise and manual effort.
Innovation Solution
A computer-implemented method combining machine learning algorithms with collective intelligence techniques to automate the generation, filtering, classification, and validation of questions and answers, utilizing two sources of user input and supervised learning for objective evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to generate and validate questions and answers, then expertise and knowledge accuracy can be ensured, but the process becomes slow and biased towards specific individuals or groups
Solution Approach 1:
The patent combines machine learning algorithms with collective intelligence methods to merge automated processing capabilities with human validation expertise. The system automatically generates questions and answers using ML, then validates them through a community of validators, achieving both speed and reliability simultaneously.
Solution Approach 2:
The patent introduces an intermediary validation layer where multiple human validators review and approve automatically generated questions and answers. This intermediary step ensures quality control while allowing the bulk of question generation to be automated, resolving the contradiction between automation speed and validation reliability.
2Measurement precision
If a small group of experts manually creates questions and answers, then knowledge accuracy is maintained, but the process is slow and lacks uniformity across different subjects
Solution Approach 1:
The patent creates a universal system that can generate and validate questions across multiple subjects and domains using the same ML algorithms and collective intelligence framework. This universal approach ensures uniformity in evaluation standards while dramatically reducing the time required for test development compared to manual expert creation.
Solution Approach 2:
The system performs preliminary automated generation of questions and answers using machine learning before human validation. This preliminary action handles the time-consuming generation phase automatically, allowing human validators to focus only on reviewing and approving the pre-generated content, thus reducing overall development time while maintaining uniformity.
3Reliability
If manual expert validation is used for questions and answers, then quality control is achieved, but the process remains biased towards the knowledge of specific individuals
Solution Approach 1:
The patent enables the system to serve itself by using machine learning to automatically generate questions and answers without relying on a specific group of experts. The collective intelligence of multiple validators then ensures quality control, eliminating bias towards any single individual's knowledge while maintaining validation accuracy.
Solution Approach 2:
The patent changes the parameter of validation from a small group of experts to a larger community of validators with diverse backgrounds. This parameter change increases knowledge diversity and reduces individual bias while maintaining or improving validation accuracy through the aggregation of multiple perspectives.
Data Source
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AI summary
Method for the evaluation of subjects knowledge using collective intelligence in combination with machine learning algorithms that comprises: • Obtain, filter and classify questions, with their corresponding answers, of a knowledge of a given subject by means of artificial intelligence, • Validate the questions and the answers generated, filtered and classified in the previous stage, using collective intelligence for the correct classification and ordering • Review the questions validated in the previous stage for their use or discard.