AI Problem Evaluation Using Embedding Vectors
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Solution Overview
Problem
Conventional methods require thousands of students to solve new problems to identify their characteristics, leading to inefficiencies and low learning efficiency, especially when problems are updated frequently, and they cannot evaluate problem quality without solution result data.
Innovation Solution
A learning content evaluation apparatus and method that uses an AI model trained with a problem embedding vector, not reflecting solution result data, to predict the probability of a correct answer for added problems, allowing for the evaluation of problem qualities like difficulty and discrimination power using item response theory without actual user solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional IRT methods are used to evaluate problem quality, then measurement precision of problem characteristics is improved, but productivity deteriorates due to requiring thousands of students to solve problems
Solution Approach 1:
The patent uses embedding vectors as digital copies of problem characteristics, allowing the AI model to learn from represented problem features without requiring actual student responses. The embedding vector captures essential problem attributes in a compressed form that can be processed efficiently.
Solution Approach 2:
The patent replaces the mechanical system of collecting actual student responses with an AI-based system that processes embedding vectors. The neural network model substitutes the physical process of thousands of students solving problems with computational processing of vector representations.
2Adaptability or versatility
If problem content is updated frequently to maintain educational quality, then adaptability of learning content is improved, but loss of time increases due to repeated evaluation requirements
Solution Approach 1:
The patent performs preliminary evaluation by processing problem embedding vectors immediately when problems are added or updated, before actual student usage. The AI model pre-computes problem characteristics and quality metrics, so that when problems are deployed, their evaluation is already complete.
Solution Approach 2:
The embedding vector serves as a compact copy of problem content that can be processed rapidly by the AI model. This allows frequent problem updates to be evaluated quickly without requiring full re-evaluation through student responses.
3Productivity
If AI model is trained with embedding vectors without solution result data to predict correct answer probability, then productivity is improved by eliminating the need for user problem solving, but measurement precision may deteriorate without actual solution data
Solution Approach 1:
The patent introduces embedding vectors as an intermediary representation between problem content and the AI model. The embedding vector captures essential problem features and serves as a mediator that allows the model to infer correct answer probabilities without directly observing student responses.
Solution Approach 2:
The patent changes the input parameters of the AI model from actual student response data to embedding vector representations. By transforming the problem evaluation task into processing vector embeddings, the system achieves efficient prediction while maintaining reasonable accuracy through learned vector representations.
Data Source
AI summary
A learning content evaluation apparatus includes a problem information processing unit configured to generate a problem embedding vector on the basis of problem information included in pre-collected problem content; an artificial intelligence (AI) model training unit configured to generate AI learning information including a weight determined using a result of training an AI model on the basis of the problem embedding vector and a user embedding vector, in which solution result data of a user for the pre-collected problem content is reflected; and a correct answer probability prediction unit configured to calculate correct answer probability information about a probability of being answered correctly by the user for the added problem, on the basis of a problem embedding vector of the added problem content and the AI learning information.