Adaptive Test Generation Using Knowledge Graph Mastery Updates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional educational assessments are static and fail to adapt to individual student performance, leading to misalignment between students' knowledge levels and their assigned learning paths, resulting in ineffective learning experiences and resource wastage.
Innovation Solution
An adaptive test generation system utilizing programmatic control and guided/constrained AI engines to select educational standards based on real-time user responses, updating knowledge graphs, and determining the test's end based on predefined rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static assessment tests with fixed questions are used, then the assessment structure is simple and easy to administer, but the tests fail to accurately capture student knowledge levels and do not adapt to individual performance
Solution Approach 1:
The patent implements dynamic adaptive testing where the assessment system continuously adjusts question selection and difficulty based on real-time student performance. The test transitions from a static fixed-question format to a dynamic system that adapts its structure, content, and difficulty level according to the student's demonstrated knowledge, thereby improving measurement precision without requiring overly complex infrastructure.
Solution Approach 2:
The system incorporates real-time feedback mechanisms where student responses are immediately analyzed and used to adjust subsequent question selection. This feedback loop enables the assessment to accurately track knowledge levels by continuously adapting to student performance, resolving the contradiction between assessment accuracy and system complexity through intelligent algorithms.
2Ease of operation
If one-size-fits-all static assessments are used, then the administration process is simple and resource-efficient, but student placement is inaccurate and learning paths are misaligned
Solution Approach 1:
The adaptive testing system performs self-adjustment based on student responses, automatically selecting appropriate questions and adjusting difficulty levels without requiring manual intervention. This self-service capability maintains ease of administration while significantly improving placement accuracy, as the system autonomously adapts to each student's needs in real-time.
Solution Approach 2:
The system dynamically changes assessment parameters such as question difficulty, topic selection, and test length based on student performance. By automatically adjusting these parameters, the system achieves reliable student placement while keeping the administration process simple and resource-efficient.
3Adaptability or versatility
If traditional static tests are used, then the test structure is stable and predictable, but the tests cannot adapt to changing educational content and student needs
Solution Approach 1:
The system performs preliminary actions by pre-configuring question banks tagged with metadata such as difficulty levels, topics, and learning objectives. This preliminary preparation enables rapid adaptation during testing without requiring complex real-time generation, resolving the contradiction between adaptability and complexity through advance planning and structured data organization.
Solution Approach 2:
The adaptive testing system serves multiple functions: assessment, diagnostic evaluation, and learning path recommendation. By integrating these functions into a single platform that leverages student performance data across different contexts, the system achieves high adaptability while managing complexity through unified architecture and standardized processes.
Data Source
AI summary
An adaptive test generation system based on varying mastery levels of the user on educational standards to guide and constrain an AI engine in selecting educational standards for adaptive testing and updating user mastery levels based on real-time responses is disclosed. The method involves receiving a list of eligible standards, the user's knowledge graph, and dependencies between standards. A prompt is generated to direct AI engine in choosing the next educational standard for questioning. The AI engine selects a standard based on current knowledge graph and previous user responses, presents a question, and receives the user's answer. The knowledge graph is updated to reflect user's mastery levels of related standards. The AI engine then selects next standard, considering the updated knowledge graph and interdependencies among standards. This iterative process determines difficulty of subsequent questions and continues until the adaptive test concludes, based on the states of the educational standards involved.