Diagnostic Asset Generation for Targeted Competency Assessment
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Solution Overview
Problem
Existing information retrieval systems face challenges in determining user competency efficiently and accurately, leading to burdensome queries and ineffective diagnostic assets that do not cater to user proficiency levels.
Innovation Solution
A system utilizing part-of-speech tagging algorithms to identify specific words and phrases within diagnostic assets, generating targeted questions to assess user competency and provide personalized retrieval guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system queries the user with multiple questions to determine competency level, then the accuracy of competency assessment is improved, but the user burden and negative impact on user experience worsens
Solution Approach 1:
The system extracts only the most relevant parts of speech from diagnostic assets to form targeted competency questions, rather than using all available content. This extraction process identifies key nouns, verbs, and adjectives that are most indicative of user competency, allowing accurate assessment with fewer questions.
Solution Approach 2:
The system segments the diagnostic asset content by parts of speech and selects specific segments (words/phrases) to create focused competency questions. This segmentation allows the system to assess multiple competency dimensions using a limited number of targeted questions rather than comprehensive queries.
2Adaptability or versatility
If the system uses untargeted and repetitive diagnostic assets to assess user competency, then the coverage of competency areas is improved, but the tedium and user experience worsens
Solution Approach 1:
The system dynamically adapts the diagnostic asset selection based on user responses to previous questions. As users answer competency questions, the system adjusts which diagnostic assets are presented next, focusing on areas where competency is uncertain or needs verification. This dynamic adaptation maintains comprehensive coverage while avoiding repetitive questioning on already-assessed topics.
Solution Approach 2:
The system changes the parameters of diagnostic asset selection by filtering and weighting assets based on parts of speech relevance and user competency profiles. This parameter-based selection ensures diverse competency coverage while presenting only the most relevant and non-repetitive diagnostic content to users.
3Loss of information
If the diagnostic assets contain information and instructions that users do not comprehend, then the completeness of diagnostic content is improved, but the helpfulness and effectiveness worsens
Solution Approach 1:
The system applies local quality by tailoring the complexity and language of diagnostic assets to the specific user's competency level and comprehension ability. Based on user responses and competency assessments, the system selects diagnostic assets with appropriate vocabulary, detail level, and instructional complexity for each user, ensuring completeness of diagnostic information while maintaining comprehensibility.
Data Source
AI summary
Methods and systems are provided for information retrieval pathway guidance. In particular, the systems and methods provided apply artificial intelligence to novel tasks related to retrieving remotely accessible data such as detecting competency levels of users, generating personalized guidance for individual users based on the retrieval goals and initial competency level of a user, generating custom diagnostic assets for those goals based on current strengths and weaknesses, generating content for custom questions for those diagnostic assets, and dynamically tracking and updating the competency level of the user during the course.