Encrypted Learning-Object Tagging for Private Competency Remapping
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Solution Overview
Problem
Existing automated tagging tools for educational content are inefficient, prone to errors, require manual reconciliation, expose proprietary data, and lack mechanisms for continuous model refinement and framework alignment, leading to latency and privacy issues.
Innovation Solution
A privacy-preserving machine-learning system that computes encrypted hashes of learning objects, applies them to a categorization engine for competency tagging and depth-of-knowledge classification, and incorporates feedback for model refinement, enabling scalable and automated remapping to updated frameworks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated tagging tools use keyword matching or static rule templates, then tagging can be performed automatically, but the tools are brittle when confronted with synonym drift, acronym variation, and discipline-specific terminology, resulting in inconsistent or incomplete metadata
Solution Approach 1:
The patent replaces traditional keyword-matching and rule-based systems with a machine learning model that processes learning object content. The model uses natural language understanding to identify competencies and depth of knowledge levels, overcoming the limitations of static rule templates and keyword matching while maintaining automated tagging capability.
2Productivity
If conventional tagging services require wholesale upload of raw assessment content to external servers, then processing can be performed, but proprietary question banks are exposed to privacy risks and intellectual-property leakage
Solution Approach 1:
The patent extracts only the essential features and characteristics of learning objects needed for competency tagging, rather than uploading complete raw content. The system processes content locally or uses encrypted representations, extracting only the metadata necessary for tagging while leaving proprietary content secure and inaccessible to external servers.
3Reliability
If the same learning object is re-processed each time it is encountered, then processing can be performed, but compute resources are consumed unnecessarily
Solution Approach 1:
The patent implements a caching mechanism that stores previously processed learning objects and their competency tags. When the same learning object is encountered again, the system retrieves the cached results instead of re-processing the content, significantly reducing compute resource consumption while ensuring consistent tagging across multiple encounters.
4Adaptability or versatility
If manual spreadsheet-based reconciliation is used for remapping course objectives to revised standards, then alignment can be performed, but the process is susceptible to versioning errors and audit gaps
Solution Approach 1:
The patent implements an automated remapping system that uses the machine learning model to align learning objects with updated competency frameworks. The system automatically detects changes in framework versions, re-processes affected learning objects, and maintains an audit trail of remapping operations, eliminating manual spreadsheet errors and versioning issues while providing continuous feedback for verification.
Data Source
AI summary
A computing system is disclosed for classifying rendered learning-object content and generating structured metadata representing competency and depth-of-knowledge attributes. The system detects rendered instructional content within a user interface and generates a cryptographic hash of the content, which is encrypted with session metadata to form a classification request. The request is transmitted to a remote categorization engine, where the content is processed using embedding models and inference classifiers to determine one or more competency labels, knowledge depth values, and confidence scores. The classification result is encrypted and returned to the client device for interface rendering. Classification records, including feedback interactions and associated metadata, are stored for use in model retraining.