Semantic Entity Graphs for Localized Teaching Content Adaptation

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Solution Overview

Problem

Educators face challenges in efficiently creating teaching media that is localized or targeted for various teaching environments and contexts, often requiring manual updates that are tedious and produce inconsistent results due to the need for in-depth knowledge of cultural and contextual factors.

Innovation Solution

A method and system that utilize an entity relationship database and semantic processing to automatically customize teaching content based on localized cultural and contextual factors, using an entity extraction model to identify entities and generate replacement entities based on semantic triples and graph data structures.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If educators manually create and format multiple teaching media for different teaching environments, then the content can be customized for specific contexts, but the process becomes tedious and time-consuming

Engineering Contradiction:

Improvecontent customization for different contextsVSAvoidtime required for manual content creation

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system pre-processes teaching content by extracting entities and generating alternative versions before the actual customization is needed. The entity extraction model identifies replaceable entities, and the system pre-generates multiple contextual versions of the content, allowing educators to simply select or make minimal adjustments rather than creating content from scratch for each context.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates copies of the original teaching content with automatically generated contextual variations. By generating alternative versions of the same content for different teaching environments and cultures, the system allows educators to work with pre-made copies rather than creating unique content for each context, significantly reducing the time required.

Inventive Principle:

Principle #26Copying

2Adaptability or versatility

If educators manually update content for different learning contexts, then the content can be adapted to specific cultures and locations, but the results become inconsistent and require in-depth knowledge of cultural factors

Engineering Contradiction:

Improvecultural and contextual adaptationVSAvoidconsistency of content adaptation

Core Design Contradiction:

Adaptability or versatilityVSManufacturing precision

Solution Approach 1:

The system replaces the manual mechanical process of content adaptation with an automated computational system. The entity extraction model and contextual version generator work algorithmically to identify entities and generate consistent adaptations across different cultures and contexts, eliminating the variability introduced by manual editing and ensuring uniform quality and consistency.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-service by automatically extracting entities, determining appropriate contextual versions, and generating adapted content without requiring extensive human expertise in cultural factors. The automated system handles the complex task of cultural adaptation, freeing educators from the need to possess in-depth knowledge of different cultures while maintaining consistent and accurate adaptations.

Inventive Principle:

Principle #25Self-service

3Adaptability or versatility

If educators manually create teaching media for various formats, then the content can be tailored to different teaching environments, but the process requires in-depth knowledge of cultural and contextual factors

Engineering Contradiction:

Improvetailoring content to teaching environmentsVSAvoidknowledge requirement for context customization

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system introduces an intermediary layer between the original content and the final adapted versions. The entity extraction model and contextual version generator act as intermediaries that automatically analyze the content, identify replaceable entities, and generate appropriate contextual variations. This intermediary system handles the complex knowledge requirements, allowing educators to focus on teaching rather than mastering cultural and contextual nuances.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12488701B2Systems and methods for semantic content customization

Publication Date: 2025.12.02 PENCIL LEARNING TECH INC

AI summary

The present disclosure provides systems and methods for modifying teaching content based on localized cultural and contextual factors. A system can maintain an entity relationship database including semantic triples. The system can identify entities from text content of an information resource. The system can generate, based on the semantic triples, an entity graph including nodes. Each node corresponds to a respective entity identified from the text content. The system can receive an indication to replace a first entity corresponding to a first node with a first replacement entity. The system can determine to replace a second entity corresponding to a second node with a second replacement entity based on the semantic triples and a connection between the first node and the second node. The system can generate processed text content based on the text content, the first replacement entity and the second replacement entity.