Personalized Curriculum Generation for Relevant Educational Media

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

The challenge of generating and curating educational media content that is relevant to users is complicated and time-consuming, making it difficult for content providers to attract user attention.

Innovation Solution

A computing system determines a user's understanding of educational topics and uses machine learning models to generate personalized educational media content tailored to the user, incorporating a personalized curriculum and interactive elements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If personalized educational content is generated manually for each user, then content relevance to user is improved, but time consumption and production cost increase

Engineering Contradiction:

Improvecontent relevanceVSAvoidcontent production time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system automatically generates personalized educational content by having the computing system determine user understanding levels, generate personalized curricula, and create customized media content without manual intervention for each user, thereby reducing production time while maintaining content relevance

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system uses machine learning models to dynamically adjust content parameters based on user understanding levels, generating personalized content by modifying existing educational material according to individual user needs rather than creating content from scratch

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If personalized educational content is generated manually for each user, then content relevance to user is improved, but production cost increases

Engineering Contradiction:

Improvecontent relevanceVSAvoidproduction cost

Core Design Contradiction:

Adaptability or versatilityVSEase of manufacture

Solution Approach 1:

The automated system performs content generation tasks that would otherwise require human experts, eliminating manual labor costs while maintaining personalized content quality through algorithmic analysis and generation

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system generates personalized content by adapting and customizing existing educational material through automated processes rather than creating entirely new content for each user, reducing the resource investment required

Inventive Principle:

Principle #26Copying

3Productivity

If automated content generation is implemented, then content production efficiency is improved, but personalization quality may deteriorate

Engineering Contradiction:

Improvecontent production efficiencyVSAvoidpersonalization quality

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The system determines the extent of user understanding as feedback input, uses this feedback to generate personalized curricula, and continuously adapts content based on user responses, ensuring high personalization quality through iterative improvement

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system replaces manual content creation with machine learning models that automatically analyze user understanding and generate personalized educational content, maintaining quality through algorithmic precision rather than human judgment

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250373913A1Computing System with Personalized Educational Content Generation Feature

Publication Date: 2025.12.04 ROKU INC

AI summary

In one aspect, an example method includes (i) determining, by a computing system, an extent of a user's understanding of one or more educational topics; (ii) using, by the computing system, at least the determined extent of the user's understanding of one or more educational topics to generate a personalized curriculum for the user; (iii) using, by the computing system, at least the generated personalized curriculum and one or more trained machine learning (ML) models, to generate personalized educational media content for the user; and (iv) performing, by the computing system, a set of operations to facilitate outputting for presentation via a user interface, the generated personalized educational media content for the user.