Intent-Driven Adaptive Learning Delivery via Metadata Graph
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Solution Overview
Problem
Existing learning systems provide static educational content that does not account for individual user learning preferences, talents, or intents, limiting their effectiveness in adaptive learning delivery.
Innovation Solution
An intent-driven adaptive learning delivery method that uses a metadata graph to filter and generate a k-partite graph based on user learning queries, creating a personalized learning curriculum that aligns with user talents and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static educational content is provided to all users, then system simplicity is maintained, but adaptability to individual user learning preferences and talents deteriorates
Solution Approach 1:
The patent segments the learning content delivery system into multiple components: a metadata graph representing the asset catalog, user profile data structures, and query processing modules. This segmentation allows the system to handle adaptability requirements through modular components rather than a monolithic complex system, resolving the contradiction between adaptability and system complexity.
Solution Approach 2:
The patent introduces a metadata graph as an intermediary structure between the asset catalog and user learning queries. This intermediary enables adaptive learning delivery by serving as a structured interface that connects user-specific learning queries to relevant educational assets, allowing adaptability without requiring direct complex interactions between all system components.
2Loss of information
If a comprehensive asset catalog is maintained for all learning topics, then content completeness is improved, but information retrieval efficiency deteriorates
Solution Approach 1:
The patent extracts and utilizes only the relevant portions of the comprehensive asset catalog needed for each specific user learning query. By filtering the metadata graph based on user intent and learning goals, the system retrieves only necessary content subsets, maintaining content completeness for the full catalog while significantly reducing retrieval time for individual learning paths.
Solution Approach 2:
The patent changes the parameter of content retrieval from returning all matching assets to returning a curated subset based on user profile parameters and learning intent. This parameter change allows the system to maintain the comprehensive asset catalog while optimizing retrieval efficiency by adjusting what constitutes a complete response for each user context.
3Ease of operation
If generic learning content is delivered to all users, then system operation simplicity is maintained, but learning effectiveness for individual users deteriorates
Solution Approach 1:
The patent implements dynamic content delivery where the learning curriculum is generated and adjusted based on user-specific parameters including learning intent, talent information, and profile data. This dynamic approach maintains ease of operation through automated generation while significantly improving learning effectiveness by adapting content to individual user needs rather than using static generic curricula.
Solution Approach 2:
The patent incorporates user profile information and learning intent as feedback mechanisms that guide content selection and curriculum generation. This feedback loop allows the system to automatically adjust content delivery based on user characteristics, maintaining operational simplicity through automation while improving learning effectiveness through personalized adaptation.
Data Source
AI summary
A method and system for intent-driven adaptive learning delivery. Intent-driven adaptive learning delivery may entail the conveyance of a learning curriculum surrounding a learning topic and a learning intent reflecting the reason for pursuing the learning topic. A learning curriculum, in turn, may generally refer to an ordered (or sequenced) manifest of learning materials and/or content that may progressively advance user proficiency in at least the learning topic. Existing systems or solutions offering learning curriculums today advance individual skills based on inferred educational goals for, and provide a static presentation of materials/content to, any given user. As an improvement over said existing systems/solutions, embodiments disclosed herein provide learning curriculums accounting for user learning preferences, the sought learning intent, and the user talent information.