Knowledge Subgraph for Learning Session Question Generation
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Solution Overview
Problem
Conventional knowledge sharing sessions often struggle to address the varying knowledge levels of participants, leading to incomplete understanding due to skipped topics, and existing methods for addressing these issues, such as pre-session questionnaires or live questioning, can be inefficient or disruptive.
Innovation Solution
A system that utilizes a processor to build a knowledge subgraph of a learning session, compare it to a knowledge base, generate relevant questions for incomplete topics, and identify suitable locations within the session to ask these questions, ensuring participants receive necessary information without disrupting the flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-session questionnaires or live questioning are used to address varying knowledge levels, then participant understanding can be improved, but session efficiency and flow are disrupted
Solution Approach 1:
The system performs preliminary analysis of participant knowledge levels and identifies incomplete topics before they become problems during the session. By pre-processing the learning content and comparing it against knowledge graphs, the system prepares targeted questions in advance, allowing seamless integration into the session flow without disruptive interruptions or需要提前 questionnaires
Solution Approach 2:
The system continuously monitors the learning session content and compares it against the knowledge base to identify gaps in real-time. This feedback mechanism generates context-aware questions that are inserted at appropriate moments, ensuring both comprehensive coverage and maintained session efficiency through automated rather than manual intervention
2Reliability
If tailored sessions are created for each participant's knowledge level, then individual understanding is improved, but device complexity and resource requirements increase
Solution Approach 1:
The system uses a universal knowledge graph that can serve multiple participants with different knowledge levels simultaneously. By representing domain knowledge in a structured graph format with entities, attributes, and relationships, the same knowledge base can be queried to generate appropriate questions for any participant regardless of their starting level, eliminating the need for separate customized sessions while maintaining individualized learning support
Solution Approach 2:
The system adjusts the complexity and depth of generated questions based on participant knowledge level parameters rather than creating entirely different session content. By modifying question parameters such as difficulty, detail level, and conceptual depth while using the same underlying knowledge graph, the system provides personalized learning experiences without the complexity of full session customization
3Loss of information
If questions are generated and asked during the learning session, then incomplete topics are addressed, but session flow may be disrupted
Solution Approach 1:
The system acts as an intermediary between the learning content and the participant, automatically generating and timing questions based on real-time analysis of the session content. This intermediary function identifies optimal insertion points for questions that minimize disruption to the natural flow while ensuring complete topic coverage, balancing information completeness with session continuity through automated timing and context-aware placement
Data Source
AI summary
One embodiment provides a method, including: receiving input of a learning session that is being conducted by an educator, being provided to at least one user, and being related to a subject; determining, using a knowledge base, that at least one topic relevant to the subject of the learning session is incomplete, wherein the determining comprises building a knowledge subgraph of the learning session and comparing the built knowledge subgraph to at least a portion of the knowledge base; generating at least one question to be asked of the educator relevant to the at least one incomplete topic; identifying, using at least one natural language text classifier model, a location within the learning session to ask the generated at least one question; and providing, to the educator, an output corresponding to the at least one question at the identified location within the learning session.