AI Assistant for Hierarchical Data Processing in Educational Platforms
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional virtual learning platforms face significant lag times due to the disconnect between synchronous and asynchronous communication platforms, hindering effective learning and organizational management.
Innovation Solution
A computer-implemented AI assistant with an extraction module, transform module, and integration module, along with a manager module, is integrated into an educational platform to hierarchically process data, automate data processing, and provide seamless interaction between users and data sources, enhancing teaching and learning experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional virtual learning platforms use separate synchronous and asynchronous communication platforms, then real-time interaction is enabled, but significant lag times occur due to the disconnect between platforms
Solution Approach 1:
The patent combines multiple communication platforms (synchronous video conferencing and asynchronous messaging) into a unified virtual learning platform with a common data infrastructure. This allows real-time data exchange between different communication modes, eliminating lag times while maintaining the functional separation needed for different interaction types.
Solution Approach 2:
The patent introduces an AI assistant as an intermediary component that mediates between synchronous and asynchronous communication channels. The AI assistant processes and translates data between different communication platforms, enabling seamless information flow and reducing response delays without requiring complete platform integration.
2Loss of time
If manual data processing is used between communication platforms, then data accuracy is maintained, but significant time is lost in obtaining information
Solution Approach 1:
The patent implements self-service data processing through automated AI assistants that independently extract, transform, and load data between communication platforms without human intervention. The system automatically processes messages, schedules, and communications data, reducing information retrieval time while maintaining accuracy through structured data pipelines.
Solution Approach 2:
The patent performs preliminary data processing by pre-processing and structuring data as it is generated from synchronous and asynchronous communications. Data is transformed into usable formats in advance, so when information is needed, it is already prepared and available, eliminating delays associated with manual data processing.
3Loss of information
If comprehensive data from multiple sources is collected, then better insights are achieved, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing system into distinct functional modules: data extraction from multiple sources, data transformation to standardized formats, and data loading into usable structures. This segmentation allows comprehensive data collection from synchronous communications, asynchronous messages, and schedules while managing complexity through modular, independent processing stages.
Solution Approach 2:
The patent creates a universal data processing framework that handles multiple data types (video conference data, chat messages, schedule information) through a single integrated system. The AI assistant performs multiple functions including data extraction, transformation, validation, and integration, reducing overall system complexity despite comprehensive data collection.
Data Source
AI summary
Computer-implemented AI assistants, computer-implemented educational platforms, and methods of hierarchically processing data. Such an AI assistant includes an extraction module, a transform module, an integration module, and a manager module. The extraction module includes an AI data extraction agent that identifies and extracts relevant raw digital data from digital data sources. The transform module includes an AI data transformation agent that transforms the raw digital data into a usable form within the computer-implemented educational platform. The integration module includes an AI data integration agent that integrates the transformed data into a digital knowledge base. The manager module includes an AI agent that coordinates workflow of and dataflow between the extraction module, the transform module, and the integration module. The AI assistant may have a hierarchical structure that automates data processing from extraction to transformation to integration. The AI assistant can be implemented as part of a computer-implemented educational platform.