Automated Learning Session Synchronization Across Devices
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Solution Overview
Problem
Current intelligent tutoring systems face challenges in enabling ubiquitous, offline, and seamless social learning across multiple devices, including device compatibility and user interaction continuity, which hinders effective social learning and AI model enhancement.
Innovation Solution
A computer-implemented method for authenticating users across multiple devices and providing automated learning sessions, utilizing AI to analyze learning models, synchronize activities, and execute personalized learning tasks, enabling social learning through device sharing and proximity-based authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If intelligent tutoring systems are implemented across multiple devices, then accessibility and ubiquity are improved, but device compatibility and interaction continuity deteriorate
Solution Approach 1:
The patent implements a universal authentication mechanism that works across diverse device types (mobile phones, tablets, laptops, wearables) by using proximity-based detection and shared session identifiers. The system abstracts device-specific variations through a unified authentication protocol, allowing the intelligent tutoring system to function universally across multiple platforms without requiring device-specific implementations.
Solution Approach 2:
The patent introduces an intermediary authentication layer that mediates between users and the intelligent tutoring system. This intermediary component handles device compatibility issues by translating various device inputs into a standardized authentication format, thereby reducing system complexity while maintaining broad device support. The intermediary manages session continuity by coordinating authentication states across device transitions.
2Productivity
If social learning features are added to enhance collaboration, then learning effectiveness is improved, but user interaction complexity deteriorates
Solution Approach 1:
The patent merges individual learning sessions into unified social learning groups by combining multiple users' learning models and activities into a single coordinated session. The system automatically synchronizes learning progress, shares resources, and coordinates interactions among group members, thereby enhancing learning effectiveness through collaboration while managing interaction complexity through automated coordination mechanisms.
Solution Approach 2:
The patent implements feedback mechanisms that monitor user interactions within social learning groups and automatically adjust session parameters to optimize collaboration. The system provides real-time feedback on group performance, learning progress, and interaction patterns, enabling dynamic adaptation of social learning activities. This feedback loop enhances learning effectiveness while simplifying user interactions by automating coordination based on observed patterns.
3Adaptability or versatility
If offline mode is enabled for ubiquitous access, then accessibility is improved, but data synchronization and model updates deteriorate
Solution Approach 1:
The patent implements preliminary authentication and data caching mechanisms that prepare the system for offline operation in advance. When connectivity is available, the system pre-loads learning models, authentication tokens, and session data into local storage. This preliminary action enables the intelligent tutoring system to function fully offline while ensuring data synchronization reliability, as all necessary resources are already available locally before disconnection occurs.
Solution Approach 2:
The patent employs beforehand cushioning by creating redundant data storage and conflict-resolution protocols that protect against synchronization issues during offline periods. The system maintains local copies of critical data with version control and conflict-detection mechanisms, cushioning against potential data loss or corruption. When connectivity is restored, the pre-established synchronization protocols automatically resolve any conflicts, ensuring data integrity without requiring user intervention.
Data Source
AI summary
Methods, systems and computer program products for automated learning are provided herein. A computer-implemented method includes authenticating a plurality of users for an automated learning session, wherein the plurality of users correspond to at least one device, and providing the automated learning session for the plurality of users. Providing the automated learning session comprises analyzing a plurality of learning models corresponding to one or more of the plurality of users, determining, based on the analysis, one or more activities to be performed by the plurality of users during the automated learning session, and executing the one or more activities on at least one device.