Dynamic Event Tree Builder for Adaptive Scenario Training
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Solution Overview
Problem
Conventional computer-based training (CBT) is limited to pre-scripted, static content, which is ineffective for scenario-based training that requires flexibility and adaptability to rapidly evolving situations and varying learner paces.
Innovation Solution
A dynamic event tree builder system that allows trainers to customize and generate interactive event trees using a graphical user interface, incorporating machine learning to recommend and continuously train event nodes, enabling rapid reconfiguration and personalized training scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-scripted training materials are used, then the training content is stable and easy to manage, but the training cannot adapt to rapidly evolving situations or different learner paces
Solution Approach 1:
The training system transitions from static pre-scripted content to dynamic event trees where nodes and sequences can be modified in real-time. Trainers can add, remove, or reconfigure event nodes during training sessions to adapt to evolving scenarios and individual learner needs, making the system dynamically responsive rather than fixed.
Solution Approach 2:
The training content is divided into discrete event nodes that can be independently selected, customized, and reorganized. Each event node represents a specific training element that can be manipulated separately, allowing trainers to build customized training paths by selecting and sequencing individual nodes rather than modifying entire training modules.
2Productivity
If static training manuals are deployed, then the content remains consistent and controlled, but updates require manual intervention and the content cannot change dynamically
Solution Approach 1:
Event nodes are pre-configured with metadata and properties that enable automatic retrieval and assembly. The system prepares training components in advance with structured data formats, allowing rapid deployment and dynamic reconfiguration without requiring manual content creation or updates during training delivery.
Solution Approach 2:
The system uses template-based event nodes that can be replicated and reused across different training scenarios. Once an event node is created or customized, it can be copied and adapted for multiple uses, reducing the time required to create new training content and enabling rapid updates by modifying templates rather than individual instances.
3Adaptability or versatility
If pre-packaged training scenarios are used, then the training structure is simple to implement, but it cannot accommodate multiple resolutions or different learning paths
Solution Approach 1:
Event sequencers act as intermediaries between event nodes and the training delivery system. These sequencers manage the complex logic of multiple learning paths, conditions, and resolutions without requiring trainers to directly program the entire training flow. The sequencers handle the coordination and state management, making the system easier to operate while supporting complex adaptive scenarios.
Solution Approach 2:
The event tree structure allows dynamic creation and modification of learning paths during training execution. Trainers can add new event nodes, create alternative branches, or modify existing sequences on-the-fly, enabling multiple resolutions and personalized learning paths without requiring complete redesign of the training structure.
Data Source
AI summary
Embodiments disclosed herein describe systems, methods, and products to generate dynamic event trees that may be generated with ease and rapidly reconfigured. A computer may provide, e.g., through a web service, a user interface for a user (e.g., a trainer) to retrieve and customize event nodes from an event node database. The computer may also provide an event tree template where the user may simply drag and drop event nodes and use the dynamic event sequencers to generate hierarchical interconnections between the event nodes to generate a dynamic event tree. The computer may further execute a machine learning model that may recommend one or more event nodes. The computer may continuously train the machine learning model based upon the dynamic event tree and based upon whether the user has accepted the recommended event node.