Adaptive Team Training Evaluation System Using Sensor Agents
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Solution Overview
Problem
Current adaptive learning technologies are unable to effectively monitor and evaluate complex interactions and contributions of multiple trainees in a group training environment, leading to inaccurate evaluations and inefficient training.
Innovation Solution
The Adaptive Team Training and Evaluation System (ATTEST) uses advanced language processing and monitoring techniques to track trainee communications and performance within a virtual training environment, allowing for real-time evaluation and adaptation of group training exercises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple subject matter experts observe and record trainee performance manually, then evaluation coverage is improved, but cost and subjectivity increase
Solution Approach 1:
The patent uses software agents to create virtual copies of evaluation capabilities that can monitor multiple trainees simultaneously. These agents replicate the observation function of human experts but without the associated costs and subjectivity, allowing comprehensive evaluation coverage with a single automated system
Solution Approach 2:
The evaluation system is designed as a universal platform that can assess multiple trainees across various training scenarios simultaneously. The software agents perform multiple functions including monitoring, recording, analyzing, and evaluating trainee actions, replacing the need for multiple specialized human observers
2Adaptability or versatility
If adaptive learning is implemented for individual students, then personalization is improved, but effectiveness in group training deteriorates
Solution Approach 1:
The system segments the evaluation of group training into individual trainee contributions while maintaining group context. Software agents track and attribute specific actions to individual trainees, allowing personalized assessment within the group environment and enabling both individual and collective performance measurement
Solution Approach 2:
The system implements continuous feedback loops that provide real-time information about individual trainee performance and group dynamics. This feedback enables dynamic adaptation of training exercises to meet individual needs while maintaining overall group effectiveness, combining the benefits of personalized learning with group training
3Measurement precision
If human experts manually evaluate group performance, then subjective bias is reduced, but cost and time consumption increase
Solution Approach 1:
The evaluation system operates autonomously without requiring human intervention for the actual assessment process. Software agents automatically monitor trainee actions, attribute them to individuals, evaluate performance against criteria, and generate reports, making the system self-sufficient and eliminating the time and cost associated with manual human evaluation
Solution Approach 2:
The patent replaces the mechanical system of human observation and recording with an automated software-based evaluation system. This substitution eliminates the subjectivity and time consumption of manual evaluation while maintaining or improving objectivity through consistent, rule-based assessment algorithms
Data Source
AI summary
An adaptive training system configured to implement a virtual training environment includes a control platform, which in turn includes a processor, and a non-transitory, computer readable storage medium having encoded thereon, machine instruction executable by the processor. The system further includes media devices in communication with the processor, with native sensors implemented on each of the media devices, each native sensor operating under control of a corresponding native sensor agent; and non-native sensors implemented outside the media devices, each non-native sensor operating under control of a corresponding non-native sensor agent. The machine instructions include instructions to deploy the non-native sensor agents, activate the native sensor agents, receive and process outputs from the native sensors and the non-native sensors, and adapt, in real-time, a training exercise by changing the difficulty, fidelity, and complexity of the training exercise.