AI Framework Improves Communication Analysis in Team Training

Researchers have introduced an innovative artificial intelligence (AI) framework capable of understanding and categorizing communication between persons with unparalleled precision, marking a significant advancement in team training technology. This cutting-edge development aims to improve team collaboration and coordination by allowing training technology to appreciate teamwork dynamics fully.

Leading the Way to the Future of Team Training

“There is a great deal of interest in developing AI-powered training technologies that can understand teamwork dynamics and modify their training to foster improved collaboration among team members,” says Wookhee Min, co-author of the study and a research scientist at North Carolina State University. “However, previous AI architectures have struggled to accurately assess the content of what team members share when they communicate.”

“We’ve developed a new framework that significantly improves the ability of AI to analyze the communication between team members,” says Jay Pande, the paper’s first author and a Ph.D. student at NC State. “This represents a significant step forward in developing adaptive training technologies that facilitate effective team communication and collaboration.”

Unleashing the Potential of Advanced AI

The new AI framework is built on a sophisticated deep learning model trained on a big dataset of text-based communications. The Text-to-Text Transfer Transformer (T5) model was then adjusted using data acquired during squad-level training exercises undertaken by the United States Army.

“We modified the T5 model to use contextual features of the team—such as the speaker’s role to analyze team communication more accurately,” Min explains. “That context can be significant. For example, what a team leader says may need to be interpreted differently than what another team member says.”

Demonstrating superior performance

To evaluate the new framework’s performance, the researchers compared it to two earlier AI systems. The researchers specifically examined the capacity of all three AI technologies to grasp a squad of six soldiers’ speech during a training exercise.

The AI framework was entrusted with two tasks: classifying the type of discussion occurring and tracking the flow of information inside the squad. Determining the purpose of what was stated is organizing the interaction. Was someone, for example, requesting information, delivering information, or issuing a command? Following the flow of information refers to the manner in which information was communicated within the team. Was information, for example, transmitted up or down the chain of command?

“We discovered that the new framework performed significantly better than previous AI technologies,” adds Pande.

“One of the things that was particularly promising was that we trained our framework using data from one training mission but tested the model’s performance using data from a different training mission,” Min explains. “And the improvement in performance over previous AI models was noticeable even though we were testing the model under new conditions.”

Compact Power’s Promise

The researchers also mention that they were able to reach these results by employing a tiny version of the T5 model. This is significant since it implies they can perform analysis in fractions of a second without using a supercomputer.

“One of the next steps for this work is to investigate how far the new framework can be applied to a variety of other training scenarios,” Pande explains.

“We tested the new framework with training data transcribed from audio files into text by humans,” Min explains. “Another next step will be to integrate the framework with an AI model that transcribes audio data into text, so we can evaluate this technology’s ability to analyze team communication data in real-time.” This will likely entail increasing the framework’s capacity to handle sounds and errors while the AI transcribes audio data.

Looking Forward

The work, titled “Robust Team Communication Analytics with Transformer-Based Dialogue Modeling,” will be presented at the 24th International Conference on Artificial Intelligence in Education (AIED 2023) in Tokyo, Japan, on July 3-7. This prestigious event will showcase the research team’s extraordinary advances, opening the path for further innovation in the field of team training technologies.

Read the original article on Tech Xplore.

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