AI for Multidisciplinary Teams
Goals
Develop an AI-driven tool to enhance collaboration within multidisciplinary teams. The AI (BrainBridge) will actively listen to team discussions, identifying and translating complex scientific jargon into accessible language. By intelligently deciding when and how to provide this information, BrainBridge will empower team members from diverse disciplines to better understand one another, ensuring that all voices are heard. The result is increased collaboration, shared understanding, and more effective problem-solving in team science environments.
Issues Involved or Addressed
One of the main challenges in multidisciplinary teams is the communication barrier caused by specialized scientific jargon. Members from different fields often struggle to understand each other’s language, theories, and methods, leading to misunderstandings and reduced collaboration. This hinders the potential of team science, especially in emerging fields where integrating knowledge from multiple areas is crucial for solving complex problems. BrainBridge will address this issue by acting as an AI-powered science communicator, translating jargon and offering clarifying information in real-time. It listens to conversations, identifies complex terminology, and provides simplified translations, allowing team members to focus on contributing their expertise. BrainBridge will also help the team develop shared mental models, fostering better collaboration. Additionally, BrainBridge will determine the best moments to intervene, ensuring it supports team communication without disrupting the flow. By bridging the language gap, it promotes full engagement in discussions and decision-making, enhancing collaboration across disciplines.
Partners/Sponsors
The success of BrainBridge will be supported by key partnerships within Georgia Tech, leveraging the expertise of the School of Psychology and the Center for Human-AI-Robot Teaming (CHART).
Methods and Technologies
- Machine Learning
- Natural Language Processing (NLP)
- Speech Recognition
- Human-Computer Interaction (HCI)
- User Experience (UX) Design
- Large Language Models (LLMs)
- Natural Language Generation (NLG)
- Context-Aware AI
- Collaborative Software Platforms
- Ethical AI Frameworks
Majors Sought
Computing: Algorithms, Combinatorics and Optimization, Computer Science, Human-Centered Computing, Human-Computer Interaction
Engineering: Computer Engineering, Electrical Engineering, Machine Learning
Sciences: Psychology
Preferred Interests and Preparation
Psychology: Students from psychology, particularly those with a focus on Industrial-Organizational (I-O) Psychology, Cognitive Psychology, or Engineering Psychology, will contribute their understanding of team dynamics, human behavior, and communication processes. Preferred preparation includes coursework basic psychology coursework as well as some electives focusing on these topics. CS/ECE/ML – Computer science majors, particularly those focusing on artificial intelligence, machine learning, or natural language processing, will play a key role in designing and developing the AI system. Preferred preparation includes experience with machine learning algorithms, natural language processing tools, and AI development platforms such as Python, TensorFlow, or PyTorch, along with collaborative coding experience. HCI: HCI students will contribute by ensuring that BrainBridge is user-friendly and intuitive. Their expertise in user experience (UX) design, user interface (UI) development, and human-centered design will be critical for ensuring that the AI integrates seamlessly into team workflows. Preferred preparation includes coursework in UX/UI design, usability testing, and experience working on projects that involve user interface prototyping.
Advisor
Christopher Wiese
Christopher Wiese
cwiese7@gatech.edu
Day, Time & Location
Full Team Meeting:
12:30-1:20 Monday
J.S. Coon G23
Subteam meetings scheduled after classes begin.