Orchestrating Interactions with AI
The foundational question for Strand 2 is: What advances in theories, interaction-paradigms, and frameworks are needed to orchestrate effective student and teacher interactions with AI partners? Our team science and human-computer interaction experts that make up Strand 2 aim to design the student-AI teaming models needed for an AI partner to scaffold 21st century learning practices. They are striving to better understand how students, AI, and teachers can collaborate effectively in both classrooms and remote learning contexts. Strand 2 also focuses on identifying socio-cognitive-affective states pertinent to student-AI team interactions, such as trust and joint attention, and ground them in neurophysiological signals. To accomplish this, Strand 2 has identified three research themes which are: Frameworks & Measures, Collaboration Processes & Orchestration, and UX Design & Multimodal Modeling.
Frameworks & Measures
This theme focuses on successfully identifying and measuring the basis of collaborative problem-solving skills in social, affective, and cognitive processes, and how to promote equitable and trusted interactions in team problem solving.
Collaboration Processes & Orchestration
This theme focuses on identifying verbal and non-verbal modes of collaborative engagement and identifying peer scaffolding moves, as well as understanding how peers support each other during collaborative learning.
UX Design & Multimodal Modeling
The iSAT lab is collecting data centered on collaboration to understand how data can be used to model collaborative behaviors and correspondingly implement these models as part of our future AI Partner.