Strand 1
Strand 1: Technical Foundations for Effective and Expansive Interactions with AI
To be an effective partner, an AI must first understand the room. Once the AI understands the environment, it must know how to act.
Strand 1 researchers focus on the technical foundations required for AI systems that can perceive, interpret, and productively support student collaborations in authentic classroom environments. The goal is to move from raw classroom signals toward actionable, interactive, and privacy-preserving models of student group activity, attention, understanding, and epistemic alignment. Two research themes guide this work: Multimodal Perception and Control Policies for Multimodal Interactive Agents. Together, these themes define the core architecture for Student-AI Teaming: AI Partners that can observe collaborations as they unfold, reason about what students are attending to and where they are misaligned, and provide support in ways that are helpful, timely, and educationally productive.
Communication in the classroom takes place through an interplay of speech, gesture and other nonverbal signals. In order for AI to reason about collaboration and learning in real-world classrooms, it must combine information across multiple modalities, aggregate it over time, and interpret it within the context of the underlying learning conversations grounded in the curriculum.
We are developing computational models that help facilitate effective student-AI collaborations. The AI will help support students’ learning and collaboration by interacting unobtrusively and naturalistically with learners and integrating seamlessly into teachers’ existing workflows and the curriculum.