CAIRE Researchers Tingting Li and Peng He Join $1.32 Million NSF Project on Collaborative Argumentation with AI Agents 

Dr. Tingting Li and Dr. Peng He from the CAIRE Research Lab at Washington State University, as Co-Principal Investigators, have received a three-year, $1.32 million award from the U.S. National Science Foundation (NSF), together with Principal Investigator Dr. Andy Cavagnetto and Co-Principal Investigator Dr. Hanjo Hellmann. 

Andy Cavagnetto, Principal Investigator
Peng He, Co-Principal Investigator
Tingting Li, Co-Principal Investigator
Hanjo Hellmann, Co-Principal Investigator

Cavagnetto, Li, and He are faculty members in WSU’s College of Education, Sport, and Human Sciences (CESHS), while Hellmann is a faculty member in WSU’s School of Biological Sciences. By bringing together expertise in science education, biology, the learning sciences, and artificial intelligence, the interdisciplinary team will investigate how university students and generative AI agents interact and construct scientific knowledge together. 

Supported through NSF’s EDU Core Research (ECR) and IUSE: EDU programs, the project, “Collaborative Argumentation with AI Agents: Understanding Human–AI Interactions,” will run from September 2026 through August 2029. 

Rather than treating AI simply as a tool that provides answers, feedback, or assistance, the project examines AI as an active participant in collaborative scientific argumentation. AI agents may respond to students’ ideas, introduce alternative perspectives, and influence how claims, evidence, and explanations develop through interaction. 

Through this work, the team will investigate how students and AI agents respond to one another, coordinate their reasoning, negotiate claims and evidence, and jointly construct scientific knowledge. This focus closely reflects CAIRE’s commitment to advancing responsible and learning-centered uses of AI that support, rather than replace, students’ reasoning and autonomy. 

Ultimately, the project aims to develop a theoretical framework and evidence-based design principles for more responsible and learning-centered human–AI collaboration. The team will also develop a working platform that integrates AI agents with instructional modules for undergraduate biology. 

Learn more about the project on the NSF Award Search website