HUMAN–AI TEAMING · HUMAN FACTORS
Human potential. Intelligent collaboration.
Exploring how people and AI can learn, reason, and work together.
Explore our researchOUR MISSION
Better collaboration. More capable people.
We study how people and intelligent systems can work together effectively. Led by Nina Jiang at Louisiana State University, our research connects human factors and human–computer interaction with advances in generative AI and large language models. Our work spans domain-specific AI assistants, education and training, and multimodal human–robot interaction. Across these areas, we ask how AI can support human performance, understanding, and trust.
Meet the people behind the workRESEARCH AREAS
Questions that move us forward.
PEOPLE & INTELLIGENT SYSTEMS
Human–AI Teaming
Understanding performance, cognitive workload, and trust when people work with AI.
02KNOWLEDGE & PERSONALIZATION
Generative AI & LLMs
Developing AI assistance informed by domain knowledge and individual needs.
03LEARNING & HUMAN DEVELOPMENT
AI for Education & Training
Exploring how AI can support learning, training, and collaborative writing.
04LANGUAGE, PERCEPTION & ACTION
Human–Robot Interaction
Connecting human intent with robot execution through multimodal interaction.
FROM THE LAB
News & updates.
PUBLICATION
AI and early mathematics education
A new HCII 2026 paper examines design considerations for AI educational math games.
PUBLICATION
Bridging human intent and robot execution
H2R Link appears in the HCII 2026 proceedings.
BOOK CHAPTER
AI in collaborative writing
Nina Jiang and Vincent G. Duffy contribute a chapter to Advances in Human-AI Collaboration.
RESEARCH & COLLABORATION
Let’s explore what’s possible.
Interested in human–AI teaming, generative AI, or human–robot interaction? Contact Dr. Nina Jiang to discuss research interests, student research inquiries, and potential collaborations.