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Human-Centered AI Lab

Research Lead: Dr. Ning Wang

Publications
Dr. Ning Wang
Visuals from the ARIN 561 game
ARIN561 2
VirtualHumanExhibit

Overview:

The Human-Centered AI Lab conducts research in the area of human-AI collaborative problem-solving, AI ethics and society, AI education and AI for education, trust between human and AI, and persuasive AI.

Projects: 

Current and previous projects include: 

  • Building Basic AI Competency for All: Centers around the idea of our trust in AI through education, the project researches AI education accessible for novice learners, who make up the majority of users and implementers of AI. The project develops an educational game, Becoming Fei (formerly known as “Age of AI”), that focuses on basic AI concepts in data science. 
  • AI behind Virtual Humans: An AI-driven interactive Virtual Human Exhibit to engage the public, particularly young children and their caregivers, in experiencing and learning about AI. Supported by the National Science Foundation under Grant 2116109, the exhibit is open to the public at the Lawrence Hall of Science from November 2023 to May 2025. 
  • Problem-solving with Probabilistic AI: The project develops an educational game, The 7th Patient, to guide high school students in learning probability and AI through problem-solving, such as making medical diagnosis. After a successful pilot with over 1000 students in Spring 2024, the full game will be released in Q2 2025. 
  • Explainable AI for Education: The project developed ARIN-561, an educational game to help high school students develop an understanding of AI. Supported by the National Science Foundation under Grant 1842385, the game won Student Choice Award at the I/ITSEC Serious Games Summit and was awarded Best Paper at HCI International in 2023.  
  • Transparency Communication for human-robot teams: This long range research initiative developed human-robot teaming testbeds and algorithms for automated explanation generation for probabilistic AI models and reinforcement learning. In collaboration with researchers at the Army Research Laboratory, the project investigated the impact of explainable AI on human-robot team performances.
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