Our research is at the intersection of AI, Human-Computer Interaction (HCI), and Cognitive Science, with the overarching goal to support Human-AI collaboration via trustworthy AI artifacts that can understand relevant properties of their users (e.g., states, skills, needs) and personalize the interaction accordingly, in a manner that preserves transparency and user control. The AI component of this research investigates how to enable AI agents to infer relevant user properties from usually limited and noisy interaction information and make informed decisions on suitable personalization. The HCI component focuses on devising user-adaptive interaction that abides by usability principles fundamental for user acceptance (e.g., unobtrusiveness, transparency, predictability, and controllability). Finally, the Cognitive Science component provides insights into the cognitive processes underlying the target interactions and helps identify which user characteristics should be modeled for effective adaptation.
The Human-AI Interaction reading group is organized to discuss recent research papers in the related field. Click here to find more information.
- If you'd like to join our research group as a graduate student, please apply here
- Cristina Conati received the "Test of Time 2022" Award from the Educational Data Mining (EDM) Society for paper
- Rohit Murali presents a paper at LAK'23. Read here
- Cristina Conati receives the ACM Distinguished member honour (as of 2020), in recognition of significant contributions to the field of computing