Amy Ogan
_______________________________
Professor, Human-Computer Interaction Institute
School of Computer Science, Carnegie Mellon University
Director, Learning Science for Innovators
Courtesy Faculty, Carnegie Mellon University Africa
Embedded Files
About Me
I am the Director of the Learning Science for Innovators program and a Professor in Carnegie Mellon University’s Human-Computer Interaction Institute, with a courtesy appointment at CMU-Africa. My research sits at the intersection of human-computer interaction, learning science, and educational technology, with a focus on designing engaging and effective learning technologies that respond to learners’ social, cultural, and infrastructural contexts. I have conducted field research on the deployment of educational technology across five continents. I have been named a Jacobs Foundation CRISP Fellow, World Economic Forum Young Scientist, and Rising Star in EECS by MIT. I have received the McCandless Chair, the Moran Professorship in Learning Science, the 2024 SIGCHI Societal Impact Award, and numerous best paper awards. Before CMU, I was a visiting researcher at USC’s Institute for Creative Technologies and the Pontificia Universidad Católica de Chile. My research is supported by the Mastercard Foundation, National Science Foundation, Google, McDonnell Foundation, and Jacobs Foundation.
I am the General Chair, along with Anind Dey, for ACM CHI 2027.
Email: aeo@cs.cmu.edu
Office: Newell Simon Hall 3527
Admin: Reenie Kirby
My Research
My research asks how educational technologies can better support learning across the diverse social, cultural, and institutional contexts in which learning takes place. I design and study next-generation learning technologies that address both the cognitive and social dimensions of learning, with particular interests in culturally responsive educational technology, social and relational AI for learning, and technology-enabled teacher professional development. My work combines the development of new systems with empirical studies of how people actually use them in classrooms around the world, contributing both practical design principles and new insights into learning. Several of my lab's current research directions are described below.
Taking Educational Technology Worldwide
Most educational technologies and AI-based learning systems are designed and evaluated in WEIRD—Western, Educated, Industrialized, Rich, and Democratic—contexts. Yet these systems inevitably encode assumptions about how people communicate, teach, learn, and interact, and those assumptions may not hold across cultures or socioeconomic settings. My lab studies educational technologies in schools around the world, including Côte d’Ivoire, Tanzania, Mexico, Costa Rica, Brazil, the Philippines, and Belgium. Working with local researchers, educators, and school systems, we combine observation, interviews, learning assessments, and behavioral data to understand how technologies are perceived, adopted, and used. Our goal is to identify principles for designing educational technologies that respond to, rather than erase, cultural and socioeconomic diversity.
Building Relationships with Learning Technologies
Learning is fundamentally social. Virtual agents and other AI-driven learning technologies therefore need to do more than provide the right answer or feedback at the right time, they must also understand how to build productive relationships with learners. My lab studies the verbal and nonverbal behaviors through which teachers, peers, and students establish rapport, trust, and engagement, and translates these insights into computational models for learning technologies. Our goal is to create learning companions that can sustain meaningful social and pedagogical relationships over time.
Classroom Sensing for Teacher Professional Development
Decades of research show that active, participatory classrooms produce better learning outcomes than passive lectures, yet instructors often receive little timely feedback about what is actually happening in their classrooms. My lab develops professional-development tools that use live classroom sensing and AI to give instructors actionable feedback on teaching practice and student engagement. These systems draw on computer vision, machine learning, intelligent environments, and personal informatics while incorporating theories of teacher learning and professional development. Rather than automating the work of teaching, our goal is to use technology to help instructors reflect on and continuously improve their own practice.
Google Sites
Report abuse