Research and practical efforts in artificial intelligence currently focus on machine learning — asking how we humans can teach machines to do tasks. But having the machines learn should only be the beginning. Instead, people can learn from and with machines through experiential learning. Sam Ransbotham at the Carroll School of Management at Boston College will discuss his study about using the context of chess to understand how people can learn with machines through four elements of experiential learning (concrete experience, active experimentation, reflective observation, and abstract conceptualization). We find evidence of learning from each element that, importantly, differs by a player’s skill level. Players are more likely to seek AI help when surprised, invested, or foresee benefit. While getting the machines to learn is essential, humans can also use these AI tools to learn ourselves, to learn from and with machines, allowing benefits from personalized learning at scale.
This talk is part of the Digital Seminar, a D^3 Assembly series that is open to faculty, doctoral students, and academic researchers.
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