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AI’s Impact Education, Training, and Learning: Potential and Limitations

Paulo Blikstein is Assistant Professor of Education and (by courtesy) Computer Science at Stanford University. He is also the Director of the Tranformative Learning Technologies Lab. Blikstein's research focuses on how new technologies can deeply transform the learning of science, technology, engineering, and mathematics. He creates and researches cutting-edge educational technologies, such as computer modeling, robotics, digital fabrication, and rapid prototyping, creating hands-on learning environments in which children learn STEM disciplines by building sophisticated projects and devices. In his talk, Paulo looks at these points...

  1. When we talk about machine learning or teaching machines, we're also altering our metaphor of human learning.
  2. The use of teaching machines has an 80-year history, but the results are not encouraging.
  3. Educational researchers mostly know why these attempts fail, but communication between educators, cognitive scientists, and technologists is faulty.
  4. Some areas of application of AI have shown promise in education, but their business models are still ill-defined.
  5. The biggest impact of AI in education might come from applications that do not even exist today, and will likely not come from the replacement of teachers or legacy education infrastructure.