From the November 13th 2017 Symposium “Innovation Ecosystems for AI-Based Education, Training and Learning” Paulo Blikstein, Assistant Professor, Stanford Graduate School of Education (and by courtesy) Computer Science st Stanford University discusses 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.

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