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“But to get a computer to talk appropriately, we need an explicit theory of talking.”
-Patrick Suppes

Can we gen generative AI to explain this.?

“No amount of rules and facts can substitute for the know-how experts have gained from experience in tens of thousands of situations.”
-Hubert and Stuart Dreyfus

Some large language model may be challenging this reality.

Rules ignore context, which is why rule-based learning is of limited usefulness and why there are so many exceptions.

Users’ perceptions are what matter most in technology systems. Students’ perceptions are what matter most in educational systems.

What if we started using the term “mentoring” rather that “teaching” to describe our work? And then changed our practice to reflect the new term.

Assumptions so “obvious and natural” that we are not conscious of their effects are among the greatest limits to our actions.

Writing is often perceived as magic when first introduced into cultures.

The writer’s audience is always a fiction. -Walter Ong

is about forming relationships with and together. Any other chatter about standards, instruction, competencies, technology, etc. is meaningless unless those relationships are established.

Is driving the mechanical act of operating a vehicle or a social act of sharing the roads? (Our collective answer matters.)

No surprise. are less about relationships between and and more about systems than ever before. Outsiders are deciding the technologies that will “fix” education. The approach is doomed to failure.

Inert information is organized in no way, interpreted with no theory, places in no context. It really has no purpose.

“Does technology assure abundance?” - David Nye With information, the answer appears, “yes,” but the quality of that information is dubious.

Knowledge is ultimately not a fractioning but a unifying phenomenon...." -Walter Ong

“Does technology assure abundance?” - David Nye With information, the answer appears, “yes,” but the quality of that information is dubious.

Inert information is organized in no way, interpreted with no theory, places in no context. It really has no purpose.

Do we recognize the biases and assumptions of the technologies we use? The answer is unequivocally, “no.” Do they matter? Unequivocally, “yes.”

The most important and challenging problems are rarely solved with more data.

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