Bookmarked Oblongification of education (code acts in education)

Ishiguro’s notion of the “oblong professor” is useful because it helps to deflate all of the magical thinking that accompanies AI in education. It’s hard to get excited about an oblong.

Sure, AI might be useful for certain purposes, but a lot of the current promises could also lead to real problems that need serious consideration before activating autopedagogic tutors in classrooms. Currently, AI is being promoted to solve a huge range of complex issues in education.

But AI tutors are simplified models of the very complex, situated work of pedagogy. We shouldn’t expect so much from oblongs.

Oblongification of education by Ben Williamson

 


Ben Williamson questions the promises of AI tutors. Borrowing Kazuo Ishiguro’s use of “screen professors” and “oblongs”, he describes AI as the “Oblongification of education”.

For me, this touches on Dave Cormier’s point about the ‘left-overs’ after structured problem-solving.

Bookmarked Moving beyond ‘solving’ problems as meaningful learning- a conference #ShrugCon by dave dave (davecormier.com)

A conference about uncertainty which might also be about the left-overs after problem-solving.

Dave Cormier describes how, according to Herbert Simon, there are well-structured problems and the rest that is left over. Cormier explians why he prefers to focus on the “left overs”.

A well-structured problem almost never happens to me in real life. At work, as a parent, as a partner, as a citizen I am almost never in a position where I’m given a clear question that isn’t messy in some way, a process that I can follow, and a way for someone to say ‘yeah, you did that exactly right’. And when I am, I can mostly just use a GenAI tool to get there.

The things that are meaningful, to me, are about real life. They aren’t about chess, they aren’t about puzzles, they are about how each of us faces the uncertainty around us. With all these GenAI discussions swirling around I’m even more interested in how we learn when things are uncertain.

Moving beyond ‘solving’ problems as meaningful learning- a conference #ShrugCon by Dave Cormier

For me, this touches on Dan Meyer’s comments about ChatGPT-4o and mathematics:

This looks like success to many. To me it looks like someone has successfully diced an onion without understanding why we’re hosting the dinner party, what we hope our guests experience, or how we’re going to structure the evening.

We can focus students on larger ideas by asking other questions.

What is the question asking you to do?

What do you know about that?

What is special about this triangle?

What do you know about sine?

Source: ChatGPT-4o Will Be Great for Certain Math, Certain Thinking, and Certain Kids by Dan Meyer

I have been watching the Seal Team. One of the key phrases used by the team leader, Jason Hayes, is ‘work the problem’. This relates to going beyond intuition to focus on the situation at hand.

Leaders must “work the problem” through proper and thorough procedures. Specifically, they should:

  1. Define the problem
  2. Determine goals/objectives
  3. Generate an array of alternative solutions
  4. Evaluate the possible consequences of each solution
  5. Use this analysis to choose one or more courses of action
  6. Plan the implementation
  7. Implement with full commitment
  8. Adapt as needed based on incoming data

It is interesting to watch the show and think about the problems that can be broken down and those outside of the sphere of control. Makes me wonder about whether working the problem relates to the space at hand or the space created.