AI in Education

Teaching students to converse and coach is teaching them to ask good prompts.

Asking questions is more important than answering them.

So far, these researchers do not come from the world of education, confirming David Weintrop’s assertion that only engineering is asking these sorts of questions.

Don’t get caught up in trying to “catch” students using AI; instead change your methods. Whatever guard rails or sniffers you try to put up, people are clever and will find ways to get around it. Don’t create opportunities to game the system; instead create a whole new system. AI can’t smell its own nose.

Plucker’s insight into the sociocultural context in relation to creativity is significant. It suggests that a product must not only be novel, but also useful within a specific cultural context. This concept can also be applied to assessment, particularly personalized assessment. As humans, we inherently understand where a student is in their learning process, providing us with an ability to assess more naturally and accurately than a computer might. While AI can provide good feedback, human input can elevate it to great feedback. This may suggest that we should intentionally design systems which determine which assignments should be graded by humans and which by computers, thereby enabling more deliberate decision-making processes.

Here is a rebuttal to the idea that professors should not use AI to teach:

And from Leon Furze, an important point about student motivation::

“It’s good enough” and “it’s better than me” represent two of the biggest problems with GenAI in education, but they’re not necessarily caused by the technology itself. ChatGPT doesn’t create the conditions for students to decide when work is good enough. GenAI can’t subjectively produce art better than a human. These two problems seem to stem much more from systemic problems in education and students’ self-esteem, confidence and understanding of why we learn and create.