How do we prepare teachers to teach AI to their students, teach their students to use AI effectively, and how to effectively use AI themselves.

I was having this discussion with a friend this morning and I'm sure many educators have been having this and similar conversations over the past year or so.

While the discussion around AI is different from the discussion of CS, after all an AI writing assistant can be used with zero understanding of AI and in any subject area but AI is a subset of CS and there are a number of parallels when thinking about preparing teachers in both cases. Unfortunately, we're still having the same discussions about CS so while we can look at what's been done so far, everything's still too new to know what works or not.

Should students learn about AI in all of their classes or in specific AI or CS focused classes? What about in a specific "Using AI" class? Can "all teachers" learn sufficient AI to teach their students or do we need specialists? Are the answers different for the early grades than from high school? Will teachers learn on their own? Should we provide professional development? What would that look like? Or, do we need to integrate all of this into our teacher preparation programs.

These are all issues we've been dealing with in CS education and we don't have any definitive answer.

I don't have any final answers but I though I'd share some of my current thoughts both on delivery and also on training content.

In terms of delivery, the two options are Professional Development (PD) or preservice coursework.

While PD can be very valuable, quality PD is extremely rare, at least if you listen to teachers. On the other hand, if you're in a state with minimal teach requirements or the powers that be can't convince the colleges and universities that prepare teachers to bring in AI (or CS for that matter), then you're left with PD.

A local department of education, even one as large as NYC can also roll out PD much more quickly and at a wide scale like what NYC has done in their CSforAll program without requiring any changes at the state level, in legislation or by including colleges or universities.

A good example of some good work being done here is what Nora Burkhauser and Nick Yates are doing in Maryland. I wrote a bit about it here. They run a 2 part PD series. It doesn't prepare teachers to use AI effectively as it's meant to educate them on what AI is but it looks to do a great job at that. Part 1 is a great introduction for non CS teachers of all grades and part 2 goes even further. Ideally there would be even more for CS teachers, as I wrote about in my earlier post, but that's a limitation of the PD model and they're doing all they can do under their current system. They're also doing more than so many others.

On the other hand, I've had an opportunity to really see the pros and cons of the PD offered in NYC for their CSforAll program - here PD is generally offered by a PD provide - that is, someone like code.org or the College Board.

I got to do a deep dive when I evaluated applications for the CS Certification programs at Hunter College. I reviewed hundreds of applications over a three year period. If I remember correctly over 100 each year and close to 200 multiple times. There was a small amount of overlap each year in applicants but not much.

Applicants needed rudimentary programming knowledge in any text based language. Write a program that used a loop, had variables, a conditional, a function, and used an array or list. That's it.

The vast majority of the applicants had NYC CSforAll training and many, if not most had multiple years of this PD.

In years one and two I could barely accept 20 per year. In the third year I was able to accept close to 50 but that was by additionally offering multiple bootcamp sessions.

Clearly, the PD model wasn't working. We had a large number of teachers having gone through multiple years of training by the big name players in the field yet far too many couldn't tell the difference between a for loop and a forklift.

Sometimes PD is the best you can do and often it's all you can do right now but it's not the long term answer.

If this doesn't convince you I'll add that we all know that the attrition rate for teachers is among the highest of all professions and every time a teacher leaves the profession after two or three years, all that effort is lost. A PD model at best is a never ending treadmill.

On the other hand, if you work AI into your preservice courses, you have more time to cover it, the students (teachers) have some accountability (which rarely exists in PD sessions as one basically becomes certified by just showing up), and teachers enter the profession with AI as part of their skill set. Of course, ongoing PD after that then becomes a bonus.

Of course the challenge then becomes convincing states to require it and educational institutions to offer it.

In my Hunter programs, there's certainly room for both AI as a CS subject and also how to leverage using AI but my program is really made for high school CS teachers. I've had elementary school teachers but it goes so far beyond what they need to know in terms of content and there's less focus on their needs. It's also made for CS specialists so we'll never get the history teacher for example.

That said, the topics and ethics CS classes both give platforms to learn about AI. Even in 2020 we had a unit in the topics class on neural nets and many in the ethics class on bias. Meanwhile the methods classes and curriculum development class give a platform for exploring and using the tools, although, again the focus in on a CS class.

I can see a number of places we could work AI, from a generalists point of view into a typical preservice program and, between you and me, there's enough cruft in most programs that nothing would be lost. Even if you do have to cut something, AI isn't going anywhere so methods classes, for example, really will have to deal with AI tools if they really want to prepare teachers.

In any case, the next question is what to teach the teachers. I can think of a number of activities, both on the computer and unplugged which would give teachers a basic understanding of some key AI concepts. While not AI, a Markov Chain text generator show how you can use statistics and probability to generate text. Now and LLM is much more complex and uses a neural net on the inside but an unplugged activity around this simple generator can go a long way in demystifying an LLM and once demystified, they can be used more safely and more effectively.

I've done a number of other proto-AI lessons like "Who Played Spiderman?" or my Caesar Cipher unit which can be converted into unplugged activities to demystify other aspects of AI.

There's also a good amount of low hanging fruit for other uses of AI like generating discussion topics or having groups of students using AI to generate or research content and then have them discuss or debate.

Another advantage of doing this in preservice is that you can spread the AI over many courses and you'll have the time to also discuss issues like bias and dangers like using an AI tutor at the expense of developing teamwork and interpersonal skills with your classmates. In A PD time is much more limited and while there's good work like the Maryland project I spoke of above, far too much is given by vendors who's bottom line is selling a product. On top of that, things like bias is really beyond AI - yes, AI exhibits all sort of bias but the root is societal bias and where an AI PD is unlikely to be able to sufficiently handle that, an entire masters program can at least make a dent.

So, those are my thoughts for now. They'll probably change and evolve and in any case, given my current role as "retired guy" I'm not sure when I'll be able to act on any of this, though I do have some thoughts.