Many of those of us who work in higher ed have been thinking about ChatGPT since OpenAI dropped free access to it at the end of November. The fancy new chatbot which can generate essays, responses to qualitative quiz questions, and discussion board prompts has everyone thinking about academic integrity and “cheating”. The tech has been around for several years but offering access for free, right before finals, has caused quite the stir in higher education.
Something I hear a lot of people talk about, but which I feel is still not getting enough attention, is the question about why this tech is free. It is not much of a question because it seems everyone is aware that the tool has been given for free to the public so that massive amounts of people can help to train it.
So, like most of these kind of things, it is not really free. Maybe we are having fun playing with it but you are exchanging your time, creativity in writing questions/prompts, and your data in exchange for access. You need to create an account which needs to be tied to an email and I also believe a phone number. At the bottom of the ChatGPT input screen it clearly reads “ChatGPT Dec 15 Version. Free Research Preview. Our goal is to make AI systems more natural and safe to interact with. Your feedback will help us improve.” But improve for who and to what end?
Most of the folks who I have heard talk about this hint at how it is being trained by public labor for free, on public data obtained for free, so that eventually it will be used to create corporate products which will likely take away jobs and make billions for the creators. But after throwing this fact out there nonchalantly and often with a tone insinuating that this is a no brainer, they continue to move on and talk about how they used it to generate a rap in the style of Jay-Z, ask it questions about philosophy, or try to get it to mimic a student responses so that they can see if they (or their colleagues) could be fooled by it. I realize I’m about to be guilty of doing the same thing here – perhaps I just point this out to try to redeem some semblance of integrity. This work continues to put me in paradox.
OpenAI seems more than aware of the potential economic impacts of all of this and they have a research agenda around it – but this gives me little comfort. I can’t help but think about my own position in instructional design/faculty development/academic technology.
“Instructional design” (ID) can live in lots of places in the university and the position takes on a different flavor depending on where it exists. You have a different job if you are an instructional designer in the teaching center vs the library vs IT vs HR. Not all of us work with faculty but there is even variation between those of us who do. Some IDs are content focused and they use skills like graphic design or videography to develop things. My work has never been very content creation heavy – though I do like to create content. Working in smaller schools with tight budgets I mostly consult with faculty and for many of us this consulting role is a big part of our work. I talk with faculty about their teaching and offer advice about what they can do better.
I talk with them… I offer advice… You see where I’m going here.
This made me wonder, what kind of instructional designer/faculty developer consultant ChatGPT would make and so I decided to have a very basic conversation with it posing as a faculty member in physics. I copied the transcript of the chat into this google doc and I’m sharing it publicly for reflection by others in the field.
As for my own reflection
I’ll say that the results of my chat are much like what I have seen in the disciplines. These are perfectly plausible responses that sound very human but they don’t go very deep and it is in that lack of depth where those of us who do this work will recognize the flaws.
The bot falls down when asked about discipline specific approaches and when asked for anything that could connect to what other instructors may have tried in the past. It glazes over specifics around EdTech and flat out gets it wrong sometimes (I think the directions it gives for dropping the lowest grades in Canvas sound more like directions for Moodle, personally). I’m not actually a physics professor so I didn’t/couldn’t ask it specifics about advice for teaching individual topics in physics. In my experience, it does do better when you ask it to narrow its scope; so asking more detailed questions could make a big difference.
Still, the results are very familiar to a lot of faculty development advice that I see. Be it on various blogs, websites, listservs or even what I sometimes hear come out people’s mouths – much of it is the same basic stuff over and over. Professors are busy and so giving them simple lists and easy answers for improvement is quite common and ChatGPT mimics these basics pretty well and includes attempts at big picture synthesis. It ends its advice to me about developing as teacher by saying “Remember, being a lifelong learner as an educator is an important part of staying current and effective in your teaching practice.”
It’s not surprising. ChatGPT is trained on huge data dumps of the internet (including Reddit, Wikipedia, and websites). I threw the phrase “5 things you can do to improve collaboration in your class” into google and got a return of 712,000,000 results of various listicle type pedagogy puff pieces. With so much of this stuff out there already maybe it doesn’t matter that a chatbot can regurgitate it too? But I have to wonder what it means for our work.
I’ve been struggling with a kind of imposter syndrome from this work for some time. I say a “kind of” imposter syndrome because I refuse to take all of the blame. I can’t shake the feeling that at least some of it comes from the work itself; that the nature of the work encourages it. So many of us are limited in our own opportunities to go deeper or to reflect in more meaningful ways. We are incentivised to create/repeat these easy answers/”best practices”. After the pandemic we have seen many of our professional development organizations raise prices of in person conferences and reject accessible virtual options. Simultaneously, professional development funds often have not increased from our institutions and don’t get me started about how frequently we are throttled in our attempts to teach directly ourselves.
Many of us rely on disclaimers and positioning ourselves in various ways to account for our lack of knowledge/experience in domain specific areas, with technology, or even with specifics of teaching. In the beginning of that chat, the bot even gave me its own disclaimer “As a language model, I don’t have personal experience in instructional design or teaching, but I can provide general information and suggestions based on best practices in the field.” So, some of this is just the nature of the work but it is depressing nonetheless.
No one really knows yet what ChatGPT means for higher ed and I’ve not seen much talk about what it means for EdTech/Instructional Design/Faculty Development. We are kind of in a wait and see and react when we can kind of moment. I guess I’m hopeful this will open up room for more thoughtful and creative work. But I worry that it will force us to ask some hard questions about what kind of work is meaningful and that will cause some casualties.
How I got here/More if you want it
If you were paying attention to this chatbot/large language model (LLM) conversation at all before Nov. 30th, or even if you dug a little deeper since, you likely heard about this paper by Bender and Gebru et. al. but if you haven’t and want a critical look at the dangers of this stuff (including the environmental impacts and perpetuating biases) this is what you should really be paying attention to. I also found this piece from Jill Walker Rettberg, really helpful in better understanding the underlying datasets that GPT-3 is trained on and reflections the culture they come from. The relationships and evolutions between ChatGPT, GPT-3 (as well as 1 and 2), InstructGPT and all that is quite confusing but this post (from the Walmart Global Tech Blog, of all places) helps a bit. For even more, Lee Skallerup Bessette has created a Zotero library collecting all things ChatGPT in higher ed.
In addition, I ended my last post (which was mostly a reflection current happenings with twitter) with a reflection on European Starlings. Yes starlings, the invasive bird species who are problematic but in that post I referred to how they are also “strange and wonderful for lots of reasons”. I had focused on their murmurations but another important facet of the starling’s disposition is its proclivity for mimicry – and of course got me thinking about things that can talk but not really understand.