Episode Transcript
[00:00:10] Speaker A: Hey, everyone, and welcome back to Talking ELT, the easiest place to learn about the big trends in language teaching. Today we're continuing our conversation about artificial intelligence with Higher reindeers and Ben Knight.
Now, last episode, we got a bit gloomy, and we talked about some of the dangers that AI might pose, and I don't want to leave you on that note. So today I'd like to explore some of the positive impacts that AI might have if we use it right, be they educational, personal, psychological.
[00:00:43] Speaker B: I guess that's the other part of the piece here, which we haven't really talked about, but it's the notion of well being, digital well being in particular, which, of course, in recent years has become more and more recognized as a real issue as we engage more with digital resources and devices, et cetera.
But I think here it's also important to discuss it in the context of how can we maintain our own as teachers as well as our learners, social, affective, emotional well being, and how can we as teachers really play a very positive role in that?
[00:01:29] Speaker C: Yeah, I think so, especially when we're talking about a world where I think it's partly what you were hinting at, that you get constant feedback on your performance. That's nice in some ways, but in other ways it can be a little bit overwhelming.
[00:01:44] Speaker B: For example, you're right.
[00:01:46] Speaker C: So how do you manage that? Well, you need a human, I think, to manage that. Definitely.
[00:01:52] Speaker B: Yeah, absolutely. There is an interesting concept which you may or may not have come across, which is called positive computing. And it was proposed by a couple of researchers from MIT.
Raphael Calvo and Dorian Peters. Later on, also work with DC from the famous Ryan DC self determination theory.
And the positive computing concept essentially looks to positive psychology and asks how technology can be used to actively proactively foster well being. And for those listening and watching who may not be familiar with the concept of positive psychology, it's essentially a way of looking at humans from a positive point of view and asking how well systems, people, resources can help humans to flourish. So rather than looking at psychology as fixing mental health problems, it's flipping that on its head and saying, how can we make you happier? How can we make you feel more satisfied with your life? And what Calvo and Peters did was to say, well, there's actually a lot of examples already, and there should be more of technology actively helping to develop that. And they labeled this positive computing, and they give some interesting examples of the use of, for example, augmented and virtual reality. And there's one which I'll briefly mention, which I really, really like. It's an AR app. It's freely available, so it's on your phone. You look through the camera lens on your phone, and what this app does is that it blurs your vision. And the reason why it does this is because it is designed to enable family members and friends of people who are suffering from an eye degenerative eye disease to experience what it's like if one of their loved ones is slowly losing their sight.
[00:03:52] Speaker D: Oh, wow.
[00:03:52] Speaker B: Right. I mean, it's very difficult for a person to imagine what it's like to lose your sight and how you start bumping into things and how everything looks weird. But with this app, what they found was they did some research, and it greatly enhanced people's empathy and understanding. They also applied it this is a different project at the University of Stanford with homeless people. So they had non homeless people use this app, walk around the town and experience what a homeless person would see. So, oh, that would be a good spot to maybe sleep tonight. Oh, I have to be careful, there might be police there, et cetera. And, well, they interviewed these people immediately afterwards and used other research instruments to identify that. Well, yes, this greatly enhanced, again, the level of kind of understanding that people had about the plight of homeless people. And then they came back six months later, and it turned out that those levels of empathy had remained equally high. So it had a long term positive impact. And those are really wonderful examples that potentially we can also think of in the area of education, of language education, where I think as humans, we would feel quite happy to make a contribution. Right.
[00:05:14] Speaker C: Yeah.
[00:05:15] Speaker D: I think we're moving away from AI as a ticking time bomb here, I think got some nice stuff coming.
[00:05:21] Speaker B: It's getting more.
[00:05:24] Speaker C: I mean, it's interesting because I've just been traveling. I've been thinking about how coming back a little bit to the English lingua, Franca, how your ability to communicate in English is partly about your awareness of what the person you're speaking to will understand, and therefore, you have to be able to adjust even if you speak fluently. But if you realize the person you're speaking to is not going to understand that, you have to adjust your language. So I'm trying to think of your example and think of it in a language learning context.
I'm sure there's a way that AI can help you become more aware of your communication as an English speaker. And I'm avoiding the word using native speaker because I think anybody who's very fluent can find that they're not adjusting their language to their interlock.
[00:06:29] Speaker D: Very nice.
[00:06:30] Speaker B: Yeah, that's a really wonderful idea. And it also relates to the position paper that I've just written for Oxford University Press on Teaching Refugees, because, for example, as a teacher, having some insight into the linguistic and social and other experiences that learners in your classroom might have had will obviously enable you to much better attune your delivery and your support, et cetera, to those learners needs.
[00:06:57] Speaker C: But I also think and it can relate to what you're saying is I think at the moment we're in a stage of here's some fantastic new technology, it's really great, and we're thinking, how can we use that technology in education?
But when we're talking about a year from now, two to three years from now, we have to be thinking, well, what are the problems we're actually trying to solve? And working backwards from that. At the moment, I think we're working forwards from the technology rather than kind of bearing in mind what's our problem.
[00:07:30] Speaker A: In the first place.
[00:07:31] Speaker B: True. But at the same time, we also need to keep the openness of mind to recognize that there will be both solutions to problems that we don't yet recognize exist, as well as problems that will emerge that we cannot yet anticipate, as well as potential solutions that we cannot yet imagine. Right.
[00:07:56] Speaker C: Okay. You see, when I'm listening to podcasts, sometimes people will say things like that, which is really I know, as are really interesting, but I'm thinking, well, give me an example.
I'm not really sure what you have.
[00:08:09] Speaker B: Well, how can you give an example of something you can't anticipate yet? But for example, I can think of something reasonably practical because it sort of exists, which is something that emerges from a good learning analytics system. Right. And there's not many of them because too often the human has been left out of the loop, and it's just an engineer who designs the system, as you said earlier.
[00:08:32] Speaker D: Right.
[00:08:33] Speaker B: But if you have a really well designed and well maintained, well monitored educational system which has a learning analytical or educational data mining engine, shall we say, running in the background, then it should not only be able to give you answers to questions that you have, but it might also provide you with insights that you yourself wouldn't have come up with. So going back to the earlier example of complex dynamic systems, the system might help you to realize that your learners I'm just making something up here. Your learner's level of motivation or engagement in class is low, not because of anything that you did or didn't do in your class. It's because the day before, they always take another class, and that teacher gives a tremendous amount of homework, and the kids are just bloody tired by the time they come to your class the next morning. Right.
[00:09:35] Speaker C: Yeah.
[00:09:35] Speaker B: It's a fairly mundane example, but you would never in a complex system like an educational context, you wouldn't recognize it. You wouldn't know. You don't even know that the students are taking someone else's class and what's happening there. And it's those hidden variables, those hidden connections that can well, often in reality, of course, have tremendous impact. And being able to visualize that or being alerted to it by a system, well, that's an example of an insight that you and I wouldn't have probably come up with ourselves.
[00:10:05] Speaker E: Yeah.
[00:10:05] Speaker C: And that's interesting. That reminds me, conversation with Professor Bart Rientes of Open university professor of learning analytics, who they're talking about the kind of learning analytics which is kind of popular at the moment, which is universities tracking engagement of students.
And it's quite nice because it gives them a predictive power of who's going to succeed, who's not going to succeed, what problems are. And he was talking about similar things that you identify drops of engagement or someone disengaged completely and then obviously you find that not obviously but often the cause of it has nothing to do with the course.
[00:10:49] Speaker B: Right.
[00:10:51] Speaker C: And it can be really difficult to as a teacher to know are you having to adjust your teaching or is there some other course? So as you say, the more we have sources of data which are giving us information about not just that course but other aspects of their program, the more likely we are to join the dots together, I suppose.
[00:11:15] Speaker B: Yeah, absolutely. I think we should do a regular podcast on AI.
[00:11:20] Speaker D: I think we should I think there's enough to talk about.
[00:11:22] Speaker B: Well, this is the interesting thing. It will very rapidly continue to develop. Teachers will continuously have new questions to ask about it, as for example, new plugins are developed for language education or for assessment or what have you. And there are so many interesting new technologies coming out as well that the implications of which be really interesting and I think very useful for teachers to hear about.
[00:11:47] Speaker D: Yeah, I mean just in the years time things might have exactly.
[00:11:51] Speaker B: So just have a regular a regular return.
[00:11:53] Speaker D: I like it, I like it. I mean, we'll get you up on screen.
[00:11:58] Speaker C: Yeah, particularly because at the moment that thing which is I think has triggered a lot of the change has been a natural language processing element of it, but it is based on fluent use of the language rather than learn a language. So there's a dimension which is not there yet, which is understanding how language competence develops and plugging that into the whole system, which we don't have that at the moment.
There's a perspective on different grades of education. So you can talk about what does someone at fifth grade or 10th grade know?
Chat GPT can can handle that, but it doesn't really, I don't think at the moment have that dimension of a a two Spanish speaker using English. What is that perspective? And I can see that changing in the next year for sure. Yes.
[00:13:00] Speaker B: Well, it requires somebody in our field to say, well, here is the inter language of a typical learner of this language at this level and please, agent AI, agent adapt yourself to this.
So for those of you listening, if you have ideas around this, please run with them because that's what we need.
We need the people who work in a given discipline and whether you're working as a nurse or as a teacher, that doesn't really matter all that much. But we need the human in the loop to say, this actually doesn't matter so much. Let's not focus on that. This is what we should really care about, right? Because without that voice, decisions will be made for us. They will be made by the algorithm, but we should be the algorithm, and we should bring the technology and the human side together in what we think is the best possible way, and then we remain in control.
[00:14:09] Speaker A: Absolutely.
[00:14:10] Speaker E: Thanks for listening to this episode of Talking ELT. That brings us to the end of our series on artificial intelligence. And I just wanted to say a really big thank you to Ben and Hayo for this fascinating conversation.
We'll be back in a few weeks with another series on the topic of independent self regulated learning. If you want to hear about that or about other important issues, make sure to like and subscribe drive while you're waiting. You can access loads of other great professional development resources on our website. Just follow the link in the description.
