Rose Luckin is a Professor of Learner Centered Design at the UCL Knowledge Lab in London. She researches how educational technology is designed and how it is evaluated. Professor Luckin is particularly interested in using AI to show teachers and students how people learn and how learning is cognitively, socially and emotionally shaped. She is also the Director of EDUCATE, a hub for Ed-Tech startups in London. In 2017, Rose was named on the Seldon List as one of the 20 most influential people in Education.
She recently sat down with Iridescent CEO Tara Chklovski to discuss her work with education technology, the elements of a good problem, and her advice to staying motivated in the face of setbacks.
Tara Chklovski:Thank you so much for talking to me today. Tell me about the problems you work on and why you chose them.
Rose Luckin: My work is really about trying to help individual learners understand more about themselves and develop a more sophisticated understanding of where knowledge comes from, what evidence is and why they should believe something or not. And then, beyond understanding themselves in terms of their knowledge, also understanding themselves in terms of their emotions, social intelligence and awareness of their physicality in the world.
Most of what I do is trying to understand human intelligence and see how we can use artificial intelligence to help support our own intelligence. I find this increasingly involves talking to broad audiences to help people understand what AI is and what it’s good for.
That’s about half of my time. The other half of my time I spend working with startups and small and medium enterprises, some of whom are using AI to develop tools, techniques or methods that can support teaching and learning. I have a program called EDUCATE, which links startups and SMEs to researchers who are working in an area that’s relevant to them and to educators and learners for whom they are trying to develop — trying to raise the quality of the conversation around evidence and how we know if something works.
Tara Chklovski: Can you give an example of some of the systems that you’ve worked on?
Rose Luckin: A system that I think sums up the potential of AI was part of a European research project to develop learning analytics for collaborative problem solving. Through that project we developed a way of identifying collaborative problem solving behaviors from fine-grained actions – things like gaze, or hand movements – that could be tracked and then analyzed using AI. That system would provide information for teachers who were trying to support collaborative problem solving between students . For example, students who were better at collaborative problem solving would coordinate their gaze and hand movements, more often than students who were poor at collaborative problem solving. The next step is to use our understanding of the social element of collaborative problem solving to make the application even more useful.The underpinning framework of the research is to look at what can be automatically captured and used to help humans understand what is working (or not) for a particular group of collaborative learners. And that includes helping those learners to understand how they are developing in terms of their collaborative problem solving skills.
This is a nice example of how learning science and technology comes together.
Tara Chklovski: And this is online collaboration?
Rose Luckin: No, this is in-person.
Tara Chklovski: And so the camera is watching, is analyzing the footage?
Rose Luckin: Well, at the moment we are analyzing it, but we’re hoping in the next iteration for the analysis to be automated. We are doing multi-modal data capture and analysis, trying to surface the invisible events that occur when humans teach and learn together.
Tara Chklovski:We did something similar in partnership with a team of computer scientists at USC. They captured audio and video data of families engaged in hands-on STEM activities, and tried to see if they could automatically detect when families were disengaged. We took hours and hours of footage and it was actually very hard to tease out real patterns that were repeatable and identifiable.
Rose Luckin: It is! We used some existing research that looked at synchronicity and individual variability to see if when everybody is looking in the same direction, and then marrying that data with what you do with your hands. It’s not foolproof!
Tara Chklovski: But you have to start.
Rose Luckin: You have to start. And it’s also about informing this data with what we understand about teaching and learning. This combination can of knowledge, models and technologies can then help teachers and learners to be their best.
Tara Chklovski: Right! So I’ll ask you just two more questions. So one is, what advice would you give children and parents as they try to find a problem to solve using AI?
Rose Luckin: For me, AI is all about problem solving, and it’s all about understanding the problem well enough that you can specify a solution and then decide how you use AI to help you with that solution. It’s about the right blend of AI and HI – Human Intelligence. What I would say to parents and children and teachers is– be really curious, and demand of anybody who’s offering you AI that they explain to you how it works. Don’t be shy. Don’t be brushed off. Demand an explanation.
Tara Chklovski: That’s very good advice. Second question: What has worked for you when you encounter obstacles and demotivating experiences?
Rose Luckin: That’s such a good question. I am naturally an optimistic person, and that helps a lot. I’m very aware of the importance of working with other people. So everything I do is collaborative.
I remain motivated by working with amazing people, amazing academic colleagues, teachers, students and companies.
And then you have to follow and guard the positive energy. Listen to people who don’t agree with you, but guard against spending too much time with people who are just very negative. Know where you can have an impact, but try to keep moving. Being positive is so important for learners.
Tara Chklovski: Solving hard problems.
Rose Luckin: Exactly. And I really believe that even an individual person can make a huge difference. You just keep after it!