Transcription

Daniel Emmerson 00:01
Welcome to Foundational Impact, a podcast series that focuses on education and artificial intelligence from a nonprofit perspective. My name is Daniel Emmerson and I'm the Executive Director of Good Future Foundation, a nonprofit whose mission is to equip educators to confidently prepare all students, regardless of their background, to benefit from and succeed in an AI infused world.
Daniel Emmerson 00:28
Welcome everybody to Foundational Impact. It's an absolute pleasure and privilege to have Jane Mann with us here today. Jane is the Managing Director of the Partnership for Education and Education Director for International Education at Cambridge University Press and Assessment. Jane, thank you so much for being here. How are you doing today?
Jane Mann 00:49
I'm good. It's beautiful out there. It's such a sunny day. I'm not out in it yet, but I'm looking forward to it later.
Daniel Emmerson 00:55
Very, very glad to hear that. Also looking forward to experiencing some sunshine. But before we get to that point in our week, I'm delighted to be able to have you here to talk through multiple aspects of your work. I think you do. It seems so, so much in the partnerships and in the assessment space, but to sort of unpack a little bit of that through the lens of AI is our objective here. Before we get into that, though, could you tell us a little bit about what it means to be the Managing Director of the Partnership for Education and Education Director for International Education?
Jane Mann 01:33
I have two hats, unfortunately, these are figurative hats, not actual hats that I'll put on. So as Managing Director of the Partnership, I have the enormous privilege of leading the team in Cambridge that works directly to government or directly to donor agencies, international development organisations, on programmes of education transformation. So these will usually be either programs working with ministries of education on things that they want to change within their system, and that may be driven by many things, many disruptors, many opportunities, things that they want to tackle, but obviously all towards improving outcomes in some way.
Daniel Emmerson 02:14
Can we get an essence of. Because you work across so so many countries, more than 20 countries on a regular basis.
Jane Mann 02:21
Oh, yeah. So our portfolio at the moment is probably around 30 countries at the moment. I think we've worked with 74 governments and our programs vary in scale. So sometimes it will be really holistic, like we are going in and working with that government and the stakeholders on curriculum, assessment, teacher development, materials. There'll be a whole thing. Say for instance, in the Sultanate of Oman, we did maths and science from grade 1 to 12 with them, the whole thing, and it was amazing. Other times it will be a small policy intervention. So we want to make sure that the policy that we have around With AI regulation, for instance, we started working on how should great AI frameworks show up in policy. In Bhutan, at the moment, we're working with them on trying to get their grade 12 first of all and then grade 10 up to international standards. But typically it will be a real sort of under the bonnet, getting in there. We start with the what's going on, roadmap it, work out what they can do, and then we roll our sleeves up. And that's the really privileged part of getting involved in the implementation. We also do that work in education in emergencies contexts. So we'll work in fragile contexts. Like we work in the Cox's Bazaar refugee camp in Bangladesh with the Rohingya communities there. And that's around really basic, great stuff in education, like great formative assessment. How do you get that into those learning spaces? We work in Ukraine. In fact, today I've got 23 school leaders from Ukraine in Cambridge today. They've been here all week and I don't want them to go home because they've just been amazing, the energy that they've brought. So we also take that same work into these spaces. We've worked on programs of learning in Afghanistan. And to be honest, it's because every great education system needs the same ingredients. The way you deliver it might be incredibly changed by context. But fundamentally a coherent system that's working across curriculum, assessment, supported teachers, great school leaders, great materials that all needs to come together to deliver more. So that's Managing Director of the Partnership for Education,
Daniel Emmerson 04:39
Which is massive in itself, of course. And when you're working across so many different countries, let's take the example that you mentioned in Oman, a huge amount of cultural context will come into play, I guess, in each of these situations. How do you make sure that when you're looking at transformation, that the cultural aspects of the work that you're doing are comprehensive and fully integrated into what it is you're transforming?
Jane Mann 05:07
It's critical because not only are these national programs that need to look and feel local and representative of local identity, but also from a pedagogical perspective, children learn better in environments that are familiar and what they see in the materials needs to be set in a landscape that looks like theirs, unless you're deliberately trying to take them out of that. Just for everyday learning, so we have offices, we have a representation in 12 countries. We represent many, many languages. We work in pretty much any language. We're currently working on a qualification with the Mauritius Ministry of Creole Mauricio. I'm not yet fluent, but working on it. And the most important thing in terms of contextual relevance and proper implementation is local partners. We would not presume to be able to navigate these spaces from Cambridge. So we are always looking for really great, strong local partners who we can work with, who we can learn from to help deliver these programs and design the programs in a way that makes them feel really relevant.
Daniel Emmerson 06:13
We're going to move into some of the AI implications that you mentioned earlier on, but I think before we do that partnership is such a significant factor here. When you're working with those partners on the ground, how do you go about, first of all, finding the right ones to work with when it comes to, of course, due diligence, but also value alignment, credibility, and then sort of implementing that work and making sure it's sustainable in the long term?
Jane Mann 06:39
Values alignment is absolutely essential. I mean, it's a lot like dating, right? You need to know that you trust these people and that you can get along and that you probably make similar decisions even when the other one is not around. So the values and the principles come first because you need to be able to work with these partners in contexts that sometimes are really spiky. You know, ministry programs always come with really difficult deadlines. Quite often, in some of the contexts we're working in, it comes with complexity around where the work's being delivered. So you can't forge those relationships in the heat of the moment that has to already have happened. So the values alignment is really important. Also, the breadth of ingredients within any kind of reform or transformation program mean that we need to just really find those pockets of expertise. It might sometimes just be one small setup doing one small thing brilliantly, and that's exactly the bit that we need. But a lot of it is around. It's around trust, transparency, sharing values, both having the same aim, which is to support the ministry or whomever else it might be in getting to where they need to be.
Daniel Emmerson 07:57
How does that translate to those fragile communities that you mentioned, whether that's refugee camps or countries that are experiencing high levels of conflict?
Jane Mann 08:07
It becomes even more important than ever when we are delivering programs, particularly on the ground. And when I say we, I actually mean us and our partners, because in many of those places, we, Cambridge, can't be on the ground. There may be countries that we're unable to travel to for obvious reasons, and where the ability to deliver comes through a series of really carefully negotiated tiny little channels of possibility that can be seized upon in just the right way. So for that, you need partners who know the ground inside out. We also work a lot with UNICEF, with the Global Partnership for Education, with local and international NGOs who deliver. And usually in most contexts of education and emergencies, there will be a, already like a cluster on the ground, usually run by UNICEF, of all of the NGOs working at that time in that space. And it's to get coherence around delivery. So for us to be able to go through that cluster and make sure that we're not duplicating, we're not getting in the way, we're not somehow messing up something that was already in place, that's really important. It's so important for us to tread carefully in those spaces. And what we're bringing there is not the expertise in how you deliver an emergency program within a refugee camp. What we're bringing is what does the best possible education in that space look like? Because in those spaces, typically the children who are learning in them, the challenges they've had to overcome to get to that place already, they may be learning in a non permanent structure, a tent or something. There may be no space at home to continue that learning or indeed anything to take home to continue with. So you have to make sure that every moment that you have them, you're delivering something which is the most useful it can possibly be.
Daniel Emmerson 10:06
And that is going to vary substantially, I would have thought, when it comes to access to resources. What's available for bringing into a classroom? What young people are engaging with? How do you perhaps go about thinking through the role of technology, if at all, in these situations, particularly where there's a restriction on resources? Does it come front and center regardless of what you're doing? Is it there a tool, a mechanism, do you have to adapt from place to place?
Jane Mann 10:38
You definitely have to adapt. It is a tool. It's one of the tools that's available. Where it is available, it can be absolutely golden. But it does carry as, of course, you know, the risks of widening equity gaps. In some places, it carries actually the risk as well of greater vulnerability. So just having a device makes you more vulnerable because those things are valuable. But from a learning perspective, I mean, I’mI talking about the Ukrainian leaders we have in right now, their what they have learned in that country around hybrid learning, we need to all be watching because they have for the last four years been moving in and out of modes in some of those most affected areas of Ukraine. And without technology, without the ability to do that, for many of those children, learning would have stopped. So, and it's not just learning of course, it's the. It's the routine of school. It's the reassurance that in the morning you can wake up and switch on and you're going to see your friends and your teacher. And so in some places that has just been our literal lifeline.
Daniel Emmerson 11:43
And thinking about AI specifically, I'm wondering if that's played into the Ukrainian context. How frequently are you coming across AI as a challenge or an opportunity? What does that look like?
Jane Mann 11:58
It looks like both a challenge and an opportunity. I'll give you some examples of the ways in which we're working with governments in AI at the moment, because they vary, but they're also showing something quite interesting, I think. So we are working on things like what should the regulatory environment be around? Not kind of big regulatory, but within an education system, what are good policies to have around AI? So that's really encouraging. Lots of teacher development, which is really encouraging because when I first came into this line of work 150 years ago, it was traditional that you'd see a tender document come in and it would be asking for loads of stuff that was new. And then at the bottom there might be like a, oh, could you do a bit of teacher training as well? The whole thing should have been flipped. You start with the teachers, right? And we're really seeing that with AI. So we are delivering, along with another mutual friend, Professor Rose Luckin's team at EVR, we're delivering programs of AI education to teachers and school leaders. And in some contexts, actually the people that they've got together into the room may be from kindergarten to further ed, higher ed. So some of these elements are really common around what is an ethical, effective, relevant way to be using AI. So lots of teacher education. And we're starting now as well to see really useful ways to use AI in, for instance, mapping curricula. What does this one look like next to this one? How aligned is my curriculum with my textbook? How is my physics curriculum running at the same pace as my maths curriculum? Because if it's not, I'm going to get to the point where the physics doesn't make sense because I haven't got the maths yet. So very little. Please can you put some AI into your resources and much more. How can we work with this thing so that it's doing the heavy lifting? There's also, we're looking at things like great teacher development tools that. Not teacher development, teacher lesson planning tools that can be on a mobile device in places like Sub Saharan Africa. And there you need to be looking at issues of things like language, what language does it need to be delivered in? Are those languages that they're typically trained in? But yeah, it's more about how can AI make the role of teaching and the creation of the tool, the things that we need for teaching and learning, better, rather than put it into the thing that the student is interacting with. Now, that's just our experience. I know that others are focused very much on having AI in the product, but I feel quite reassured by that at the moment.
Daniel Emmerson 14:52
So thinking about that professional development in particular, I mean, we have the privilege as well, I suppose of working in different countries around the world and supporting schools in multiple contexts when it comes to AI and professional development. And the need varies substantially from place to place. We often find that the first thing that school leaders think they want is a list of tools that they can use for certain subjects. Where in fact it's the EVR approach, thinking about purpose first, that always triumphs in the end because once you start with that, everything else seems to follow. I'm wondering about your experiences at a transformation level when you get those proposals in, how much of it is, okay, we would like to start with teacher training, but it's training on these particular products or is it more focused on outcomes? Or is there a real sense of purpose there from the start?
Jane Mann 15:53
There is a real sense of purpose, but the purpose is not narrow. So it's not just we want teachers to be using more AI in the classroom in order to get better outcomes. I think there's much more of a sense at the moment that AI is such a nebulous thing. The way in which you could use it as a school leader or a teacher, you could simply use it in your classroom management work, you could use it for your budgeting. There are so many ways in which you could be using AI that really a lot of what we're asking for, a lot of what we're asking for is what is the evidence, how do we work with it, how do we bring our specific problems to it and solve those problems with it? And also I found this really interesting. We run a program of executive education for policymakers called the HP Cambridge EdTech Policy Fellowship. And we take these groups of policymakers with our partner HP and we take them through a five month program of learning from the evidence. It's kind of three pillared. We work with the evidence, they bring a project which they are, or a problem that they're trying to solve and we use that as their life space to learn through. But the one pillar which has been critical and which if I'm absolutely honest, I didn't necessarily think would be is leadership. Because nobody can claim to know everything about AI or ed tech. Everybody is in a bit of a kind of. Well, they're on a spectrum but most people are kind of trying to find their way. And what you really need to be able to do in that is to be able to say I understand what we need, I've done the work and I can see what might be able to help us with that. And now I'm going to lead us all through this change in a way that everybody can end up feeling effective, supported, enabled. And so to get those coalitions of change, particularly at policymaker level, are critical. So we're seeing a lot of the desire to learn more about the skills, things like leadership and not just the AI itself. And I think that is reflected in the classroom. You know, what we need to be doing to be teaching effectively about AI is to be teaching effectively about communication and self management collaboration. How to ask a great question, it's not simply about how does the AI work and which tools should you be prioritising for which tasks.
Daniel Emmerson 18:24
And you mentioned policy a couple of times, Jane, can you tell me about where some of the best policies that are out there are coming from and how those are implemented?
Jane Mann 18:34
We're seeing some really careful policy making coming from all over really. But so we've worked with some of the Gulf countries on this and you know, because they are quite often quite far ahead with the technology, I think they're seeing the need to make sure that implementation is thought through, is careful, is sustainable. So definitely there. I think that countries where, countries like Singapore, we work with Singapore, again we're seeing really thoughtful. It's another Rose-ism, isn't it, that you should think slow and act fast. We're seeing a lot of thinking slowly and acting quickly, which is good. Naturally a lot of the action is coming from those higher resourced contexts. But some of the most interesting conversations I've had around AI and policy and what to be careful about have not necessarily come from those contexts. So some of the countries that are involved in the EdTech Hub, AI Observatory, the minister from Sierra Leone, Conrad Saki, the way that he's thinking about what AI should and shouldn't be doing and how to make his education system really efficient so that it's used really carefully in the spaces where it can deliver the most value without. Yeah, without having to kind of widen equity gaps or make assumptions that just aren't realistic.
Daniel Emmerson 20:03
So how does that work then for you? Would he approach Cambridge and say, this is what I want to achieve this efficiency at a systemic level, and then you sort of work through that problem?
Jane Mann 20:16
So in some cases, and we found out this morning actually that we have just won an opportunity in a Gulf country to work on AI teacher education, which is wonderful. It's nice news for a Friday. And that was through a tender, through a public tender, but it was with a country that we've worked with extensively in the past. The thing about a tender is that you absolutely need to be as far away as possible from the creation of it, but you need to understand where it's coming from and that it's probably coming because you need that context in order to be able to come up with a proposition which will work. So sometimes it will be a tender, sometimes it will be a relationship that we already have with a country. I mean, we've been working with Singapore for a very, very long time. I think about 80 something years, I probably got that wrong, but a long time. And in that case, they are almost sort of pushing us to be, to innovate with them, which is wonderful. They know what they want. We are already their partners. So we are evolving to make sure that we can provide them what they need. In other instances, it will be more around a desire to do something within the education system which will transform it and an expectation that somewhere along the line that's going to involve AI because of a cost efficiency issue, because they want to embed more AI learning into their classrooms, because they know that's going to be really important or whatever else it might be. It exists on a whole spectrum. But the lovely thing about working for Cambridge is that we are long term partners. We are not transactional. The expectation is always that from day one of working with a new partner, it's like, oh yeah, we're going to be friends for a while. So we get to see these situations evolve and we get to work through all sorts of changes and disruptions and opportunities with our government partners. So you can usually see these things coming from a little way off.
Daniel Emmerson 22:26
Because of where we are, I suppose, collectively around the world on this ever evolving AI journey. When you come across some examples of what you, Cambridge, would consider as best practice, how open is, is learning from that opportunity to other ministries or other institutions. Are there opportunities to, to broaden that collaboration out or is it more isolated than that?
Jane Mann 22:56
Yeah, and that's one of the reasons we created the fellowship. We want to create this alumni network I think that we've now got something like 104 fellows who are currently impacting about 38 million students. And they are all connected and they are from. We're in our sixth cohort. We've worked with fellows from sub Saharan Africa, from Central Asia, from Europe, from the Americas, from, you name it, they're all over the place. And what's lovely is as we keep them together in a regional cohort as they learn, because we think it's really valuable to have conversations with those whose situations are not a million miles from yours. But once they graduate, we kind of release them into this wonderful family of fellows from around the world and then we work with them as an alumni network to use them. That sounds dreadful to use them, but to make sure that there is cross learning happening there. And we find these weird bilaterals that we didn't expect would happen between countries talking to each other. Or we're also working with UNICEF and GPE on a program of learning for some of their countries that they're working through with edtech. So the tech for ed and the learning pioneer countries, and we're finding ways to join up those two networks, to make networks of networks. So whether you are in Azerbaijan or Scotland or Sierra Leone or Dubai, you can find something that really interesting that's going on and think, okay, I'm not policy borrowing, but I can completely see things in that that would work for me or that looked disastrous. I'm not going anywhere near it. And we do have a failure session quite often where people will quite openly talk about decisions they took around EdTech that they wish they had not.
Daniel Emmerson 24:52
That's, I can imagine, incredibly helpful. But of black box learning and understanding what went wrong. When it comes to those insights then that emerge from the fellowship, how do folks access that? You know, you've got school leaders looking for examples of best practice.
Jane Mann 25:10
Yeah.
Daniel Emmerson 25:11
Where can that be found and what is the best way of finding it?
Jane Mann 25:15
So we, from the Cambridge international side, I haven't even talked about my Education Director hats. So with our international schools network, we have about, I think it's about 7 or 8,000 schools that are really following a kind of clear Cambridge pathway and another couple of thousand schools that are taking little bits of Cambridge. We are absolutely looking for more ways to be able to connect the learning between them. But we're finding that there are already similarities between groups that are useful and that where we think we can take learning from one into the other. So for instance, a big international schools group is a system. If you look at something like Nord Anglia, they have what, nearly 90 schools across 30 something countries. And you know, this is a system. So what we're learning around supporting government systems is actually really relevant for how we could work with the schools group to say, right, let's look at how we do centralised work around great policy, great research that we can feed back the insights from. But then let's look at how we then localise that so that it becomes deeply relevant, even though it might be in a context that's different from the one where it was generated. An international schools group is a lot like a multi academy trust. It's a system. So when we're looking at system transformation, there are common elements which we can learn lessons from across and that might be about leadership, great decision making, what does good policy look like, how do you make sure policy is implementable in the classroom? What's likely to go wrong at that level and get in the way? So there's definitely that. There's also just basic elements of what does a great teacher development program look like. Because teachers around the world, if you ever see them, international gatherings of teachers, there's this kind of connection that they will have instantly. So many of the things that you'll be training them in will be common no matter where they are. So there are learnings that way. When it comes to research and insights, we're really lucky not only to be of course sitting in the middle of an amazing university doing some amazing research on this, but we also have our own research team. So with my Education Director hat on, I work with our amazing research teams, our impact team, thought leadership, and they are generating some fascinating insights which we then will share with our fellows, with our schools, usually through newsletters or conferences and things. But we're looking now at increasing the frequency. I think it reminds me a bit of COVID when we moved to a living evidence model because we had to get in front of the virus. We're not going to get in front of AI, but we need to at least speed up our learnings from it and make those available in a way that doesn't need kind of peer review or something before you can actually read it. So we're looking at how we can get to a much pacier way to test, learn, share, go back, try it again, share. And that's something we're actively working on now. We just launched a Futures Lab to support with that as well.
Daniel Emmerson 28:30
Amazing stuff. Jane, I think just before we wrap up, I'd love to ask you, based on the work that you have done and that you've seen internationally. What is it that you would like to see happen in the UK when it comes to systemic change, perhaps as a consequence of AI and beyond?
Jane Mann 28:52
I would love to see, when it comes to AI, I think we. I'm going to take it back to actually the new statutory elements that are coming into the citizenship piece, at the moment, a primary. And actually the commitment that we've seen in the reports to putting critical thinking everywhere, because that's the thing that worries me, is that all the evidence already shows that AI is only effective if you use it in a really effective way. And unfortunately, if you don't use it in a really effective way, it's not just ineffective, it is damaging. So we have this. We have this risk and effective use of AI needs students who can think really well, who can manage their impulses, who can communicate clearly, and who are not afraid to try and fail. They need resilience, but they need to be able to identify learnings from that as well. And I hope that the commitment to things like improved media literacy or that understanding that we need more critical thinking spread across the piece. I hope that that's done really profoundly. So I don't think it's enough to be able to support a child in spotting fake news. I think we need to support students. That was just an example. I think we need to support students to understand, for a start, how is information now shared and why? What are the motivations, what are the drivers, incentives of doing this? What's your responsibility when it comes to receiving that information? How will you think critically about it? How will you know what happens if you try this? What would be the possible likelihood, likely effect of you doing this? So that's not learning about AI, that's learning how to think. And we have an opportunity now to make that really profound, really significant. But it will take a commitment. It will take a commitment to teacher development, to looking deeply into our curricula.
Daniel Emmerson 31:12
A phenomenal challenge. Jane, it's absolutely fascinating to listen to you, as always, and I'm sure our listeners will hugely appreciate all of the knowledge and insights that you've shared with us today. Thank you so much for being part of this episode. It's a. It's a joy speaking with you.
Jane Mann 31:30
Thank you, Daniel. It's always lovely to chat to you. I've really enjoyed it. That's it for this episode. Don't forget, the next episode is coming out soon, so make sure you click that option to follow or subscribe. It just means you won't miss it. But in the meantime, thank you for being here and we'll see you next time.

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