CGI's Tim Cockle and our guest Professor Yonghong Peng discuss how AI enables personalised, student-centred learning, where students identify their educational needs and career aspirations.
Supported by lecturers and guided by AI, students can tailor their learning journeys, addressing skill gaps and preparing for future industry demands. This transformation also impacts university operations, using AI to improve efficiency and enhance experiences for staff and students while promoting responsible adoption to mitigate risks.
In research, AI is reshaping methodologies by optimising processes, designing experiments, and fostering interdisciplinary collaboration. Universities and industries must collaborate closely to align educational programmes with market needs, ensuring graduates are prepared for the evolving job landscape. Addressing challenges like cultural resistance and the rapid pace of AI adoption is essential. Ultimately, AI will redefine higher education, making learning more dynamic, adaptive, and inclusive.
Meet the speakers:
- Tim Cockle, Director Consulting Expert, CGI
- Professor Yonghong Peng, Professor of Artificial Intelligence and Deputy Dean for Research and Innovation, Anglia Ruskin University
- Transcript
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Tim Cockle: Welcome to our AI podcast. This is part of our AI for Industry series. Today, we're focusing on the higher education sector and the impact of AI. I'm Dr. Tim Cockle, a Visual Consultant at CGI. I'm joined today by Professor Yonghong Peng from Anglia Ruskin University, where he's Deputy Dean of Research and Innovation and Professor for Artificial Intelligence. Yonghong, thank you so much for joining today. I think this is going to be a really interesting topic, and I'm looking forward to hearing your views.
One of your key visions for the next generation of AI is human-AI collaboration. I wonder if we could begin by exploring this concept and what it entails and the implications in particular for higher education.
Yonghong: Good morning, thank you, Tim. That is a really important topic for the future of AI. We are developing AI responsibly and we want the AI to be leveraged by human for the humanity benefit. We want the human be able to work with AI, but also we want our AI to be able to enable people, support people for benefit. That is the concept between-- the future of the AI will be human and AI cooperativable systems, which is very important for our universities to realize how we can think about how this can change our research, innovations and educations as well.
Tim: In terms of higher education, I guess there's two aspects to this, isn't there? There's this broader vision that you're talking about in terms of the future of the workforce and the skills we need to develop there. I guess within higher education and as a student, there's probably an aspect of personalized learning. Is that an area that you see of interest as well?
Yonghong: The way of learning will be totally changed because the new technologies. AI is a very important part of technology innovations. The change people will not only to learn from the class, they will learn from their own resource and the resource is everywhere. How we can navigate, how we can guide our future learning, or how our future learner can be able to adapt themselves to learn what they are expecting to learn, that is the key concept will be the change.
Traditionally, we talk about personalized learning is the tutor. The lecturers design the content, but actually now is going to move to a student-centred. The student going to be identify what they would like to learn, what the job and/or skill market is required. Then the education or lectures will support them through the process and the journeys to support them to learn and make them be a success in this learning process.
Tim: I see. AI there is supporting the student explore what their learning needs are, and helping them plan. Is that the concept?
Yonghong: Exactly. This is one of the aspect. The second aspect is what the skill they are missing, what they need to learn for the next phase of this career. Every people are different. Every people will have their own personal value and also social value towards their ultimate goals. Those combined together will be human-centred. We need to design and develop AI which can help people to fulfil their personal view, personalized learning curve and journeys. This is very much transformative directions, will be happening for the future of higher educations powered by AI, powered by data-informed understanding of the need of the skills and the working force.
Tim: A key word you came up there was transformation and transformation by its nature, it's something that's easily said, isn't it, but it's actually really hard. This does feel like a real transformation. Like you say, something quite fundamentally different to the model. What challenges do you see in terms of the transformation and what we might need to do or what universities may need to do to try and mitigate and face up to those challenges?
Yonghong: That is a very important question. Transformations is easy to say, is very hard to implement. The transformation will be driven by the human, driven by the working force, driven by the talents of the humans. We cannot design what the future in these sectors would look like, but the working force were designed for that. The people who work in the sector will define what the future of the industry sector they're working in will look like. As education providers or learning by themselves, need to identify what role they can play in this process, in this new ecosystem of the future of the industries.
There's no simple solution for that, but this is why it's revolutionary change of the industries happening. AI is a key driver, but also come with many other sectors, human's cultures, mindset and ambitions as well.
Tim: Interesting. Just following on really with that, I guess, do you envisage some changes that will happen across AG that really shape the future of the university? I guess, in particular, just digging a little bit deeper and understanding what that means from a culture within the university that embraces responsible AI and upskilling, I suppose, as well of the employees within the university.
Yonghong: Exactly. AI will impact to every industry sectors. Higher education is one of the biggest impact happening. The higher-skill need sectors will be highly impacted by AI. Higher education is one of the higher-skilled sectors. What the higher education or university will happen is research is happening for over 80 years fully on AI, but education just started.
Most important, the working force, the professional service operationally will be changed because the financial situation is difficult so we are looking-- every university are looking into how AI can empower people to do their job, better qualities, and more efficiencies. Education is happening. I think many universities are looking into how we are going to embed AI into other disciplines, other sectors, which are looking into how AI is going to improve the student experience and also to improve the experabilities for a student to be able to work in the sector they will be working for.
Tim: Just culturally then, where do you think universities are in terms of culturally being ready to embrace responsible use to understand what that means? Do you think there's a clear path forward or do you see signs where the culture is jarring against the vision? People understand, potentially, the vision of what it means, but that doesn't quite translate into the culture that the organization has today and what they need to do to embrace that. Do you see a gap there or do you feel as if it's a challenge that's well understood?
Yonghong: Definitely there are some gaps. There are two types of view on AI in the higher education sector. If people understand the benefit of AI, people are more proactive to embrace AI. Another sector, another part will be a lot of resistance, worry about AI may take over the job. This can be a challenging part for the higher education, to look into how we can transform the working force. This takes time and this needs an education process. There needs to be a supporting, enabling programs to help people in the working force to be able to understand AI, to be able to use AI responsibly, to be able to understand there's risk behind AI and how to mitigate the risk.
On the other hand, there will be a lot of research happening, how this AI is impact the social aspect of within the working force, within the organizations, and what the leadership will be needed to accelerate or to enable these processes. There are three elements of this. One is how we can support people to leverage AI and to adapt themselves. Second one is how we can make sure the culture is going to be inclusive and the people will have opportunities to learn rather than just be behind. The third part is why we do this. This should have a good purpose to be able to benefit for our stakeholder, which is a student and which is our staff, so it is enabling and supporting.
Tim: I'm just going to just think a little bit about the future of work and the AI-driven world as well. What are the aspects that universities should strategically consider in their educational programmes to enhance student employability? What should we be looking at as higher education institutes to get the employability in the next wave of students through into th e world?
Yonghong: The speed of the change of the discipline is different. AI is very fast area and other science or other sectors may be slow. How we can support our students to be able to understand AI in other sectors, for example, if we are looking into life science or medical science, how we are going to be able to help our students to be able to use AI? These are big questions because they will need AI, but they will need the AI in a different way, comparing to the digital or IT sectors.
If we are looking at the software engineering sectors, there will be massive need faster, and so on. There are many different ways of supporting our students to improve their skill and also their employability. Number one is to provide opportunities to identify what they can learn. There are so many resources available on the internet, on the web, on the mobile apps. If they are going to spend all the time on that, they will have no way to learn that. That is our responsibility is to provide a guide for the student, what can be useful to change their skills.
Second point is we need to make sure the student has the mindset to understand the two sides of AI, which is benefit and risk. Without that, there will be a higher risk because the people's risk is more than the technology risk in my view. The third part is to work with the industries to bring the industry needs back to the campus, back to the classroom, to let students understand how AI is changing in their sectors so that they can realize what really benefit they could have. Then we define those learning curve or learning experience.
Tim: It strikes me that there's a real focus on learning in a fast-paced evolving environment there. Whilst part of education has always been, I guess, to teach people how to learn, here it feels even more important. As you say, there's a wider pool, isn't there, of resources. That toolset to be able to say, "How do I evaluate what's coming up? What do I need to learn?" To bed that into real-world practice as well, seems like you're saying it's going to be a really key skill for students to learn and evaluate as they go through.
Yonghong: Exactly. To learn how to learn is far more important to learn the skill themselves. I think in the future, it's not only the lecturers, the lecturers need to learn because it's fast areas. The students will need to learn, but they are learning different things. The lecturers is more to guide the students to learn around what they need. Then the student will need to be able to identify what they need in terms of career perspective and also what their current gaps they need to identify.They can use AI tools to identify. There will be more AI tools to help people to understand what their strengths, what their need for improvement, so there will be a lot of innovations behind this. It's a very exciting area to look into it.
Tim: To fold in risk as well, which I think was a really interesting point that you made before that balance of benefit and risk, which I guess to an extent, there's always pros and cons when we're looking at techniques, but with AI, like you say, there's an element of risk, there's an element of transparency, observability and folding that into what we need to learn and how we evaluate. It sounds fascinating. Just to move on to research and innovation as well, so within research in particular, how do you see AI accelerating research in the future?
Yonghong: AI is going to change the way we do science, we do research. AI is going to change how we can train our future researchers. I'll give you examples. The AI can help-- Traditionally, AI is helping us to analyse the data which we collected, but now AI can help us to design our experiment, how we can optimize our experiment process to be able to drive the result, drive the research more, more success, more chance to be successful.
AI is going to change how we design our research. The first part is how AI is going to change how we do the research, and then the second part is how we design our research. Third part, most powerfully, is AI is going to be working across disciplines to empower. Let's say we never think about topic A with topic C, but AI can tell me if we are going to integrate those two topics together, it will be powerful to lead me towards one direction, which is the other side of my current expertise. Give me more things to think, more ideas to explore, and so on. This is going to change the whole science of the future.
Tim: That's interesting, so often we can see AI as being, I guess, quite cold. It's a tool, it's a machine that sits between us and other people. I think what you're hinting towards there perhaps is, again, AI to be able to facilitate and collaborate, but also across groups as well. Is that one of the key things that you see then, being able to draw in and facilitate groups of people to come together from different disciplines?
Yonghong: AI will be our key partners for research. The key partners can be a brainstorm partner. I have ideas, and AI can have ideas, and we come together with good ideas. This is the first part. Second part, AI can help me to understand if I have great technologies and which sector problems my technology can be useful. AI helps me explore more widely and research topics we never had a chance to explore.
Third point is AI can act as expert in another domain and speaking in a language I can understand and then speaking in a language that human experts will not be able to speak in my language because I'm in the AI domain and they are in life science or biological domain. How the AI can facilitate this communications to enable us better way of collaborations is nearly linearly excited areas.
Tim: Oh, that's quite deep, isn't it? That fundamental element about being able to communicate almost with the different cultures that come with your domain as well. Not just the language, but something quite deep there, which I've not really thought about before. That's really interesting. I'd like to pick up on your point around research and partnering, but also partnering with industries and sectors, and real-world challenges if you want. What's your perspective on universities collaborating with industry to unlock AI? More so what do you see as the key challenges and opportunities that we could bring out from these partnerships?
Yonghong: Let's say, why those two partners, industry and universities working together will be a huge benefit. First of all, those in the AI era, in the AI times, those two organizations are in a very different speed. University normally looking into the long term. How? Because education is a long term impact and industries most likely will have rapid approaches to response what they need. This information or insight can come back to the universities to inform what really needs for the current stage and the next few years’ time.
I think we working together can make these two things be more joined together, co-development since they are huge benefit. Also they are having totally different business model or financial model which can be useful to mitigate the risk for each side, for each parties of the work. Of course, there are some challenges or some gaps, which is as the speed is the gap. Also, we are focusing on different our service stakeholders. We are focused on the student, we are focused on the learners, the future working force and the industry focus on business need.
This is supply chain of the value of the knowledges is a value chain of the knowledges. If we are going to develop our students for the knowledge needed today, then once they graduate, they will be no need because it's changed. What we need to do is to have industry on board to develop our programs towards the need for when they graduate.
Tim: A much deeper partnership actually, isn't it? It's another lens onto the discussion you had around learning how to learn. How do we evolve together?
Yonghong: The future education will no longer same as today's. There will be a lot of AI lectures available to help our students, our future working force, to learn by themselves. What value we can offer on the top of that, that is big questions our universities need to think about.
Tim: I think that's a perfect endpoint. That's fantastic. We are also out of time. I think that's just such a wonderful point to close on. Thank you so much for joining today, Yonghong. This has been a really interesting discussion.
Yonghong: Thank you so much, Tim.
Tim: Thank you everyone who's listening in to this. I hope you've enjoyed it as much as I had. Please do also check out our other podcasts in the series as well if you like this one.
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