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Sam Illingworth and Rachel Forsyth, "GenAI in Higher Education: Redefining Teaching and Learning" (Bloomsbury, 2026)
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Dr. Sam Lingworth
the New Books Network.
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Ketam Sulongkumar (Host)
Hello everyone. Welcome to New Books Network. Today. I'm here with Sam e. Lingworth and Dr. Rachel Forsyth to talk about their book Generative AI in Higher Education Redefining Teaching and Learning. I'm Ketam Sulongkumar, the host of this channel. Before I move to the authors, let me say something about this book now. This book again follows four foundational pillars in terms of discussing the content. The first is this idea of student centeredness. Second is trust. Third is relevant and the fourth is agency. Now Generative AI being one of the technology again which is bringing a lot of disruption in higher education. This is one of the very important topic that needs to be continually discussed but also at the same time literacy on this is something which needs to be developed. The authors today I'm sitting with has been working on this and it's a privilege to Sit and talk with the authors here. So let me first go to Dr. Sam. Can you please tell me something about yourself?
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Dr. Sam Lingworth
Oh, wow. Well, thank you for such a lovely introduction to the book. So, yeah, my name's Sam. I'm a full professor at Edinburgh Napier University in the uk and my research involves, well, lots of different things, but one of the things that I do at the moment is looking at critical AI literacy. So really knowing when to use AI and when not to use use AI. And I guess in addition to that, I'm also a poet. And similarly to Rachel, although Rachel can introduce herself. I'm also a lapsed physicist. So I used to be a physicist, but I'm no longer one of those more about the qualitative research these days. So that's me. And thank you very much for the introduction to you.
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Ketam Sulongkumar (Host)
Great, thank you. And Dr. Ja. Can you tell me something about yourself?
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Dr. Rachel Forsyth
Yes, yeah, thanks. I'm a senior educational developer at Lund University in Sweden. Although I am English and I worked in the UK for a long time before I came here. Yeah, yeah. Also a lapsed physicist. And I got interested in education because when I was a physics lecturer, I felt like I could always be doing things better and wanted to improve my teaching and somehow I ended up being more interested in that and doing research in that area. And my current research is much more about how relationships are built in the classroom. And right now we're looking at how AI might affect those relationships and what teachers and students could be doing to make sure that they maintain trust. So that's what I'm really interested in.
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Ketam Sulongkumar (Host)
Yeah, really interesting academic background and really interested to have this conversation with both of you. Now moving into the book and the discussion that is there in the book. One of the thing that. Again, one of the question that we have today in terms of generative AI and education is the initial question of how do we actually really understand generative AI? What, what, what is this? What kind of mission or what kind of system is this? So this question is open to both of you? Yeah.
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Dr. Sam Lingworth
Wow. Rachel, do you want me to have an attempt to.
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Dr. Rachel Forsyth
Yes, it's. It's a great question. Yeah, I don't mind. I don't can start.
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Dr. Sam Lingworth
So just basically how we would define AI or how would we use AI to what would be most useful for answering that question.
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Ketam Sulongkumar (Host)
Yeah. So how do we understand generative AI? Yeah, some say that it is a calculated mission or whatever not. So how do we really understand this?
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Dr. Sam Lingworth
Wow. Okay, so try and. Excellent question. And I'm now stalling but basically the way that I would understand generative AI is, is that it's the use of artificial intelligence to effectively pattern match. So what it's doing is it's generating content, be that text, be that images, be that numbers or code, based on patterns that it has observed against its training data set. And that training dataset, in the case of a large language model like that used by ChatGPT or by Claude or by Gemini or something like that, is effectively most of human knowledge that's stored on the Internet. And again, one of the issues with that is that it's obviously hugely biased. And I'm sure we'll get into this later, but my main issue with generative AI isn't necessarily that generative AI itself as a methodology is biased. It's that it's trained on data that is biased and that is written by people who are biased. Um, but to answer your question, the way that I would interpret generative AI is to be the creation of content based on its training data. And the key thing to remember here is it's in almost all instances not necessarily creating something new. It's effectively guessing what's going to come next based on the patterns on which it has been trained that Rachel can feel free to completely.
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Dr. Rachel Forsyth
No, that is roughly the definition that we use in the book. And I think it's really important because it sets up the discussions, like about bias, but also the fact that what's on the Internet is effectively, as you said, or stored somewhere is digitized. And not everything can be digitized. And so things that can't be digitized then get faked effectively because they can't be real things. So it gives us an end to what Sam talked about earlier, the critical AI literacy. And the other thing I think is quite important. I started off talking about generative AI tools. A tool implies something useful. And I do think there are many good applications of these large language model based products. But I try now to say product and, and, or services. So generative AI products or services. Because what we're talking about very often in higher education is something that's been packaged up to be sold to us. And when something is sold to us, then it comes with conditions that we might, just might need to think about a lot. So I think that loops back to what you said, Sam, about having to be very critical in the academic sense of the word.
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Ketam Sulongkumar (Host)
Yeah, yeah, thank you very much for that. Coming to the next question, I think when we talk about generative AI and education, I think many of the teachers or tutors have some kind of reluctance towards, again, using this in teaching and learning practices. Now, obviously, the reluctance might come from not having enough literacy about generative AI. So for someone who wants to integrate generative AI teaching and learning, or understanding how to actually use it, to take the first step of using it in teaching and learning practices, what should be the first step for someone to do?
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Dr. Sam Lingworth
Okay, so for me, the first step that people should be doing is having a dialogue with the people that are using it. So, you know, really kindly pointed out the four pillars of the book. And I think Rachel and I would say the student centeredness is the core for that. And again, I don't want to speak for Rachel, but I know Rachel's work quite well, and I think both of us really feel that element of the student at the real center of everything is essential. So when we're using AI tools or generative AI, the key thing for me is to make sure that we're actually asking our students and our colleagues as well how they want to use the tools rather than assuming how they should be using it. So for me, it comes back to this dialogue and in it to give a specific example of that. It would be if I myself feel very happy with my students using AI in part of their assessment. But it wouldn't just necessarily be a yes, you can use AI tick box on a cover sheet. It would begin with a conversation at the start of the module saying to the students, look, how I think we should be using AI. These are what the limitations are, these are what the challenges are, these are what the benefits are. Let's continue having this dialogue throughout.
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Dr. Rachel Forsyth
And.
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Dr. Sam Lingworth
And then when you submit your assignment, you can tell me how you've used AI and in which ways you've done it as well. But for me, everything comes back to the dialogue just with any pedagogy. You know, if we were, if we were using another technology, if we were using a different teaching style, be it flipped learning or whatever, rather than just doing it, I would want to have a dialogue with my students, first of all, to make sure that it matched their needs and, yeah, their experiences as well.
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Dr. Rachel Forsyth
And I would add to that as well, not to be scared of doing this. So I'm very happy if people decide that they, for a variety of reasons, they do not want to use generative AI products, that's fine. But you do have to take a step of finding out a bit more about them and ideally testing their use. Sometimes we don't get a choice because these products are being built into all Kinds of things that we use every day anyway. And it's quite hard to stop it, to switch it off. But of course you can always switch it off. But I think it's very important to do what Sam said, talk to students, but also to open yourself up to what these products do so that you can make a good argument either for or against using them. It's very, very difficult if you're not, if you're only looking at stuff people post on the Internet, terrible pictures or reading the news stories. Like anything else, it's very contextual how this might be used productively and ethically. And university teachers and teachers in general should have confidence in their own ability to make those judgments themselves and not to just take from what someone else has said. So be brave and have a try.
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Ketam Sulongkumar (Host)
Yeah. Yes, Great. Now, when you look at today's context, obviously many of the educational institutions are actually adapting generative AI in their institution and also coming up with policies in terms of how the institution will use generative AI. So what are some of the mistakes that institutions do in terms of adopting generative AI, but also at the same time, what might some of the good ways of actually adopting generative AI in an institution?
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Dr. Sam Lingworth
It's a really good question and I guess Rachel and I can answer it both with how our institutes should do it. So I'm Edinburgh Napier University in the UK in Scotland. I actually think my institute does it very well. So I think you need to have quite clear guidelines, but they need to be flexible as well to allow for different situations. So the broad governance of education generative AI or AI use at Edinburgh Napier University is threefold. One, never upload any personal information, be it, you know, someone's email address, someone's demographic status. Number two, never use AI for like confidential information. So don't upload internal strategy documents, for example, because you don't know where it's being trained, etc. Same with research as well. You know, if I've done some research that hasn't got IP yet or something, it would be silly to train to upload it to a model. And number three, never. And I think this is the most important, never use AI for decision making purposes. So that can be quite broad. It could be like never, never use it to automate assessment. But also if I'm on a hiring panel, never use AI to read through CVS and determine which people are there or they're not. So I'll say one last thing though, Pastor Rachel, for example, how I interpret that is I would never use AI to mark a student's work. But what I would use it for is I would take rough notes on what I thought was effective or not about a student's assessment, and then I would ask AI to frame that in a way that matched good feedback practices or effective feedback practices. So I think there's a nuance there, but they're the broad three things at my university that I think are quite strong.
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Dr. Rachel Forsyth
Yeah, I think the main message that I try and give out is not to get too obsessed with assessment and examination here. That usually takes up all of the discussion time. And there's a much broader conversation that needs to be had. And if we could figure out what place these products have in education, then the examination question looks after itself, because we will decide from that whether we want students to be able to use them or not. In the European Union, we have quite strict legislation now, so we would not be allowed to use these products for things like exam assessment or for appointments. But in practice, as long as somebody's making the final decision, people will always get around that, I think. So these rules are not perfect. We do have to discuss how they might be implemented and how we want them to be implemented. So lots more discussion about what seems right, what doesn't seem right. But in order to do that, people just need a basic level of understanding of what these products are. Hence, I mean, this is why we wrote the book effectively, so that we're hoping to get some conversations going of the kinds that we were already having in our own universities about this.
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Ketam Sulongkumar (Host)
Yes.
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Dr. Rachel Forsyth
Yes.
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Ketam Sulongkumar (Host)
Yeah, that's great. One of the measures that institutions and academic institutions had taken up to actually curb the use of generative AI is actually to adopt generative AI detectors. So in my institutions, Turnitin is one of the detectors AI detector platform that again, teachers use. So can you tell me something about generative AI detectors in terms of its feasibility, usability, and should we be using this?
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Dr. Sam Lingworth
Of course. I can tell you that without swearing. They're complete bs. They don't work. And they don't work for two reasons. They don't work because they're technologically inept, and more importantly than that, they're pedagogically inept as well. So, you know, lots of research has been done on this that proves they don't work. Like, there was a great paper from Stanford last year that basically showed 1 in 5 AI detection software get a false flag. So in other words, if I wrote something entirely by myself, it would be flagged as being AI, and that's me as A white, Christian, heterosexual, cisgendered male. When you're a non native speaking person, that becomes much higher, gets to about 63%, something like that. So they're massively biased against marginalized peoples. And then pedagogically speaking, the problem with these detectors is that it starts from a position of suspicion. So the idea being that we're telling our students we're going to try and catch you out, we're going to try and trick you, whereas actually we need to just have that dialogue. So I don't want to be fight. I mean me, myself personally, I don't even like things like turn it in because I think it has issues surrounding it. It's much better to have a conversation with the students about how they're using the work. And again, there's lots of cultural differences. We know that students from different parts of the world that aren't the uk, like they cite work in a way that would be considered plagiarism in the uk, but which is culturally appropriate or like part of the norm where they come from. So there's a whole issue of like colonialism there as well. So my, my, my basic takeaway is detection software does not work. It's a multi billion pound industry that is corrupt and that is, you know, awful. And the way around it is to just have open discussions with students about when AI is and is not appropriate. And also to allow for the fact that our graduates are going into a world that uses AI. So we need to prepare them for how to use this AI, these tools, how to interrogate them and to know when and when it's not appropriate to use it.
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Dr. Rachel Forsyth
I've got nothing to add to that part of it. I completely agree with you. In practice, if you've got a very large group, those conversations can be quite hard. And if you're teaching a very basic level of a subject, sometimes it's quite easy for students to take from the ideas of others. And so we do need some security that students know something. Examination, assessment, security. Examination security is very important. Conversations are really good. So in practice it can be quite difficult. But again I say to teachers to be confident that they can make good judgments. In general, it's no good setting an assignment where you're only going to get in words that no, I saw a great piece on social media today, but it was, it was along the lines of I spend so many hours grading something that a human did not write. And I think that's a very good way of putting it. That's the situation we don't want to be in, but we're not going to get out of it by adding more technology. We're going to have to find other ways to do that assessment, but people should have confidence they can manage to do that and also to not over worry about it. If one small assessment does not represent what a student can do in a whole program, it's not really, it's not that serious. We have always had ways where it's absolutely vital a student can do something like take a blood pressure or another medical procedure. We've always observed them doing it. We've never relied on them writing down I know how to take a blood pressure. So we've just got to use the same kind of approaches more widely probably. And it will require some thinking. But the detectives don't work and it takes the trust out of the relationship, which I think, as Sam said, is really not a good thing. I understand why university leaders think it's a good idea, but it isn't.
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Ketam Sulongkumar (Host)
Yeah.
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Ketam Sulongkumar (Host)
Now, one of the things that the book touches on is assessment. And if any teachers who have tried to actually do assessment the normal way, where traditionally the way we used to do, then AI is very good at doing any kind of written assessment in terms of student copying from AI. And so the mode or the way by which we do assessment needs to be changed in terms of the technology that is here. So how do you propose we change the way we do assessment?
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Dr. Rachel Forsyth
I mean, I think a lot of people are talking about the fact that we will much more assess the process of how the student got to the product, whatever it was, than the product itself. We have to be very, very clear about what it is that we want students to be able to do and how we can see that they can do it. And so I think we touched on that in the last answer. There will be probably more oral assessments, more assessments of practice, but also more discussion about how we do academic work and why is it important to write an essay. I don't think essays are dead, and I don't think dissertations and theses are dead either. But we do need to be able to check in with students in an effective way during the process, keep them motivated so that they understand that we value seeing what they can do. We're not interested in what a large language model can help, but we're interested in how their thinking changes. And perhaps that's something that in higher education, we haven't been as explicit about as we could be, telling students how we value their voice. And that loops back just to what you said, Sam, about students trying to get a perfect voice, perhaps. And so using software to help with that and then getting caught out by these detectors, because what they're trying to do is what they think we want most of the time. I mean, some students will cheat. They always have. That's not. We're not. We're not designing for them. We're designing for most students who've come to university to learn something. And I'll hand over to you, Sal, I jumped in.
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Dr. Sam Lingworth
No, no, no, no. You didn't jump in at all. I think that's a really good point. And I think a lot of it, again, comes down to pedagogy. I mean, I was reading a brilliant article last night about constructivism and actually how constructivism is seen as the default par excellence in teaching. But actually, going back to what Rachel was saying, when you've got those larger classes and especially when you're teaching foundational ideas, maybe it isn't. So, you know, constructivism, very, very basic level just being this idea that knowledge is constructed by the individual in their brain. And you know, to some extent the far end of that is as you're learning maths, I'm not going to correct you, I'm going to enable you to find out those mistakes for yourself. Whereas actually recent research has shown that there needs to be an element of actual guidance in there. So the work of Lev Vygotsky, for example, and the more knowledgeable other, this concept that yes, we should have dialogue and co learning, but there needs to be a point in which there actually is still instruction. And I think that's what's really, really interesting here because even though I'm very much in favour of the dialogic method and AI can really help students with that, we still need to remember that as educators we do have more knowledge than our students, especially in domain expertise. So even though, as you say, the kind of 5 paragraph, 1500 word essay can easily be replicated by AI, there are ways that we can get around that, like we can have a portfolio submission, et cetera. But as Rachel said, I think the key is not to get obsessed with assessment and to instead think about the underlying ways in which we teach and how AI can actually support that as well. So, you know, one of the ways in which we can do that is the educators can provide the domain expertise and then we can encourage our students to engage with their AI. Basically the key point I was saying there is it's really important to make sure that we have assessment, yes, that matches the individual learning needs of our students, but that we find ways in which the AI tools can support our roles as educators, because it's certainly never again to replace them.
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Dr. Rachel Forsyth
That's great. I mean, thinking about Vygotsky and the knowledge builder and theory of proximal development, I think there are, for some students, there are quite a lot of ways in which some of these projects might actually help them to move across that gap. And it's just being careful that we don't take the teacher out of it altogether because the AI doesn't know what's important next and never will. You can't have an AI teacher or an AI tutor an AI coach. That can't be, because the definition of those words is, to me anyway, means that it's a human job.
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Ketam Sulongkumar (Host)
True, true. Now, the generative AI has obviously pervaded all aspects of our life. The university professors and teachers are learning about Generative AI. And this is where we talk about generative AI literacy. And so while we are also learning, again, the students are actually using to ask whatnot question, whether it be personal or whatever it may be. So what kind of guidelines do we need to actually give the students in terms of using generative AI for personal. Mostly for personal use in that sense. Right. If they are putting a lot of personal information or actually chatting about many personal things to generative AI? Because I think as professors and tutors, I think this is something which we should also sensitize students about. So what kind of guidelines do we give to students regarding this?
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Dr. Sam Lingworth
Okay, so again, it depends on the institute and annoyingly it probably depends on the facilitator or the educator as well. I mean, and to some extent this has always been the case. Right? We can, it doesn't matter if it's AI or effective teaching tools or technologies. It depends very much. It's a bit of a lottery with regards to who students get. And that has always been the case. But I think the main things are that all university should be teaching their students is effectively boils down to one thing. Don't outsource judgment. You need to be able to, you need friction to be able to learn. And it's very easy to generate output, but it's in the construction of that output that real learning takes place. So I would say that's the key thing of just not outsourcing judgment to the AI tools and to instead encourage our students to do that themselves.
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Dr. Rachel Forsyth
And that's something students have to learn because they come in not realizing that their judgment that they have judgment and it's valuable and so on. So we have to help teach them. As part of the interviews for the research I mentioned earlier about trust and generative AI, one teacher said, I say to my students, only use generative AI for things you want to get worse at. And I think that's a little bit excessive, but it's a nice little phrase to say. But I think the most important thing for teachers to say to their students is I care about what you think and I want your thinking to develop. And what we do together is meant to show that. And so those are sort of things they should be saying, not instructions about use this product or don't use that one or whatever, because we'll never keep up with that. But being really clear what you expect from your students is to me the most important thing. And that's the opening for the dialogue. Students can then come back and say, what if I used it for this because they will trust you enough to have that conversation. But I know from where I work that students are very anxious to start a conversation with teachers because they think if they even mention that they might use these products, that the teachers will think they want to cheat. And so they don't say anything, and we're all in the dark. I don't know if you want to add to that. Now,
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Ketam Sulongkumar (Host)
when we talk about generative AI, a technology that is still developing, people are working on it, new features are coming. And also at the same time, when we talk about education as such, education, teaching and learning practices are something which again, is actually developing along with this technology. How do you see the future of this technology developing and the idea of education again coming forward in the days to come? Because every three to six months, new models are coming, new features are coming. And so how do you see this?
- [30:20]
Dr. Sam Lingworth
So I think we both realized that the book, in terms of actual technologies, would be out of date the second it was published. But what we tried to think really carefully about, again, keeping coming back to the pedagogy, but was the pedagogy that underpins everything. So the way in which we use these tools, the way in which we discuss these tools, the way in which we critique these tools, the way in which we don't use these tools, these are questions that will always be relevant that no matter how much the technology changes. So a lot of the outputs in the book are definitely out of date, and the tools probably do a better job in many instances, but not necessarily so. So, for example, a lot of the work that we talk about is misrepresentation and bias and the fact that large language models will often default to the male gaze or the white Western male gaze in a lot of examples. And one of the ways which you can most readily see this is, for example, when you say to a image generator, draw me a picture of a doctor, and it's normally a white man. And the tools haven't really changed. Nano Banana Pro 2, if you ask it to do the same, will do something fairly similar. And again, it's not necessarily because the tools themselves, purely because the tools themselves are biased. It's because they're pattern matching. And the data in which they've been told what a doctor looks like tends to be a white Western man. So I think a lot of the barriers and issues with these tools are still there. And in many ways they're even more subtle and nuanced and difficult to spot. So what we've tried to do is to at least provide educators, administrators and students with the frameworks and techniques they need to critically assess AI outputs.
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Ketam Sulongkumar (Host)
Yeah, great. So, coming to my last thing, and this has been a very wonderful conversation. And so can you tell the listeners about what you are actually currently working on and if anyone wants to reach out to you regarding this book or anything that you're currently working on, how do they reach out to you? Yeah.
- [32:41]
Dr. Rachel Forsyth
Right. Well, I'm always happy to engage in conversation about these issues. This is how we develop our thinking by having conversations, particularly with people from different contexts who working with different constraints and different regulatory systems and so on. So I think that's always good. I'm mostly working with policy matters here, developing guidelines for the university. Sam and I have been working on some open online courses, one for staff, which is already available, and one for students, which will soon be available, and those things will be available internationally. And we certainly welcome people getting in touch to find out how to use them. And I think we need to focus very much on the human element here, what is important in a teaching and learning relationship. And this is an opportunity for us to remember that that's what students come to university for. A long, long time ago, an author, Diana Lorillard, said that students could, if they could get a degree by going to the library for three or four years, then they would have been able to do that 100 years ago because all the knowledge is there, but they do need some structure and we should be talking about that as much as we can. I'll pass over to Sam.
- [34:04]
Dr. Sam Lingworth
No, thanks, Rachel. Yeah, so Rachel and I have been working on a couple of open courses, one of which is available on Coursera, specifically around Gen AI and higher education. It's free for people to use. I really recommend people checking that out. Also, huge thanks to Lund University, because they basically paid for this book to be made open access, which means that anybody across the world can download it and use it for free. And Rachel and I would really love for people to let us know how they're using it. And also if there's anything that we got wrong or that we got right as well, and our email contacts are in the book for people just to reach out if they want to. For myself, you know, I run a substack called Slow AI, which is about critical AI literacy. You can just find it on substack. It's just called Slow AI. And the idea being, again, as we've talked about a lot in this talk, just for people to understand when to use AI and when not to use AI and how to tell the difference between the two. So really happy for people to reach out there. And yeah, Rachel and I are always happy to receive comments and chat to people about their work as well.
- [35:07]
Ketam Sulongkumar (Host)
Yes, thank you.
- [35:09]
Dr. Rachel Forsyth
Contact details in the notes yeah, yeah.
- [35:12]
Ketam Sulongkumar (Host)
Yes. Thank you. Thank you very much, Dr. Rachel and Dr. Sam for being here at New Books Network. And to the listeners, if you are starting off trying to understand generative AI and trying to, you know, know more about it and adopting your teaching and learning practices, I would highly recommend this book. I think this is one of the book where you can actually kind of take the first step towards actually understanding the system and it's written in a very accessible way. So thank you very much Dr. Sam and Dr. Rachel for being here at New Books Network.
- [35:44]
Dr. Rachel Forsyth
Thank you for having us.
- [35:45]
Dr. Sam Lingworth
Thank you. T.
- [35:52]
Dr. Rachel Forsyth
Foreign.
- [35:59]
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