This podcast features Robert Wood, Emeritus Professor of Information Systems at Alliance Manchester Business School and Professor of Business Technology at the Greater Manchester Business School, University of Bolton. In this enlightening episode, Bob shares his insights into the transformative impact of artificial intelligence (AI) on higher education. As traditional lecture halls give way to innovative algorithms, the conversation explores the ways in which AI has evolved and is reshaping the educational landscape.

In this episode, we explore the transition from bias to belonging, focusing on the imperative of fostering inclusive AI practices in higher education. We examine the pivotal role of artificial intelligence in enhancing diversity, equity, and inclusion within academic environments. Join us in this critical conversation with Dr Olatunde Amoo Durowoju, a Reader in Education Management and the Associate Dean, Diversity and Inclusion, for the Faculty of Business and Law.

Having led and worked on several inclusivity projects within and outside of LJMU, Tunde has developed a keen understanding of the nuanced requirements for a truly inclusive and equitable practice and have been actively working towards removing barriers and delivering equity of outcomes and experiences for all student and staff groups. He has won several awards over the years and is currently leading a campaign on developing staff and student agency towards the use of artificial intelligence for inclusivity purposes within higher education. He is the creator of the ‘Working Seminars on Achieving Inclusivity using Artificial Intelligence in HE’, a melting pot for inclusivity scholars and AI advocates. He is a member of the UK Higher Education Award Gap Group and the Vice Chair of the Anti-Racism and Learning Technology special interest group within the Association for Learning Technology. While we may be different from each other, we are not different in importance and value.

In this episode, Technology Enhanced Learning expert Dr Enilda Romero-Hall, breaks down the intricacies of how AI is transforming traditional educational approaches and reshaping the way we learn. Together, we explore the exciting prospects and ongoing discussions regarding the integration of AI in technology-enhanced learning.

Dr. Enilda Romero-Hall is an award-winning scholar, Associate Professor, and Coordinator of the Learning, Design, and Technology Ph.D. program at The University of Tennessee Knoxville.  Enilda also serves as the Program Chair for the AERA SIG Instructional Technology and Advising Editor to the Feminist Pedagogy for Online Teaching digital guide. Her research interests include the design and development of interactive multimedia, faculty and learners’ digital literacy, and networked learning in online social communities, in addition to innovative research methodologies; culture, technology, and education; and feminist pedagogies. 

In this follow up episode with guest speaker Dr Olatunde Durowoju, we discuss how AI tools can be designed to ensure equal access and engagement from students while avoiding bias and disparities. We explore steps institutions can take to facilitate professional development of staff to create an inclusive learning environment that respects differences in perspectives while promoting equity.

Dr Olatunde Amoo Durowoju is a Reader in Education Management and the Associate Dean, Diversity and Inclusion, for the Faculty of Business and Law. Having led and worked on several inclusivity projects within and outside of LJMU, Tunde has developed a keen understanding of the nuanced requirements for a truly inclusive and equitable practice and have been actively working towards removing barriers and delivering equity of outcomes and experiences for all student and staff groups. He has won several awards over the years and is currently leading a campaign on developing staff and student agency towards the use of artificial intelligence for inclusivity purposes within higher education. He is the creator of the ‘Working Seminars on Achieving Inclusivity using Artificial Intelligence in HE’, a melting pot for inclusivity scholars and AI advocates. He is a member of the UK Higher Education Award Gap Group and the Vice Chair of the Anti-Racism and Learning Technology special interest group within the Association for Learning Technology. While we may be different from each other, we are not different in importance and value.

This episode defines AI literacy by exploring its key components and argues it's crucial role in today's world. With the rapid pace at which Generative AI technologies are advancing, how do we navigate this complex landscape and foster critical thinking abilities in learners in Higher Education? Join Dr Martin as he taps into the response from Russell Group Universities in the UK to rise to the challenges and benefits of integrating Generative AI into teaching and learning.  

⁠Dr Martin Compton⁠ is the College Lead for Programme, Module and Assessment Design at King’s Academy, King's College London. With over 30 years in education, Martin has taught at various levels and in a number of countries. He focuses on adapting higher education pedagogy, assessment and feedback to emerging technologies like generative AI, advocating for compassionate pedagogy and joyful learning with a 'freedom to learn' philosophy. Martin also contributed and co-delivers on the ⁠free 'Generative AI in Higher Education' online course ⁠from King's College London which has been helping the HE sector understand the impact of AI since its launch in October 2023. 

Dr Gerhard Kristandl is an Associate Professor for technology enhanced learning in the school of accounting, finance, and economics at the University of Greenwich. He has 18 years of experience in higher education across the UK, Canada, and Austria, with a focus on learning technologies in HE. He is a key member of the University of Greenwich AI Task Force, the university lead for Mentimeter, a Senior Fellow of the Higher Education Academy, and a former management consultant. He blogs about generative AI on LinkedIn and Medium, and runs his own YouTube channel, with recent videos around generative AI and its applications in education. He is passionate about creating engaging and innovative learning experiences for his students and is a strong believer that generative AI makes and will make human educators even more important than ever before.

LinkedIn profile: https://www.linkedin.com/in/gerhardkristandl/

Medium: https://medium.com/@gerhard.kristandl

YouTube channel: https://www.youtube.com/@drgeekay

''AI is fundamentally uncaring. It is a language model. It’s not a system that knows and cares about its students. It can be enormously helpful but it needs to be in addition to the support and mentoring of a human teacher. If we can get the balance right, so that the human teacher is at the centre of education with AI acting as a really valuable supplement to that, then I think we have got a powerful combination'' - Prof. Mike Sharples. 

In this episode, in conversation with Mike, we unpick some nuanced considerations educators need to be mindful of when planning to use Gen AI technologies for teaching and learning. As Mike suggests 'Gen AI could be treated as a conversational partner who is prone to make mistakes' and as educators we shouldn't forget that the focus 'should always be on good pedagogy'. 

Professor Mike Sharples is Emeritus Professor of Educational Technology at The Open University, UK. He gained a PhD from the Department of Artificial Intelligence, University of Edinburgh on Cognition, Computers and Creative Writing. His expertise involves human-centred design and evaluation of new technologies and environments for learning. He provides consultancy for institutions worldwide including UNESCO, UNICEF, universities and companies. As Academic Lead for FutureLearn.com he led pedagogy-informed design of the open learning platform. He is an Associate Editor of the International Journal of Artificial Intelligence in Education. He is author of over 300 published papers in the areas of educational technology, learning sciences, science education, human-centred design of personal technologies, artificial intelligence and cognitive science. His recent books are Practical Pedagogy: 40 New Ways to Teach and Learn and Story Machines: How Computers Have Become Creative Writers both published by Routledge, and An Introduction to Narrative Generators, published by Oxford University Press.

In this episode, we unpack the AI CHECKLIST Model, a framework designed to enhance AI literacy in Higher Education students. What is the model, and what inspired its development? Sue Beckingham shares insights into the components of CHECKLIST and the pressing need for a structured approach to teaching AI literacy skills while Peter Hartley discusses strategies for adapting it to various disciplines with reference to their recently published book co-edited with Prof. Jenny Lawrence and S. Powell. The AI CHECKLIST model provides practical steps, emphasizing accessibility and inclusion. Beyond AI literacy, Sue and Peter also reflect on the model's emphasis on responsible AI use and decision-making, equipping students to navigate complex challenges.

To check resources relevant to the AI CHECKLIST model, click on the hyperlinks in the episode detail.

Peter Hartley is now freelance Higher Education Consultant, and Visiting Professor at Edge Hill University, following previous roles as Professor of Education Development at Bradford and Professor of Communication at Sheffield Hallam. As National Teaching Fellow since 2000, he has promoted new technology in education, including the development of award-winning software. He led influential development projects for HEA and Jisc and was involved in other national initiatives (e.g. CETLs) covering a range of themes, including institutional online and assessment strategies.

Sue Beckingham is an Associate Professor and Learning, Teaching and Assessment Lead in the School of Computing and Digital Technologies at Sheffield Hallam University. In addition to teaching at both undergraduate and postgraduate level, Sue has an academic development leadership role where she provides support and guidance relating to learning, teaching and assessment. In 2017 Sue was awarded a National Teaching Fellowship. Sue is also a Fellow and Executive Committee Member of the Staff and Educational Development Association.

In this episode, Dr Zhongtian Sun shares insights from both sides of the AI education landscape—as a researcher developing behaviour-aware personalised learning systems, and as a lecturer witnessing first hand how generative AI is reshaping how students learn.

We dive into the development of SPAR-GNN, a hybrid framework that uses graph neural networks and large language models to deliver selective feedback only when students show signs of struggle—like frustration or hint-dependence. We unpack how “at-risk” learners are identified, the ethical dimensions of selective AI support, and what it means to teach in an age where tools like GPT and Co-pilot are becoming second nature to students.

We also explore the shifting dynamics of deep learning and software development education—where project-based, independent learning collides with the realities of AI-assisted shortcuts. What new pedagogies are emerging to ensure genuine understanding, not just efficiency? And how do we assess learning when generating an answer is no longer proof of knowledge?

Dr Zhongtian Sun is a Lecturer in Artificial Intelligence at the University of Kent, with a research focus in graph representation learning, explainable AI and large language models (LLMs). His research aims to enhance the reasoning capabilities of deep learning models, with applications in healthcare, finance, education and recommendation.

He holds a PhD in Computer Science from Durham University and has held research roles at the Universities of Cambridge and Oxford, contributing to projects in clinical AI, knowledge graph reasoning and LLM-based decision support. He also worked on a funded research project with the UK Department for Transport through the Turing Internship Network.

Dr Sun is Co-founder and CTO of an AI for Finance startup, developing a knowledge graph powered platform for market analysis and multi-agent portfolio reasoning.

His academic collaborations span Oxford, Cambridge, Glasgow, Durham and the Alan Turing Institute. He serves as Area Chair and Senior Programme Committee Member for leading conferences including ECAI, AIED, ICLR and is Visiting Faculty at the University of Cambridge.

This podcast episode, featuring guest Lukas Erle, considers the transformative impact of Generative AI (GenAI) in higher education. The discussion explores both the student and educator perspectives on the significant challenges and opportunities presented by GenAI. The episode examines how students currently perceive and use GenAI in their studies, and the needs and insecurities they face. Furthermore, it addresses the critical topic of bridging the gap between student and teacher experiences to inform future regulations. Finally, the episode concludes with a forward-looking discussion on co-creating effective and adaptable guidelines for GenAI, and the proactive steps institutions should take to ensure its responsible and beneficial integration. 

Lukas Erle is a research associate and lecturer at Ruhr West University of Applied Sciences (HRW) in Germany, working in the research group of Prof. Dr.Eimler and Prof. Dr. Straßmann. His expertise is the intersection of psychology and computer science dealing with human-robot interaction and AI in higher education. Lukas also leads courses on psychology in human-computer interaction and researches how students and educators can co-create sustainable, ethical uses of AI in higher education.

This episode explores the balance between technological innovation and human-centered learning through the use of intelligent tutoring systems (ITS) in higher education. With guest speaker Adrienn Toth, we discuss the benefits of ITS—such as personalised feedback, adaptive scaffolding, and improved engagement—while highlighting the critical role of student trust. Trust, Adrienn argues, cannot be engineered by algorithms alone but must be built through intentional design that prioritises transparency, collaboration, and ethical responsibility. By engaging students in the co-creation of these systems—through design input, feedback loops, and opportunities for agency—ITS can empower learners and align more closely with pedagogical values. Adrienn also emphasises that while machines can support learning, it is trust that ensures their effectiveness.

Adrienn Toth is a doctoral student in the Joint Doctoral Program in Learning Sciences offered by ETH Zurich and EPFL. She completed her studies at Corvinus University of Budapest and Erasmus University Rotterdam, with exchange semesters at HEC Montreal and Universidad Torcuato di Tella in Buenos Aires, Argentina. Apart from her studies, she has always worked in the education field, first at a startup and later in a university environment, mainly focusing on content creation and curriculum development.

In her research, Adrienn focuses on supporting the adoption of AI-based Intelligent Tutoring Systems (ITS) in the core curriculum of higher education institutions. More precisely, she would like to understand how students perceive ITS from the data privacy perspective and what are the barriers of increasing their trust in such systems. Building on these identified concerns, the goal of her project is to enhance existing ITS with new features specifically targeting these student concerns and testing their effectiveness in controlled experiments, field studies and qualitative evaluations.

AI is changing the way students learn—but is it also changing the way they think? In this episode, we take a closer look at whether we are letting machines take over too much of the thinking process and what that means for skills like critical thinking, reflection and self-questioning. We talk about how educators can design learning that keeps students actively engaged, how to spot the difference between surface-level AI use and deeper learning, and simple ways to weave metacognitive prompts into AI tasks so students stay thoughtful, curious, and in control of their own learning.

Dr. Anjali Singh is a postdoctoral fellow at the University of Texas at Austin, conducting research at the intersection of Learning Sciences and Human-AI Interaction. Her research focuses on the design and study of interactive learning technologies that support reflection, critical thinking and metacognition in learners in the age of AI. She holds a PhD in Information from the University of Michigan. Her work has been published at venues such as L@S, LAK, AIED, SIGCSE, and AAAI, and has been recognized with multiple best paper awards.

Links to resources mentioned in the episode: 

the chapter on metacognitive prompts - the International Handbook of Metacognition and Learning Technologies. 

Anjali's research paper presented at a workshop at AIED: https://arxiv.org/abs/2505.24014