Higher Education Policy Institute (HEPI), October 2025
Summary
This collection of essays explores how artificial intelligence—particularly generative AI (GenAI)—is reshaping the university sector across teaching, research, and administration. Contributors, including Dame Wendy Hall, Vinton Cerf, Rose Luckin, and others, argue that AI represents a profound structural shift rather than a passing technological wave. The report emphasises that universities must respond strategically, ethically, and holistically: developing AI literacy among staff and students, redesigning assessment, and embedding responsible innovation into governance and institutional strategy.
AI is portrayed as both a disruptive and creative force. It automates administrative processes, accelerates research, and transforms strategy-making, while simultaneously challenging ideas of authorship, assessment, and academic integrity. Luckin and others call for universities to foster uniquely human capacities—critical thinking, creativity, emotional intelligence, and metacognition—so that AI augments rather than replaces human intellect. Across the essays, there is strong consensus that AI literacy, ethical governance, and institutional agility are vital if universities are to remain credible and relevant in the AI era.
Key Points
AI literacy is now essential for all staff and students.
GenAI challenges traditional assessment and integrity systems.
Universities must act quickly but ethically in AI integration.
Professional services can achieve major efficiency gains through AI.
AI enables real-time strategy analysis and forecasting.
AI literacy must extend to leadership and governance structures.
Human intelligence—creativity, criticality, empathy—remains central.
Ethical frameworks and transparency are essential for trust.
Data maturity and infrastructure underpin successful adoption.
Collaboration across disciplines and sectors will shape sustainable change.
Conclusion
The report concludes that AI will redefine the university’s purpose, requiring institutions to shift from reactive adaptation to active leadership in shaping the AI future. The challenge is not simply to use AI but to ensure it strengthens human intelligence, academic integrity, and social purpose in higher education.
Generative AI is not just changing how we create, but how we fundamentally process information and express ourselves. Explore the profound ways this transformative technology could reshape human thought patterns and linguistic communication in the years to come. Image (and typos) generated by Nano Banana.
Source
The Conversation
Summary
Antonio Cerella examines how generative AI may reshape the cognitive and linguistic habits that underpin human thought. Drawing on psychology, neuroscience, and linguistics, he argues that over-reliance on AI tools risks weakening creativity, critical thinking, and language mastery. Just as GPS technology has diminished spatial memory, constant AI-assisted writing and problem-solving could erode our ability to form and express original ideas. Cerella warns that when language becomes pre-packaged through AI systems, the connection between speech and thought deteriorates, fostering a “culture of immediacy” driven by emotion rather than understanding. Yet for those with mature linguistic awareness, AI can still serve as a creative partner—if used reflectively and not as a substitute for thought.
Key Points
Overuse of AI may dull critical thinking and creative language use.
Psychological research shows that technological reliance can reconfigure the brain.
AI-generated language risks weakening the link between thought and expression.
The loss of linguistic agency could erode democratic discourse and imagination.
Conscious, reflective engagement with language can preserve creativity and autonomy.
To truly prepare students for tomorrow’s workforce, higher education must foster an AI-positive culture. This involves embracing artificial intelligence not as a threat, but as a transformative tool that enhances skills and creates new opportunities in the evolving world of work. Image (and typos) generated by Nano Banana.
Source
Wonkhe
Summary
Alastair Robertson argues that higher education must move beyond piecemeal experimentation with generative AI and instead embed an “AI-positive culture” across teaching, learning, and institutional practice. While universities have made progress through policies such as the Russell Group’s principles on generative AI, most remain in an exploratory phase lacking strategic coherence. Robertson highlights the growing industry demand for AI literacy—especially foundational skills like prompting and evaluating outputs—contrasting this with limited student support in universities. He advocates co-creation among students, educators, and AI, where generative tools enhance learning personalisation, assessment, and data-driven insights. To succeed, universities must invest in technology, staff development, and policy frameworks that align AI with institutional values and foster innovation through strategic leadership and partnership with industry.
Key Points
Industry demand for AI literacy far outpaces current higher education provision.
Universities remain at an early stage of AI adoption, lacking coherent strategic approaches.
Co-creation between students, educators, and AI can deepen engagement and improve outcomes.
Embedding AI requires investment in infrastructure, training, and ethical policy alignment.
An AI-positive culture depends on leadership, collaboration, and flexibility to adapt as technology evolves.
Ever wonder why AI-generated images sometimes have a unique, almost unnatural vibrancy? This visual contrast highlights the fundamental differences in how AI systems and human perception process and interpret visual information, explaining the often “garish” aesthetic of AI art. Image (and typos) generated by Nano Banana.
Source
The Conversation
Summary
T. J. Thomson explores how artificial intelligence perceives the visual world in ways that diverge sharply from human vision. His study, published in Visual Communication, compares AI-generated images with human-created illustrations and photographs to reveal how algorithms process and reproduce visual information. Unlike humans, who interpret colour, depth, and cultural context, AI relies on mathematical patterns, metadata, and comparisons across large image datasets. As a result, AI-generated visuals tend to be boxy, oversaturated, and generic – reflecting biases from stock photography and limited training diversity. Thomson argues that understanding these differences can help creators choose when to rely on AI for efficiency and when human vision is needed for authenticity and emotional impact.
Key Points
AI perceives visuals through data patterns and metadata, not sensory interpretation.
AI-generated images ignore cultural and contextual cues and default to photorealism.
Colours and shapes in AI images are often exaggerated or artificial due to training biases.
Human-made images evoke authenticity and emotional engagement that AI versions lack.
Knowing when to use AI or human vision is key to effective visual communication.
by Sue Beckingham, NTF PFHEA – Sheffield Hallam University and Peter Hartley NTF – Edge Hill University
Estimated reading time: 8 minutes
Image created using DALLE-2 2024 – Reused to save cost
The GenAI industry regularly proclaims that the ‘next release’ of the chatbot of your choice will get closer to its ultimate goal – Artificial General Intelligence (AGI) – where AI can complete the widest range of tasks better than the best humans.
Are we providing sufficient help and support to our colleagues and students to understand and confront the implications of this direction of travel?
Along with many (most?) GenAI users, we have seen impressive developments but not yet seen apps demonstrating anything close to AGI. OpenAI released GPT-5 in 2025 and Sam Altman (CEO) enthused: “GPT-5 is the first time that it really feels like talking to an expert in any topic, like a PhD-level expert.” But critical reaction to this new model was very mixed and he had to backtrack, admitting that the launch was “totally screwed up”. Hopefully, this provides a bit of breathing space for Higher Education – an opportunity to review how we encourage staff and students to adopt an appropriately critical and analytic perspective on GenAI – what we would call ‘critical AI literacy’.
Acknowledging the costs of Generative AI
Critical AI literacy involves understanding how to use GenAI responsibly and ethically – knowing when and when not to use it, and the reasons why. One elephant in the room is that GenAI incurs costs, and we need to acknowledge these.
Staff and students should be aware of ongoing debates on GenAI’s environmental impact, especially given increasing pressures to develop GenAI as your ‘always-on/24-7’ personal assistant. Incentives to treat GenAI as a ‘free’ service have increased with OpenAI’s move into education, offering free courses and certification. We also see increasing pressure to integrate GenAI into pre-university education, as illustrated by the recent ‘Back to School’ AI Summit 2025 and accompanying book, which promises a future of ‘creativity unleashed’.
We advocate a multi-factor definition of the ‘costs’ of GenAI so we can debate its capabilities and limitations from the broadest possible perspective. For example, we must evaluate opportunity costs to users. Recent research, including brain scans on individual users, found that over-use of GenAI (or specific patterns of use) can have definite negative impact on users’ cognitive capacities and performance, including metacognitive laziness and cognitive debt. We group costs into four key areas: cost to the individual, to the environment, to knowledge and cost to future jobs.
Cost of Generative AI to the individual, environment, knowledge and future jobs (Beckingham and Hartley, 2025)
Over-reliance: Outcomes for learning depend on how GenAI apps are used. If students rely on AI-generated content too heavily or exclusively, they can make poor decisions, with a detrimental effect on skills.
Safety and mental health: Increased use of personal assistants providing ‘personal advice’ for socioemotional purposes can lead to increased social isolation
Cost to the environment
Energy consumption – The infrastructure used for training and deploying Large Language Models (LLMs) requires millions of GPU hours to train, and increases substantially for image generation. The growth of data centres also creates concerns for energy supply.
e-Waste – This includes toxic materials (e.g. lead, barium, arsenic and chromium) in components within ever-increasing LLM servers. Obsolete servers generate substantial toxic emissions if not recycled properly.
Cost to knowledge
Erosion of expertise – Data is trained on information publicly available on the internet, from formal partnerships with third parties, and information that users or human trainers and researchers provide or generate.
Misinformation – Indiscriminate data scraping from blogs, social media, and news sites, coupled with text entered by users of LLMs, can result in ‘regurgitation’of personal data, hallucinations and deepfakes.
Bias – Algorithmic bias and discrimination occurs when LLMs inherit social patterns, perpetuating stereotypes relating to gender, race, disability and protected characteristics
Job matching – Increased use of AI in recruitment and by jobseekers creates risks that GenAI is misrepresenting skills. This creates challenges for job-seeker profile analysers to accurately identify skills with candidates that can genuinely evidence them.
New skills – Reskilling and upskilling in AI and big data tops the list of fastest-growing workplace skills. A lack of opportunity to do so can lead to increased unemployment and inequality.
We can only develop AI literacy by actively involving our student users. Previously we have argued that institutions/faculties should establish ‘collaborate sandpits’ offering opportunities for discussion and ‘co-creation’. Staff and students need space for this so that they can contribute to debates on what we really mean by ‘responsible use of GenAI’ and develop procedures to ensure responsible use. This is one area where collaborations/networks like GenAI N3 can make a significant contribution.
Sadly, we see too many commentaries which downplay, neglect or ignore GenAI’s issues and limitations. For example, the latest release from OpenAI – Sora 2 – offers text to video and has raised some important challenges to copyright regulations. There is also the continuing problem of hallucinations. Despite recent claims of improved accuracy, GenAI is still susceptible. But how do we identify and guard against untruths which are confidently expressed by the chatbot?
We all need to develop a realistic perspective on GenAI’s likely development. The pace of technical change (and some rather secretive corporate habits) makes this very challenging for individuals, so we need proactive and co-ordinated approaches by course/programme teams. The practical implications of this discussion is that we all need to develop a much broader understanding of GenAI than a simple ‘press this button’ approach.
Reference
Beckingham, S. and Hartley, P., (2025). In search of ‘Responsible’ Generative AI (GenAI). In: Doolan M.A. and Ritchie, L. eds. Transforming teaching excellence: Future proofing education for all. Leading Global Excellence in Pedagogy, Volume 3. UK: IFNTF Publishing. ISBN 978-1-7393772-2-9 (ebook). https://amzn.eu/d/gs6OV8X
Sue Beckingham
Associate Professor Learning and Teaching Sheffield Hallam University
Sue Beckingham is an Associate Professor in Learning and Teaching, Sheffield Hallam University. Externally she is a Visiting Professor at Arden University and a Visiting Fellow at Edge Hill University. She is also a National Teaching Fellow, Principal Fellow of the Higher Education Academy and Senior Fellow of the Staff and Educational Developers Association. Her research interests include the use of technology to enhance active learning; and has published and presented this work internationally as an invited keynote speaker. Recent book publications Using Generative AI Effectively on Higher Education: Sustainable and Ethical Practices for Learning Teaching and Assessment.
Peter Hartley
Visiting Professor Edge Hill University
Peter Hartley is now Higher Education Consultant, and Visiting Professor at Edge Hill University, following previous roles as Professor of Education Development at University of Bradford and Professor of Communication at Sheffield Hallam University. National Teaching Fellow since 2000, he has promoted new technology in education, now focusing on applications/implications of Generative AI, co-editing/contributing to the SEDA/Routledge publication Using Generative AI Effectively in Higher Education (2024; paperback edition 2025). He has also produced several guides and textbooks for students (e.g. co-author of Success in Groupwork 2nd Edn ). Ongoing work includes programme assessment strategies; concept mapping and visual thinking.
