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AI Conversations: Critical discussions about AI, Art And Education

by Chris Rowell


AI Conversations: Critical discussions about AI, art and education
Edited by Chris Rowell

The AI Conversations series was made possible by The Exchange's Digital Practice Team at UAL; Ruth Powell, Darren Gash, Chris Rowell, Kei Ferguson, Roshni Bhagotra and Hannah Hyde.



Babies and Bathwater: how far will AI necessitate an assessment revolution? with Associate Professor Martin Compton, Kings College

How are university students using AI? with Sue Attewell, Head of AI and Codesign at JISC

Can AI be anti-racist? with Professor Gurnam Singh, Warwick University

How is AI affecting HE libraries? with Coco Nijhoff, Senior Teaching Fellow at Imperial College London

Images and AI. with Kristina Thiele, Lecturer at CCW.

Ethical curating using AI tools with Elliot Burns and James Irwin (BA Culture Criticism and Curation, UAL)

Has AI stolen creative labour? with Dave White, Dean of Academic Strategy (online) at UAL

AI and the Art School with Mark Robinson, Learning Technologist at LCF.

Gender bias in AI – can we do anything about it?  with Emma Gibson, Alliance for Universal Digital Rights

What can educators do with AI? 101 Creative Ideas to Use AI in Education with Chrissi Nerantzi and Antonio Martinez-Arboleda
(University of Leeds), Sandra Abegglen (University of Calgary) and Marianthi
Karatsiori (University of Macedonia)

CCI perspectives on AI with Mick Grierson, Creative Computing Institute, UAL

Navigating the Complexities of Machine Translation in University Education with Helen McAllister Jo Bloxham, UAL

The Intersection of Art and Technology: A Journey from the 1960s to Today with Catherine Mason

Generative AI and the Automating of Academia with Donna Lanclos and Lawrie Phipps


The AI 'genie' has left its bottle and is not going back. So, if the genie granted us three wishes, what would we do? Would we want to ‘reverse the tide’ and go back to our pre-AI days? Or wish it away? Or embrace it and see if it does have any benefits? These are the questions that the AI Conversations series wanted to explore. We invited a series of guests working with different aspects of AI and interviewed them on how AI is affecting teaching in and across universities and art colleges.

The AI Conversations series of online discussions was facilitated by the Digital Learning Practice team in the Teaching, Learning and Employability Exchange at UAL. We explored how AI is affecting teaching and learning in a arts based institution through challenging and provocative discussions about where we are going with AI and how we respond to this ever changing phenomenon. Invited subject specialists had conversations on topics such as gender and racial bias, ethical uses of AI, AI and creativity and whether we see a need for a whole assessment revolution.

AI Summaries:

After every live conversation we used AI to summarize the discussion that took place. These summaries have been added to this collection. The AI Conversations were first recorded in MS Teams, which automatically created a transcript. Next the transcript was summarised using Claude and the blog post was generated using ChatGPT4. The Images were created by Adobe Firefly and DALL-E (except for the cover image and the image used in the 'AI and Images' chapter that were created by Kristina Thiele).

Creating these summaries with these AI tools was a relatively quick and easy process but it also masks some the underlying ethical issues of using these tools, Lawrie Phipps put this well at the start of his chapter, "Whilst none of our work was developed using ChatGPT or other Large Language Model, their existence is the subject of our research and we have benefited in carrying out that work. Therefore we would like to acknowledge that tools like ChatGPT do not respect the individual rights of authors and artists, and ignore concerns over copyright and intellectual property in the training of the systems; additionally, we acknowledge that the system was trained in part through the exploitation of precarious workers in the global south". These are important issues and apply not just to Lawrie and Donna's chapter but to all the chapters in this book.

Babies and Bathwater: how far will AI necessitate an assessment revolution?

Associate Professor Martin Compton, Kings College

Part 1: The conversation

The ongoing dialogue around AI’s influence on education often has us pondering over the depth and dimensions of the issue. Our peers frequently express their concerns about students using AI to craft essays and generate images for their assessments. Recently, I (Chris) stumbled upon the AI guidelines by King’s, urging institutions to enable students and staff to become AI literate. But the bigger question looms large: what does being AI literate truly entail?

For me (Martin), this statement from the Russell Group principles on generative AI has been instrumental in persuading some skeptics in the academic realm of the necessity to engage. It’s clear that AI literacy isn’t just another buzzword. It’s a doorway to stimulating dialogue. It’s about addressing our anxieties and reservations, then channeling those emotions to drive conversations around teaching, assessment, and learning.

Truth be told, when we dive deep into the matter of AI literacy, we’re essentially discussing another facet of information literacy. It’s a skill we aim to foster in our students and one that, as educators, we should continually refine in ourselves. Yet, I often feel that the larger academic community might not be doing enough to hone these skills, especially in the digital age where misinformation spreads like wildfire.


With the rise of AI technologies like ChatGPT, I was both amazed and slightly concerned. The first time I tested it, the results left me in awe. However, on introspection, I realized that if an AI can flawlessly generate a university-level essay, then it’s high time we scrutinized our assessments. It’s not about the capabilities of AI; it’s about reassessing the nature and objectives of our examinations.

When my colleagues seek advice on navigating this AI-augmented educational landscape, my primary counsel is simple: don’t panic. Instead, let’s critically analyze our current assessment methodologies. Our focus should pivot from regurgitation of facts to evaluating understanding and application. And if a certain subject demands instant recall of information, like in medical studies, we should stick to time-constrained evaluations.

To make our existing assessments less susceptible to AI, it’s crucial to reflect on their core objectives. This takes me back to the fundamental essence of pedagogy, where we need to continuously question and redefine our approach. Are we merely conducting assessments as a formality, or are they genuinely driving learning? It’s imperative to emphasize the process as much as the final output.

Now, if you ask me whether we should incorporate AI into our summative assessments, my perspective remains fluid. While today it might seem like a radical notion, in the future, it could be as commonplace as using the internet for research. But while we’re in this transitional phase, understanding and integrating AI should be done judiciously.

Lastly, when it comes to AI-generated feedback for students, I believe there’s potential, albeit with certain limitations. There’s undeniable value in students receiving feedback from various sources. Yet, we must tread cautiously to ensure academic integrity.

In essence, as educators and advocates of lifelong learning, we must embrace the challenges AI brings to our table, approach them with a critical lens, and adapt our strategies to nurture an equitable, AI-literate generation.
Part 2: Assessing Process Over Product in the Age of AI

The following is a synthesis of comments made during the discussion that ensued after the intial Q & A conversation.

There’s been a long-standing tradition in education of assessing the final product. Be it a project, an essay, or a painting, the emphasis has always been on the end result. But isn’t the journey as significant, if not more so? The time has come for assessments to shift their focus from the finished piece to the process behind its creation. Such an approach would not only value the hard work and thought process of a student but also celebrate their research journey.

Currently, knowledge reproduction assessments rule the roost. Students cram facts, only to regurgitate them during exams. However, the real essence of learning lies in fostering higher-order thinking skills. It’s crucial to design assessments that challenge students to analyze, evaluate, and create. This way, we’re nurturing thinkers and not just fact-repeating robots.

The introduction of AI image generators in classroom projects was met with varied reactions. Some students weren’t quite thrilled with what the AI generated for them. However, this sparked a pivotal dialogue about the value of showcasing one’s process rather than merely submitting an end product.

It became evident that possessing a good amount of subject knowledge positions students better to use AI tools effectively, minimizing misuse. This draws a clear parallel between disciplinary knowledge and sophisticated AI usage. Today, employers prize graduates who can adeptly wield AI. Declining AI usage is no longer a strength but a weakness.

As AI tools constantly evolve and become more sophisticated, we can expect students to step into universities already acquainted with these tools. However, just familiarity isn’t enough. Education must pivot towards fostering honest AI usage and teaching students to discern between appropriate and inappropriate uses.

AI tools, no matter how advanced, are just tools. They might churn out outputs that match a user’s intent, but it’s up to the individual to critically evaluate the AI’s output. Does it align with what you wanted to express? Does it represent your research accurately? Developing a robust AI literacy is paramount to navigate this digital landscape.

We must remember that the act of writing or creating is in itself a learning experience. Merely receiving an AI’s output doesn’t equate to learning. There’s an intrinsic value in the process of creation, an enrichment that often transcends the final product.

To sum it up, as the lines between human ingenuity and AI blur, our educational paradigm must pivot, placing process over product, fostering critical thinking, and embracing the AI wave, all while ensuring we retain our unique human touch in creation. The future beckons, and it’s up to us to shape it judiciously.

Original Blog post: Babies and bathwater: How far will AI necessitate an Assessment revolution?