In the late 70s, the first of Anne Rice’s Vampire Chronicles books was released. It was later translated into a 90s gothic blockbuster, Interview with the Vampire, staring Brad Pitt and Tom Cruise. Recently, a growing cult-following has emerged in response to the TV adaptation. The first two series focus on Louis de Pointe du Lac’s vampiric perspective, weaving a woeful story of tragic loss, hubris, excess, and privilege, with a side hobby in photography. The latest Rice adaption, The Vampire Lestat, promises a rock and roll adventure in a modern world.
But what do outlandish vampires have to do with education?
Rice was very clear about many aspects of her vampires. A key feature that sets them apart from humans is their complete lack of artistic prowess. Louis engages with photography for decades, but his images are lack lustre, dull, dry… soulless. Lestat is a successful rockstar, but that doesn’t mean that his music is any good. In fact, the showrunners are at great pains to follow Rice’s vision that the vampires simply do not have the human soul required to produce good art.
Perhaps by now, you will have guessed that I am using this as a vehicle to discuss generative AI. It’s coming up more in every part of education and we have lots of resources (below) and multi-sector discussions about it all the time. But it is worth pausing on how we use it in relation to expressing creativity and human-ness.
Lots of people focus on the art, music and film outputs as a category of artistic expression. That is not the only type of expression, however. Just as poetry and novels are artistic, so is our academic writing. We all have unique writing voices that can be easily influenced or overtaken by AI. In our recent student focus groups about AI, many students lamented the loss of their unique voice because of required use of or over-reliance on AI.
Louis and Lestat present a simulacrum of artistic expression, but it is always shallow, soulless and ultimately unfulfilling. Likewise, AI can produce simulated artistic expression, but that doesn’t necessarily imbue it with meaning or human value.
AI and creative expression in research-rich education
These concerns are epistemic and multifaceted. Let’s look at some examples at what this might look like in education.
Creative thinking
The literature tackles critical thinking and AI head on, but many colleagues may be interested in the research on creative thinking and AI. One example drew evidence based on a review of 56 empirical studies. Affordances of AI in the learning activities included creative problem solving and affective and motivational activation through storytelling, scenario design and reflective writing, and dialogic engagement and perspective shifting. Key limitations included over-reliance and creative passivity, and a loss of voice and affective authenticity.
Research demonstrated that thoughtful design and instruction mitigates many of the limitations experienced, with inquiry-based, project-based and iterative-learning approaches, supplemented by dialogic interaction and reflective scaffolding, enhance creative thinking when engaged with AI. With the emergence of AI “humaniser” services (that rewrite AI text to appear more human), these practices are even more appealing and may serve as a touchstone for colleagues rethinking assessment design. There is a lot more nuance to the paper and it is highly-recommended reading!
Qualitative research
Qualitative approaches are used across many degree programmes as part of our research-rich curriculum at Bristol. The use of AI for qualitative research analysis is hotly debated (e.g. applied to focus group and interview transcripts, and using thematic analysis).
Fans of AI praise its pattern recognition functionality and speedy ability to process huge datasets for keywords and themes. Critics are concerned with how much authentic control researchers have, given the black-box nature of generative AI, and known problematics such as algorithmic bias, trustworthiness and accuracy limitations.
Thematic analysis requires insight, imagination and adept contextual interpretation of nuance and emotion. As such, AI may be a useful supplemental tool but probably not one researchers should rely on exclusively. (See suggested readings on this topics below).
Synthetic gaze and ethnographic eyes
Some researchers reflect on the “synthetic gaze” of AI that they see in conflict to human-mediated ethnographic eyes. For example, a researcher in Boston University (Begüm Ergün), designed class activities comparing human-authored visual data to AI-generated imagery, analysing the friction between AI’s probabilistic outputs and students’ lived realities. The activity explores themes of inherent bias, automated knowledge, distinctive human skills and embodied realities. It moves students away from passive AI-generation into a values-rich interrogation of synthetic perception in comparison to the real world.
Art as assessment
Dr Bridget Bradley (Social Anthropology, University of St Andrews) explores disability and difference through creative pedagogic approaches. This includes writing a collaborative AI statement with students and an end of semester quilt-making project. The unit interrogates technoableism and technology’s contradictory capacity for liberation and exploitation. One of the students commented how important the collaborative AI statement was as it was “the first time I was able to have a say in how AI is used”. The quilt-making project encourages students to lean in to emotional engagement in a collaborative activity, one where students have agency and choice, and develop deep metacognitive approaches to valuing human-centred learning.
This alternative learning practice is not just about academic collaboration but about interdependence within the cohort. Often collaborative work is viewed as “less than” in academic contexts while AI is framed as a solution to apply to oneself in response to an unsustainable (stressful) system of assessment. The quilt-making task frames learning around creativity, collaboration, and “slow scholarship”. It is also a form of communicating ideas that otherwise wouldn’t normally be seen in academia.
For Bridget and her students, art and collaboration alleviate Neoliberal structural issues and keep work human. The approach also champions assessment for all as “the joy in human creativity is a form of inclusive teaching”.
Is there a ghost in the machine?
Some of you may be familiar with the philosophical concept purported by Gilbert Ryle’s take on René Decartes’ “mind-body dualism”. This concept found its way into film through the notion that a consciousness may emerge from a machine, as a ghost in the machine. Many sci-fi movies use the concept as metaphor and prophetic warning, that AI may ultimately gain real consciousness, placing humanity at great peril, or offering a new way to understand what defines the human soul.
Right now, however, the perils of accepting the ghost as real or inevitable are a choice. The ghost is currently the reflection of human endeavour encoded and co-opted in the black-box heart of generative AI, from stolen and otherwise acquired intellectual property. Artists, musicians and writers despair at the theft and reuse of their work, used uncaringly to create endless simulacrums. The use of AI in this way is not a requirement that any of us are beholden to, but it is very trendy and actively encouraged by leaders in many sectors. We do not have to fall into the trap of relying on AI to replace human expression, these things are not actually inevitable despite what AI-corporations may propagandise. Any adoption of AI is a choice that we all much make with careful reflection of its appropriateness in context.
There are many ways to leverage AI to support human endeavour. AI-generated creative content is, however, not real in the same way as human-content is real. Going forward, we need to be clear on the pedagogic choices we make for our students so that we can champion the development of their unique voice and expression, and associated skills development, without undesirable compromises and unintended consequences because of how we choose to use AI.
Note
I recognise that AI is not all one thing, there is lots of nuance and definitions from LLMs to NLPs to ANI and AGI, multimodel, deep learning, and many many more. For the ease of the reader, I am a light touch on this in the blog. You can find out a lot more in the new Futurelearn course AI Fundamentals.
Check out our education focused resource via Develop, perfect for teaching and professional service staff. Dip in and out of relevant sections and explore the many case studies included, and tips on prompt engineering.
Further reading
- Naeem, M., Smith, T., & Thomas, L. (2025). Thematic Analysis and Artificial Intelligence: A Step-by-Step Process for Using ChatGPT in Thematic Analysis. International Journal of Qualitative Methods, 24. https://doi.org/10.1177/16094069251333886
- Awais Hameed Khan, Hiruni Kegalle, Rhea D’Silva, Ned Watt, Daniel Whelan-Shamy, Lida Ghahremanlou, Liam Magee. (2024). Automating Thematic Analysis: How LLMs Analyse Controversial Topics. Microsoft Journal for Applied Research, Vol 21 (2024), pp 69 – 87 https://doi.org/10.48550/arXiv.2405.06919
- Xu, W. (2026). Doing Thematic Analysis in the Age of Generative AI: Practices, Ethics and Reflexivity. International Journal of Qualitative Methods, 25. https://doi.org/10.1177/16094069261425173
- Hitch D. Artificial Intelligence Augmented Qualitative Analysis: The Way of the Future? Qualitative Health Research. 2024;34(7):595-606. doi:10.1177/10497323231217392
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