When I started collaborating with Dr. Rebecca Feasey and Eu Jin See to record an episode of Jisc’s Beyond the Technology podcast, I expected a good discussion about Adobe Firefly and its role in creative education. What I didn’t expect was a conversation that would shift how I think about creativity—and how AI might help us support it more meaningfully across disparate disciplines.
Rebecca, a media subject leader at Bath Spa University, and Eu Jin, a Master’s student in architecture at Manchester Metropolitan University, brought different perspectives but shared a lot of common ground. What emerged was a thoughtful, practical conversation about how AI can support creative thinking, reflection, and communication.
Creativity as a Social Process
One of the first things Rebecca shared was how she uses AI during Welcome Week. Using Adobe Firefly, students collaborate on poster designs, experiment with prompts, and start to build confidence in expressing ideas visually. It’s a way of breaking the ice and encouraging students to talk through their creative ideas.
That idea of creativity as something social and communicative came up repeatedly. Whether it’s designing a building or crafting a media campaign, creativity is about taking an idea in one person’s head and helping it land resoundingly with someone else. AI, in this context, becomes a bridge—not a shortcut.
Iteration and Exploration
Eu Jin described two distinct modes of iteration that he and his peers and colleagues use in their creative workflows within the context of architecture. The first is the feedback loop—a more linear, step-by-step process where each AI-supported design builds on the last, often in response to critique or reflection (e.g. from a client or a tutor). This approach is particularly useful when refining a concept with a clear direction in mind. The second is bulk ideation, which he likened to casting a wide net. Here, the goal is to generate a broad range of architectural possibilities quickly, especially when the creative direction is still uncertain. This can be useful when helping clients to crystalise their design visions.
Rebecca made the point that AI is often most useful when students don’t yet know what they want to create. It gives them a low-stakes way to explore, test, and refine ideas—quickly and confidently. This early experimentation helps students commit to a direction with more clarity and purpose.
Showing Your Workings: A Lesson for Academic Integrity
One of the most compelling parts of the conversation was around the idea of “showing your workings.” Rebecca compared it to maths—where you don’t just give the answer, you show how you got there. In creative work, this means documenting the process: the prompts used, the iterations explored, the decisions made.
This principle has broader implications, which go beyond creative disciplines. Given the challenges AI poses to academic integrity, the approach of showing your workings has the potential to make assessments less susceptible to inappropriate uses of AI. Portfolio-based assessments, which both Rebecca and Eu Jin championed, have the potential to make learning more visible and verifiable. Within this paradigm, the process of developing a piece of work becomes a key part of the assessment, which would no longer focus solely on the finished product.
Domain Expertise and the Role of AI
Another key theme was the importance of domain expertise—especially when students are working on real-world briefs or with external clients.
Rebecca shared a powerful example of students at Bath Spa designing visual campaigns around mental health. Students used AI to challenge stereotypical portrayals often seen on social media, replacing them with more accurate, ethical, and inclusive representations. In doing so they channeled both creative skills, and a strong understanding of the issues and the intended audiences.
Ethics, Attribution, and the Risk of Stagnation
One of the more nuanced parts of the conversation centred on how AI tools like Firefly can prompt ethical reflection—not just in what students create, but in how they think about representation and authorship.
Rebecca shared that when students are asked to generate images, they often default to certain assumptions—such as depicting an older white male when asked to visualise a professional figure. These moments become opportunities to pause, reflect, and recalibrate. Students are encouraged to ask: Whose story am I telling? Am I the right person to tell it? And am I doing so in a way that is inclusive and meaningful, rather than tokenistic?
This kind of reflection is supported by Firefly’s design. Because it’s trained on Adobe Stock and includes built-in attribution tools, it encourages transparency. Both Rebecca and Eu Jin stressed the importance of students crediting AI-generated content in their coursework, just as they would cite a source in an essay. Attribution isn’t just about academic integrity—it’s about being honest about the tools and processes behind the work.
Eu Jin also raised a concern that’s becoming more common in creative circles: the risk of aesthetic homogenisation. He referenced the Studio Ghibli trend, where people use AI to generate images in a recognisable style. While technically impressive, these outputs often lack the personal touch—the human story—that gives creative work its depth. The challenge therefore is to use AI not to mimic, but to amplify one’s own voice.
Together, these reflections point to a broader theme: AI is not just a tool for churning out finished products—it’s a tool that runs alongside the whole creative journey. Used well, it can help students become more reflective, more transparent, and more intentional in their creative practice.
Final Thoughts
As we wrapped up, I found myself reflecting on something Rebecca said about Sir Ken Robinson: that we’ve spent years devaluing creativity in education, and then we’re surprised when students don’t see themselves as creative. Tools like Firefly, she suggested, might help bring that creativity back—not by doing the work for students, but by giving them new ways to explore and express their ideas.
Eu Jin echoed that sentiment. You don’t know the designs you’re missing out on until you’ve tried. AI opens up new dimensions—not by replacing traditional methods, but by expanding what’s possible.
This conversation left me with a lot to think about—not just about AI, but about how we teach, assess, and support creativity in all its forms.
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