Returning to teaching after the winter break is a bit like opening the fridge after Christmas. You’re hopeful, you’re curious, and you’re fairly certain something inside has gone bad. Despite the rigmarole of working in Higher Education today, I arrive each January pessimistically excited.
That optimism rarely survives the first week. Students often return with little or no work, as if the holiday spirit included a firm ban on thinking. My disappointment quickly escalates into panic as I calculate the workload ahead for them, and then, because I am human, I think of how this will all somehow become my problem.
The university and the governing bodies above it increasingly appear blissfully unaware of what success actually requires: what must be taught, what must be learned, and how much pressure this places on students and staff within the very limited time we’re given to prepare them for industry. Their detachment is impressive.
Each year, I naïvely hope students will be motivated by a genuine interest in their chosen subject and engage independently over the break. While it’s tempting to label them lazy, I know the reality is messier. Social, cultural, and technological distractions crowd their attention, elbowing out independent learning, accountability, and sustained focus.
What truly surprises me, though, is that today’s students possess unprecedented resources, most notably AI tools, and yet hesitate to use them creatively. Many believe AI is “cheating,” a perception shared by some colleagues who clutch their sketchbooks and pencils like emotional support animals and refuse to accept digital realities. This is despite research showing generative AI can enhance creativity rather than replace it, and that prompt literacy, learning how to articulate design thinking effectively to AI, is rapidly becoming an essential industry skill (Lee & Suh, 2024).
Over the past year, I’ve experimented with AI myself, poking at it cautiously. As Khairulanwar and Jamaludin argue, AI is not a threat but a tool that can enhance both art and education, particularly for students who struggle with traditional drawing skills, by helping them externalise ideas and iterate faster (Khairulanwar & Jamaludin, 2025). Other institutions are already exploring this terrain. As one of the world’s leading art schools and fashion programmes, we arguably have a responsibility not to hide under the desk but to speculate, challenge, disrupt and mangle its capabilities to achieve new ground.
AI has limitations: it can’t do the actual work for you so we shouldn’t fear it, though the environmental and ethical implications are real. Still, hybrid approaches, combining digital and physical practices, are increasingly central to contemporary fashion design. Used thoughtfully, AI can support experimentation, problem-solving, collaboration, and iteration.
As I look ahead on the PGCert programme, I wonder whether integrating AI more intentionally into teaching might help students produce braver, more ambitious work. My goal isn’t to replace traditional skills, but to expand creative horizons, so students can survive, and maybe even thrive, in an evolving fashion industry that shows no intention of slowing down for anyone.
References
Cooke, D., Edwards, A., Barkoff, S. & Kelly, K., 2025. As Good as a Coin Toss: Human Detection of AI-Generated Content. Communications of the ACM, 68(10).
Jung, D. & Suh, S., 2024. Enhancing soft skills through generative AI in sustainable fashion textile design education. Sustainability, 16(16), p.6973.
Khairulanwar, A.B.M. & Jamaludin, K.A., 2025. Artificial Intelligence in Fashion Design Education: A Phenomenological Exploration of Certificate-Level Learning. International Journal of Academic Research in Business and Social Sciences, 15(3), pp.313–331.
Lee, J. & Suh, S., 2024. AI Technology Integrated Education Model for Empowering Fashion Design Ideation. Sustainability, 16(17), p.7262.
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