For centuries, universities have delivered scarce expertise. We stacked programmes like layer cakes: first theory, then practice, finally – if there was time – a sprinkle of creativity. Generative AI flips that order. Because routine skills are on tap, the bottleneck shifts upstream to ideation: spotting problems worth solving and framing them so the machine can help.
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That demands divergent thinking, curiosity and ethical judgement – qualities our assessment regimes often squeeze out. We need to treat creativity as a core literacy, not a decorative extra. Don’t get me wrong, skills are not irrelevant – they just look different. Prompt craft, data stewardship and model critique replace manual citation and calculator drills. But they are means, not ends.
