Intermediate Representations in Human–AI Creative Collaboration: A Framework for Distributed Creative Cognition

Generative artificial intelligence has dramatically reduced the effort required to produce visual artifacts, intensifying questions about which human practices remain educationally valuable when computational systems can generate images almost instantly. Much discussion of human–AI creative collaboration begins with text-based prompting. This conceptual paper argues that collaboration often begins earlier, while intentions are still being formed through observation, sketching, reflection, and other intermediate representations. The framework developed here was motivated by reflective teaching practice and observation across repeated offerings of an elective course in art and design education. Classroom experiences are used as illustrative context rather than as evidence of causal effects. Drawing on embodied cognition, distributed cognition, distributed intelligence, and boundary-object theory, the paper conceptualizes sketches as evolving cognitive interfaces that externalize partially formed ideas, preserve productive ambiguity, and provide reference points against which AI interpretations can be accepted, rejected, or revised. Two complementary models describe a sketch-mediated creative cycle and the broader distributed cognitive system in which that cycle operates. The framework suggests that the educational value of sketching may increasingly lie not in artifact production but in supporting intention formation, reflective judgment, human agency, and productive friction before and during AI interaction. The paper concludes with propositions for future empirical research on sketch-first and other representation-mediated forms of human–AI collaboration.