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Producing open-ended creative work through crowdsourcing remains a challenge, as workers often lack domain expertise, and requesters struggle to provide scalable guidance. Can the workers themselves create materials that guide subsequent workers? In this paper, we prototype a workflow for emergent scaffolding, where hints, rubrics, and examples are generated by crowd workers after attempting the task. We demonstrate how an iterative Train-Try-Reflect-Synthesize pattern—supported by LLMs—can produce a structured rubric with graded examples to guide subsequent workers on a task to create digital illustrations for scientific papers. To evaluate this strategy, we conducted a between-subjects experiment with three conditions: baseline instructions, generic examples, and emergent scaffolding. Participants in the emergent scaffolding condition created significantly better illustrations, as rated by blind-to-condition judges, compared to generic examples or instructions only. While self-efficacy ratings were mixed across conditions, emergent scaffolding participants provided better feedback during their post-task reflections. We discuss the potential for emergent scaffolding to support and scale up complex, creative tasks in crowdwork.