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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. 

Introduction: The emergence and widespread adoption of generative AI (GenAI) chatbots such as ChatGPT, and programming assistants such as GitHub Copilot, have radically redefined the landscape of programming education. This calls for replication of studies and reexamination of findings from pre-GenAI CS contexts to understand the impact on students. Objectives: Achievement Goals are well studied in computing education and can be predictive of student interest and exam performance. The objective in this study is to compare findings from prior achievement goal studies in CS1 courses with new CS1 courses that emphasize the use of human-GenAI collaborative coding. Methods: In a CS1 course that integrates GenAI, we use linear regression to explore the relationship between achievement goals and prior experience on student interest, exam performance, and perceptions of GenAI. Results: As with prior findings in traditional CS1 classes, Mastery goals are correlated with interest in computing. Contradicting prior CS1 findings, normative goals are correlated with exam scores. Normative and mastery goals correlate with students’ perceptions of learning with GenAI. Mastery goals weakly correlate with reading and testing code output from GenAI.