让专家用协作工具快速生成高质量教育内容，效率提升数十倍。
PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content Creation
- 设计协作界面支持专家快速试错修改提示词
- 专家生成内容质量媲美人工，创作时间从月级缩短至小时级
- 适合教育领域专家快速落地AI辅助内容生产
让学科专家参与提示词工程，可引导大模型生成更精准、实用且符合特定领域需求的内容。然而，在缺乏系统化界面支持的情况下，有效提示词的迭代仍具挑战性。本文提出 PromptHive，一个专为提示词协作设计的界面，通过支持快速试错提示变体，加强领域知识与提示工程的结合。我们邀请10位数学领域专家开展两轮协作提示编写，并对358名学习者进行学习成效研究。结果揭示了提示迭代过程，验证了工具可用性：非AI专家能生成质量接近人工内容的材料，认知负荷减半，创作周期从数月缩短至数小时。
原文摘要 · Abstract (English)
Involving subject matter experts in prompt engineering can guide LLM outputs toward more helpful, accurate, and tailored content that meets the diverse needs of different domains. However, iterating towards effective prompts can be challenging without adequate interface support for systematic experimentation within specific task contexts. In this work, we introduce PromptHive, a collaborative interface for prompt authoring, designed to better connect domain knowledge with prompt engineering through features that encourage rapid iteration on prompt variations. We conducted an evaluation study with ten subject matter experts in math and validated our design through two collaborative prompt-writing sessions and a learning gain study with 358 learners. Our results elucidate the prompt iteration process and validate the tool's usability, enabling non-AI experts to craft prompts that generate content comparable to human-authored materials while reducing perceived cognitive load by half and shortening the authoring process from several months to just a few hours.
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