PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content Creation
Mohi Reza, Ioannis Anastasopoulos, Shreya Bhandari, Zachary A. Pardos
Abstract
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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Cited by top-tier papers4
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- Codesigning Ripplet: an LLM-Assisted Assessment Authoring System Grounded in a Conceptual Model of Teachers' WorkflowsYuan Cui, Annabel Marie Goldman, Jovy Zhou, Xiaolin Liu et al.CHI 2026 · 1 citation
- Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based ProvocationsTzu-Sheng Kuo, Sophia Liu, Quan Ze Chen, Joseph Seering et al.CHI 2026 · 1 citation
- Data-Prompt Co-Evolution: Growing Test Sets to Refine LLM BehaviorMinjae Lee, Minsuk KahngCHI 2026 · 1 citation
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- The Deskilling of Domain Expertise in AI DevelopmentNithya Sambasivan, Rajesh VeeraraghavanCHI 2022 · 79 citations
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