PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content Creation
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 18 of its 66 references that are in the map. First posted on arXiv 21 Oct 2024.
Builds on
18 of 66 references.
References · 18
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
- 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 182
- 2024The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performancecited by 97
- 2023The Metacognitive Demands and Opportunities of Generative AIcited by 85
- 2023Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Modelscited by 67
- 2022Large-scale Text-to-Image Generation Models for Visual Artists' Creative Workscited by 60
- 2024Generative Students: Using LLM-Simulated Student Profiles to Support Question Item Evaluationcited by 56
- 2023Learning gain differences between ChatGPT and human tutor generated algebra hintscited by 42
- 2024Leveraging LLM-Respondents for Item Evaluation: a Psychometric Analysiscited by 31
- 2024From Explainable to Interactive AI: A Literature Review on Current Trends in Human-AI Interactioncited by 26
- 2023Assessing the Quality of Multiple-Choice Questions Using GPT-4 and Rule-Based Methodscited by 23
6 more
- 2023Evaluating ChatGPT's Decimal Skills and Feedback Generation in a Digital Learning Gamecited by 19
- 2023ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Modelscited by 17
- 2024One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Promptscited by 16
- 2024Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoningcited by 13
- 2024Wordflow: Social Prompt Engineering for Large Language Modelscited by 9
- 2024It Cannot Be Right If It Was Written by AI: On Lawyers' Preferences of Documents Perceived as Authored by an LLM vs a Humancited by 5
Built on by
Citing papers per year: 2025: 6; 2026: 3.
9 papers.
- 2025Temperature and Persona Shape LLM Agent Consensus With Minimal Accuracy Gains in Qualitative Codingcited by 3
- 2025When the Domain Expert Has No Time and the LLM Developer Has No Clinical Expertise: Real-World Lessons from LLM Co-Design in a Safety-Net Hospitalcited by 2
- 2025Understanding Prompt Programming Tasks and Questionscited by 1
- 2025Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocationscited by 1
- 2025Data-Prompt Co-Evolution: Growing Test Sets to Refine LLM Behaviorcited by 1
- 2026The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotationcited by 1
- 2026Key Considerations for Domain Expert Involvement in LLM Design and Evaluation: An Ethnographic Studycited by 1
- 2025Towards Synergistic Teacher-AI Interactions with Generative Artificial Intelligencecited by 0
- 2026The RIGID Framework: Research-Integrated, Generative AI-Mediated Instructional Designcited by 0
- 2026The RIGID Framework: Research-Integrated, Generative AI-Mediated Instructional Design13 Mar 2026
- 2026Key Considerations for Domain Expert Involvement in LLM Design and Evaluation: An Ethnographic Study16 Feb 2026
- 2026The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation11 Feb 2026
- 2025Towards Synergistic Teacher-AI Interactions with Generative Artificial Intelligence24 Nov 2025
- 2025Data-Prompt Co-Evolution: Growing Test Sets to Refine LLM Behavior14 Oct 2025
- 2025Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocations29 Sep 2025
- 2025When the Domain Expert Has No Time and the LLM Developer Has No Clinical Expertise: Real-World Lessons from LLM Co-Design in a Safety-Net Hospital11 Aug 2025
- 2025Understanding Prompt Programming Tasks and Questions23 Jul 2025
- 2025Temperature and Persona Shape LLM Agent Consensus With Minimal Accuracy Gains in Qualitative Coding15 Jul 2025