Plain Language Summarization of Environmental Health Research Using Generative AI: Community-Engaged Qualitative Study.

BACKGROUND: Plain language summaries (PLSs) are increasingly required in environmental health publications to improve public accessibility. Generative artificial intelligence (AI) systems such as large language models can automatically produce such summaries; however, automated summaries often overlook local context and cultural relevance-limitations that are critical in environmental health research, where affected communities face disproportionate exposure and health risks. OBJECTIVE: This study aimed to develop and refine community-informed prompts for generating PLSs of environmental health research using generative AI. METHODS: We conducted a community-engaged qualitative study in Louisville, Kentucky, involving workshops with 97 participants from 4 stakeholder groups: summer interns at a local social justice nonprofit organization, participants in a youth development program, participants in a faith-based community organization, and members of a community advisory board focused on environmental justice. Participants reviewed PLSs generated using 3 different prompt styles in GPT-4o (ChatGPT, OpenAI). Feedback was collected through structured discussions and facilitator notes. Data were analyzed using thematic analysis informed by knowledge translation frameworks to identify preferred structural, linguistic, and contextual features of the summaries. RESULTS: Participants consistently preferred summaries between 300 and 400 words, written at a sixth- to eighth-grade reading level. Key priorities included presenting definitions before findings, using headings and bullet points to improve readability, and clearly explaining real-world and environmental justice implications. Narrative summaries without structure were viewed as overly long and difficult to interpret, while purely bullet-based formats were considered too simplified. Feedback from youth participants emphasized clarity and practical relevance, while faith-based participants highlighted the importance of trust and contextual framing. These insights informed the development of a community-refined prompt that included definitions, key findings, an introduction, and a concluding section on community implications. CONCLUSIONS: Community-engaged prompt development may improve the relevance and interpretability of AI-generated PLSs of environmental health research. Incorporating stakeholder perspectives into prompt design offers a replicable strategy for improving research translation and ensuring AI-generated summaries reflect the informational needs of affected communities.