The Role of AI in advancing the principles and practice of community engagement: a scoping review

Rationale As defined in the Principles of Community Engagement (3rd Edition), “community engagement builds sustainable relationships through trust and collaboration, strengthening community well-being. The process should be enduring, equitable, and culturally sensitive to all participants with a shared goal of addressing the concerns of the community.”1 In this scoping review, we will focus specifically on when this engagement occurs between an academic institution and a community experiencing health disparities. The community must have members who identify as one or more of the populations experiencing health disparities (American Indians/Alaska Natives, Asian Americans, Blacks/African Americans, Hispanics/Latinos, Native Hawaiians and other Pacific Islanders, sexual and gender minorities, socioeconomically disadvantaged, or underserved rural). Community engagement can be challenging, and thus it is important to use all available tools to build trusting, sustainable relationships. As the use of artificial intelligence (AI) grows, we must examine how AI can be leveraged to advance the principles of community engagement among academic partnerships working to address health disparities. We focused on two types of AI: generative AI (GenAI) (“AI that produces a variety of content, including text, imagery, and audio”) and large language models (LLMs) (“a subset of GenAI that specializes in interpreting and generating human language to create text”).2 Specifically, we want to examine if and how AI has been used to advance the following principles of community engagement: 1) Be clear about the purposes or goals of the engagement effort and the populations and/or communities you want to engage. 2) Become knowledgeable about the community’s culture, economic conditions, social networks, political and power structures, norms and values, demographic trends, history and experiences with efforts by outside groups. Be aware of each other’s perceptions of past engagement activities. 3) Build and maintain relationships and trust by working with individuals and/or community leaders. 4) Remember and accept that collective self-determination is the responsibility and right of all people in a community. No external entity should assume it can bestow on a community the power to act in its own self-interest. 5) Establish partnership with the community to create change and improve health. 6) Recognize and respect the diversity within the community. 7) Identify and mobilize community assets and strengths through developing the community’s capacity and resources to make decisions and take actions. 8) Recognize that individuals and institutions must be prepared to release control and be sufficiently flexible to meet changing needs. 9) Foster community collaboration and strengthen long-term commitment among the partners. 10) Demonstrating trustworthiness is fundamental to sustaining successful community engagement. While there are numerous examples of how to collaborate with community members in the development of AI products,3-5 there are few examples of how to leverage AI as a tool for community engagement. Thus, the objective of our scoping review is to map the current state of both gray and peer-reviewed literature around how AI can be used to further the principles of community engagement in health-related research. Objective To explore the role of AI in advancing the principles and practice of community engagement among academic partnerships addressing health disparities.