Abstract Patients with cancer and their caregivers face challenges accessing clinical trials because trial information, eligibility criteria, search, and referral pathways are complex and fragmented. These barriers can contribute to unequal access across racial, ethnic, and language groups. ALEX (Agents Leveraging Empathy for eXams) is a web-based portal that uses AI-enabled virtual health educators with synthesized voices and lip-synced facial animation to deliver trial education and a large language model to simplify trial information for search and referral. AI-generated content and virtual humans require human and community evaluation for accuracy, authenticity, and trust. This study examined how community feedback informed the iterative design and refinement of ALEX. The dataset included 34 English- and Spanish-language transcripts (about 44 hours) from focus groups and usability interviews using think-aloud procedures to evaluate the portal. The sessions were conducted from 2023 to 2025 at Mayo Clinic, UF-Gainesville, UF-Jacksonville, and the University of Miami. They examined 136 participants’ responses to the virtual health educators and their use of the portal for trial education, search, and referral. Participants reported their race as White (48.5%), Black or African American (47.1%), Native American or Alaska Native (0.7%), or other (3.7%). Separately, 50.7% identified as Hispanic or Latino, and nearly 25% reported Spanish as their primary language. Throughout data collection, feedback was reviewed and translated into design requirements for successive portal updates. We used deductive and inductive qualitative analysis to identify feedback patterns and user needs and reviewed version history to document portal changes. Three of the six themes derived from 1,653 coded excerpts were Trust Before Trial Search (“Who is Alex?”), Layered and Controllable Education (“If it was more in short bullets, maybe better”), and Motivation, Burden, and Research Trust (“There has to be some trust, some transparency or at least some acknowledgement”). Feedback informed portal refinements, including clearer language and institutional branding and content that was easier to scan. The virtual health educators were evaluated on representation, voice authenticity, lip-sync, warmth, and human likeness. Words like “sponsored study,” “diagnosis,” and “symptoms” guided language refinement. Suggested improvements included sample search terms, easy-to-read study cards, the ability to save results, a question guide, and direct coordinator contact. Research trust, privacy, security, and participation burden remained key priorities. To create an AI-enabled clinical trial portal that meets user needs, we used community feedback to identify modifiable barriers to communication, search, and referral. The goal of this formative process is to transparently incorporate that feedback into trial tools that are more credible, usable, acceptable, and equitable. Future work should evaluate whether these revisions improve referral outcomes. Citation Format: Fan Wang, Rashi Ghosh, Christopher You, Trevor Kauer, Andrew H. Maxim, Courtny Franco, Stephen Anton, Elizabeth Flood-Grady, Trudy Gaillard, Thomas J. George, Benjamin Lok, Carolyn Tucker, Melissa Vilaro, Tuo Lin, Fern Webb, Diana Wilkie, Danyell Wilson-Howard, Olveen Carrasquillo, Janice L. Krieger. Can artificial intelligence amplify community voice? Evidence from a multisite formative evaluation of the AI-Powered ALEX Portal to enhance equity in clinical trial access [abstract]. In: Proceedings of the 19th AACR Conference on the Science of Cancer Health Disparities; 2026 Oct 18-21; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2026;35(10 Suppl):Abstract nr B034.