Effect of Static vs. Conversational AI-Generated Messages on Colorectal Cancer Screening Intent: a Randomized Controlled Trial

The goal of this clinical trial is to assess whether messages generated by a large language model (LLM), including both static and conversational formats, can increase colorectal cancer (CRC) screening intentions among U.S. adults aged 45-75 who have never completed CRC screening. The main questions it aims to answer are: Do personalized, AI-generated messages increase the self-reported likelihood of completing a stool-based CRC screening test within 12 months? Do they also increase intent to undergo colonoscopy screening within 12 months? Researchers will compare four groups: (1) no message control, (2) expert-written patient education materials, (3) a single AI-generated persuasive message, and (4) a motivational interviewing-style AI chatbot. These comparisons will help assess whether a conversational format offers added benefit over static AI or expert-generated content. Participants will: Be randomly assigned to one of the four study arms Spend at least 3 minutes reading or interacting with their assigned material Complete pre- and post-intervention surveys assessing intent to receive CRC screening Receive messages tailored to their self-reported demographics, including age, political ideology, gender, education, community setting (urban, rural, suburb), self-reported health, and the last time they saw their PCP