A theory and evidence-based artificial intelligence-driven motivational digital assistant to decrease vaccine hesitancy : intervention development and validation

Abstract

Vaccine hesitancy is one of the top ten threats to global health. Artificial intelligence-driven chatbots and motivational interviewing skills show promise in addressing vaccine hesitancy. This study aimed to develop and validate an artificial intelligence-driven motivational digital assistant in decreasing COVID-19 vaccine hesitancy among Hong Kong adults. The intervention development and validation were guided by the Medical Research Council’s framework with four major steps: logic model development based on theory and qualitative interviews (n = 15), digital assistant development, expert evaluation (n = 5), and a pilot test (n = 12). The Vaccine Hesitancy Matrix model and qualitative findings guided the development of the intervention logic model and content with five web-based modules. An artificial intelligence-driven chatbot tailored to each module was embedded in the website to motivate vaccination intention using motivational interviewing skills. The content validity index from expert evaluation was 0.85. The pilot test showed significant improvements in vaccine-related health literacy (p = 0.021) and vaccine confidence (p = 0.027). This digital assistant is effective in improving COVID-19 vaccine literacy and confidence through valid educational content and motivational conversations. The intervention is ready for testing in a randomized controlled trial and has high potential to be a useful toolkit for addressing ambivalence and facilitating informed decision making regarding vaccination.