Artificial Intelligence for Health Message Generation: Theory, Method, and an Empirical Study Using Prompt Engineering

TL;DR

This paper explores AI-generated health messages using prompt engineering, demonstrating that AI can produce messages comparable or superior to human ones in quality, clarity, and semantic content, with implications for health communication.

Contribution

It introduces a novel AI system for health message generation using prompt engineering and evaluates its effectiveness compared to human-generated messages.

Findings

AI messages matched human messages in sentiment and readability

AI messages ranked higher in quality and clarity in human evaluations

The system was easy to use and produced prolific outputs

Abstract

This study introduces and examines the potential of an AI system to generate health awareness messages. The topic of folic acid, a vitamin that is critical during pregnancy, served as a test case. Using prompt engineering, we generated messages that could be used to raise awareness and compared them to retweeted human-generated messages via computational and human evaluation methods. The system was easy to use and prolific, and computational analyses revealed that the AI-generated messages were on par with human-generated ones in terms of sentiment, reading ease, and semantic content. Also, the human evaluation study showed that AI-generated messages ranked higher in message quality and clarity. We discuss the theoretical, practical, and ethical implications of these results.