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Social media are quickly becoming the channel of choice for disseminating emergency warning messages. However, relatively little data-driven research exists to inform effective message design when using these media. The present study addresses that void by examining terse health-related warning messages sent by public safety agencies over Twitter during the 2013 Boulder, CO, floods. An examination of 5,100 tweets from 52 Twitter accounts over the course of the 5-day flood period yielded several key conclusions and implications. First, public health messages posted by local emergency management leaders are most frequently retweeted by organizations in our study. Second, emergency public health messages focus primarily on drinking water in this event. Third, terse messages can be designed in ways that include imperative/instructional and declarative/explanatory styles of content, both of which are essential for promoting public health during crises. These findings demonstrate that even terse messages delivered via Twitter ought to provide information about the hazard event, its impact, and actionable instructions for self-protection.

An increase in demand for online education has led to the creation of a new technology, machine teachers, or artificial intelligence (AI) teaching assistants. In fact, AI teaching assistants have already been implemented in a small number of courses in the United States. However, little is known about how students will perceive AI teaching assistants. Thus, the present study investigated students' perceptions about AI teaching assistants in higher education by use of an online survey. Primary findings indicate that perceived usefulness of an AI teaching assistant and perceived ease of communication with an AI teaching assistant are key to understanding an eventual adoption of AI teaching assistant-based education. These findings provide support for AI teaching assistant adoption. Based on the present study's findings, more research is needed to better understand the nuances associated with the learning experience one may have from an AI teaching assistant.

This article explores the nature of instructional communication in responding to crisis situations. Through the lens of chaos theory, the relevance of instructional messages in restoring order is established. This perspective is further advanced through an explanation of how various learning styles impact the receptivity of various instructional messages during the acute phase of crises. We then summarize an exploratory study focusing on the relationship between learning styles and the demands of instructional messages in crisis situations. We conclude the article with a series of conclusions and implications.