Robots and artificial intelligence (AI) have seen increased adoption in healthcare. These health technologies have the capability of providing tailored messages and feedback to each individual. Thus, a robot can potentially serve as a personal health advisor, particularly for health issues that could be benefited through regular guidance and instructions. However, there is a limited understanding of how people might respond to the idea that their health advisor could be a robot. Thus, the present study employs the technology acceptance model (TAM) to examine intentions to adopt a robotic health advisor. Findings demonstrate that perceived ease of communication with and perceived usefulness of a robotic health advisor positively predict favorable attitudes toward a robotic health advisor, which subsequently leads to strong intentions to adopt it. The present investigation also finds that perceived usefulness of a robotic health advisor directly leads to an individual’s intentions to adopt it. Overall, the present study provides important implications for perceptions of a robotic health advisor.

Various interventions have been suggested to aid in smoking cessation. However, little is known about the effects of message framing in narratives embedded in serious games. This study compares when an individual experiences unfortunate events from smoking (i.e., loss frame) versus fortunate results benefited from smoking cessation (i.e., gain frame) in a computer game through a model (i.e., virtual self-modeling) that looks like oneself or a stranger. An experiment (N = 64) using a 2 (Message framing: Gain vs. Loss) x 2 (Modeling: Self vs. Other) between-subjects design was conducted using an anti-smoking game. Results show that the gain frame induces stronger perceived susceptibility compared to the loss frame, and self-modeling is more effective than other-modeling. Results further demonstrate that the virtual misfortune experienced through one’s own face, compared to someone else’s face, is significantly more likely to increase one’s susceptibility to the negative consequences of smoking. The study also finds a significant mediating role of identification between framing and susceptibility. Overall, by demonstrating the effectiveness of the self-modeling and gain-framed messages in gameplay, the present investigation provides meaningful contributions to the use of technology for effective health communication.

New advancements in technology have made machines teachers, or technology-powered robots or AI that assist in the overall learning experience, a possibility. Though adoption rates are currently low, colleges and universities will likely incorporate some aspects of machine teachers (e.g., AI, robots) in their curriculums in the foreseeable future. However, little is known about how to create an effective machine teacher-based education. As an initial step, the present study examines whether an AI instructor’s communication style would have an impact on students’ perceptions about an AI instructor-based education. To test this inquiry, the study conducted an online experiment using a 2 (communication styles: functional vs. relational) x 2 (course topic: natural science vs. social science) between-subjects design. Primary results indicate that students develop more favorable perceptions about an AI instructor-based education when the AI instructor is relational rather than functional. This tendency is particularly strong when listening to a social science lecture. Further, social presence of an AI instructor functions as a mediator, which explains the reason why a relational AI instructor leads to more favorable perceptions about an AI instructor-based education is because of one’s social presence of an AI instructor. Collectively, the study’s findings indicate the importance of communication styles and social presence of an AI instructor.

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.