Abstract
Recent times have seen increasing interest in conversational assistants (e.g., Amazon Alexa) designed to help users in their daily tasks. In military settings, it is critical to design assistants that are, simultaneously, helpful and able to minimize the user’s cognitive load. Here, we show that embodiment plays a key role in achieving that goal. We present an experiment where participants engaged in an augmented reality version of the relatively well-known desert survival task. Participants were paired with a voice assistant, an embodied assistant, or no assistant. The assistants made suggestions verbally throughout the task, whereas the embodied assistant further used gestures and emotion to communicate with the user. Our results indicate that both assistant conditions led to higher performance over the no assistant condition, but the embodied assistant achieved this with less cognitive burden on the decision maker than the voice assistant, which is a novel contribution. We discuss implications for the design of intelligent collaborative systems for the warfighter.
Highlights
In the near future, humans will be increasingly expected to team up with artificially intelligent (AI) non-human partners to accomplish organizational objectives (Davenport and Harris, 2005; Bohannon, 2014)
The analysis revealed a main effect for the assistant conditions, F(2,66) = 10.166, p < 0.001, η2p = 0.236
The results suggest that assistants were able to improve participants’ performance, confirming hypothesis H1b; we found no support that embodied assistants improved performance when compared to voice-only assistants
Summary
Humans will be increasingly expected to team up with artificially intelligent (AI) non-human partners to accomplish organizational objectives (Davenport and Harris, 2005; Bohannon, 2014). This vision is motivated by rapid progress in AI technology that supports a growing range of applications, such as self-driving vehicles, automation of mundane and dangerous tasks, processing large amounts of data at superhuman speeds, sensing the environment in ways that humans cannot (e.g., infrared), and so on. This is a difficult challenge as humans, on the one hand, do not fully understand how AI works and, on the other hand, are often already overburdened by the task (Lee and See, 2004; Hancock et al, 2011; Schaefer et al, 2016).
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