Evidence map›Paper›PMID 37711593›Full record

ArticleFrontiers in robotics and AI2023

Seeing eye to eye: trustworthy embodiment for task-based conversational agents.

David A Robb, José Lopes, Muneeb I Ahmad, Peter E McKenna, Xingkun Liu, Katrin Lohan, Helen Hastie

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

David A RobbDepartment of Computer Science, Heriot-Watt University, Edinburgh, United Kingdom.
José LopesDepartment of Computer Science, Heriot-Watt University, Edinburgh, United Kingdom.
Muneeb I AhmadDepartment of Computer Science, Swansea University, Swansea, United Kingdom.
Peter E McKennaDepartment of Psychology, Heriot-Watt University, Edinburgh, United Kingdom.
Xingkun LiuDepartment of Computer Science, Heriot-Watt University, Edinburgh, United Kingdom.
Katrin LohanEastern Switzerland University of Applied Sciences, Buchs SG, Switzerland.
Helen HastieDepartment of Computer Science, Heriot-Watt University, Edinburgh, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Smart speakers and conversational agents have been accepted into our homes for a number of tasks such as playing music, interfacing with the internet of things, and more recently, general chit-chat. However, they have been less readily accepted in our workplaces. This may be due to data privacy and security concerns that exist with commercially available smart speakers. However, one of the reasons for this may be that a smart speaker is simply too abstract and does not portray the social cues associated with a trustworthy work colleague. Here, we present an in-depth mixed method study, in which we investigate this question of embodiment in a serious task-based work scenario of a first responder team. We explore the concepts of trust, engagement, cognitive load, and human performance using a humanoid head style robot, a commercially available smart speaker, and a specially developed dialogue manager. Studying the effect of embodiment on trust, being a highly subjective and multi-faceted phenomena, is clearly challenging, and our results indicate that potentially, the robot, with its anthropomorphic facial features, expressions, and eye gaze, was trusted more than the smart speaker. In addition, we found that embodying a conversational agent helped increase task engagement and performance compared to the smart speaker. This study indicates that embodiment could potentially be useful for transitioning conversational agents into the workplace, and further

Indexed as

autonomous systemscognitive loadconversational agenthuman–robot teamingremote robotssocial roboticsuser engagement

Identifiers

PMID37711593
PMCPMC10499495

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.