Evidence map›Paper›PMID 34095237›Full record

ArticleFrontiers in robotics and AI2021

Parental Acceptance of Children's Storytelling Robots: A Projection of the Uncanny Valley of AI.

Chaolan Lin, Selma Šabanović, Lynn Dombrowski, Andrew D Miller, Erin Brady, Karl F MacDorman

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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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

6 authors.

Chaolan LinDepartment of Cognitive Science, University of California, San Diego, CA, United States.
Selma ŠabanovićThe Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States.
Lynn DombrowskiThe School of Informatics and Computing, Indiana University, Indianapolis, IN, United States.
Andrew D MillerThe School of Informatics and Computing, Indiana University, Indianapolis, IN, United States.
Erin BradyThe School of Informatics and Computing, Indiana University, Indianapolis, IN, United States.
Karl F MacDormanThe School of Informatics and Computing, Indiana University, Indianapolis, IN, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parent-child story time is an important ritual of contemporary parenting. Recently, robots with artificial intelligence (AI) have become common. Parental acceptance of children's storytelling robots, however, has received scant attention. To address this, we conducted a qualitative study with 18 parents using the research technique design fiction. Overall, parents held mixed, though generally positive, attitudes toward children's storytelling robots. In their estimation, these robots would outperform screen-based technologies for children's story time. However, the robots' potential to adapt and to express emotion caused some parents to feel ambivalent about the robots, which might hinder their adoption. We found three predictors of parental acceptance of these robots: context of use, perceived agency, and perceived intelligence. Parents' speculation revealed an uncanny valley of AI: a nonlinear relation between the human likeness of the artificial agent's mind and affinity for the agent. Finally, we consider the implications of children's storytelling robots, including how they could enhance equity in children's access to education, and propose directions for research on their design to benefit family well-being.

Indexed as

artificial intelligencedesign fictionparent-child storytellingsocial roboticstechnology acceptanceuncanny valley

Identifiers

PMID34095237
PMCPMC8172185

What OpenQuestion holds

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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.