The use of social robots with children and young people on the

RESEARCH ARTICLE

The use of social robots with children and

young people on the autism spectrum: A

systematic review and meta-analysis

Athanasia Kouroupa

1,2

, Keith R. Laws

, Karen Irvine

, Silvana E. Mengoni

Alister Baird

, Shivani Sharma

1 School of Life and Medical Sciences, University of Hertfordshire, Hatfield, United Kingdom, 2 Division of

Psychiatry, University College London, London, United Kingdom

* [email protected]

Abstract

Background

Robot-mediated interventions show promise in supporting the development of children on

the autism spectrum.

Objectives

In this systematic review and meta-analysis, we summarize key features of available evi-

dence on robot-interventions for children and young people on the autism spectrum aged up

to 18 years old, as well as consider their efficacy for specific domains of learning.

Data sources

PubMed, Scopus, EBSCOhost, Google Scholar, Cochrane Library, ACM Digital Library,

and IEEE Xplore. Grey literature was also searched using PsycExtra, OpenGrey, British

Library EThOS, and the British Library Catalogue. Databases were searched from inception

until April (6th) 2021.

Synthesis methods

Searches undertaken across seven databases yielded 2145 articles. Forty studies met our

review inclusion criteria of which 17 were randomized control trials. The methodological

quality of studies was conducted with the Quality Assessment Tool for Quantitative Studies.

A narrative synthesis summarised the findings. A meta-analysis was conducted with 12

RCTs.

Results

Most interventions used humanoid (67%) robotic platforms, were predominantly based in

clinics (37%) followed home, schools and laboratory (17% respectively) environments and

targeted at improving social and communication skills (77%). Focusing on the most common

outcomes, a random effects meta-analysis of RCTs showed that robot-mediated

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OPEN ACCESS

Citation: Kouroupa A, Laws KR, Irvine K, Mengoni

SE, Baird A, Sharma S (2022) The use of social

robots with children and young people on the

autism spectrum: A systematic review and meta-

analysis. PLoS ONE 17(6): e0269800. https://doi.

org/10.1371/journal.pone.0269800

Editor: Cristina Vassalle, Fondazione Toscana

Gabriele Monasterio, ITALY

Received: March 9, 2022

Accepted: May 30, 2022

Published: June 22, 2022

Peer Review History: PLOS recognizes the

benefits of transparency in the peer review

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editorial history of this article is available here:

https://doi.org/10.1371/journal.pone.0269800

access article distributed under the terms of the

Creative Commons Attribution License, which

permits unrestricted use, distribution, and

reproduction in any medium, provided the original

author and source are credited.

Data Availability Statement: All relevant data are

within the paper and its Supporting Information

files.

interventions significantly improved social functioning (g = 0.35 [95%CI 0.09 to 0.61; k = 7).

By contrast, robots did not improve emotional (g = 0.63 [95%CI -1.43 to 2.69]; k = 2) or

motor outcomes (g = -0.10 [95%CI -1.08 to 0.89]; k = 3), but the numbers of trials were very

small. Meta-regression revealed that age accounted for almost one-third of the variance in

effect sizes, with greater benefits being found in younger children.

Conclusions

Overall, our findings support the use of robot-mediated interventions for autistic children and

youth, and we propose several recommendations for future research to aid learning and

enhance implementation in everyday settings.

PROSPERO registration

Our methods were preregistered in the PROSPERO database (CRD42019148981).

Introduction

With an ongoing focus on early interventions [1, 2], the functioning of children and young

people on the autism spectrum has progressively improved [3]. An array of interventions exists

to support the development of social, emotional and life skills in autistic children but with

mixed evidence of clinical effectiveness [4]. This makes it important to continue to search for

approaches that are adaptable to individual needs given the heterogeneity of the autism spec-

trum, and scalable to advance the most benefit.

Amongst the plethora of interventions, robots have emerged as a promising aid in the

development of everyday skills and as a mechanism to improve quality of life [5, 6]. Recent

studies show that robots are well-accepted by children and young people on the autism spec-

trum and are linked to positive impact on imitation skills, eye-contact, joint attention, beha-

vioural response, and repetitive and stereotyped behaviour [7, 8]. Several reviews have

summarised that individuals on the autism spectrum interact more effectively with robots

than humans to practice life-skills [7, 9, 10]. This advantage has been attributed to stimulation

through repetition, simplified facial expressions that mimic humans, and a gradual increase in

the level of challenge all acting as important scaffolds to mastering skills [9–12]. In other

words, robots offer a predictable and consistent interaction pattern, which is favourable to the

learning of children and young people on the autism spectrum [13–15].

So-called ‘social robots’ have shown advantages in educating children and youth on the

autism spectrum in various domains, including: attention [6], learning [16], behavioural regu-

lation [17, 18] and restricted and repetitive patterns of behaviours [19–21]. Social robots are

described as physically embodied agents that have some (or full) autonomy and engage in

social interactions with humans, by communicating, cooperating, and making decisions [22].

A research study classified robots based on their appearance as human-inspired, animal-

inspired, imaginary, or manmade objects and functional robots (e.g., drones) [23]. Robot plat-

forms benefit from the capacity to represent familiar social cues to children and young people

in a controlled environment (e.g., facial features such as eyes). Technological advances have

also enabled humanoid robots to represent a range of human-like functions, which is impor-

tant for children on the autism spectrum, whose perceptual processing of humans and objects

appears to be similar [24]. The emergence of social robots brings opportunity that innovative

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Funding: The author(s) received no specific

funding for this work.

Competing interests: The authors have declared

that no competing interests exist.

technologies could further aid the development of skills in children and young people on the

autism spectrum through playful activities and that such interaction might positively impact

the learning process [25]. Further, rapid developments in technology mean that interventions

can be more readily personalized, a salient feature given the heterogeneity of idiosyncratic dif-

ficulties in autism [26, 27].

Several intervention programmes have explored the impact of social robots on skills devel-

opment, however evidence concerning their efficacy remains limited [28, 29]. Despite the

potential of robots in autism training, significant gaps persist in the literature. Studies have

mainly focused on reviewing the acceptability of robots to children and young people on the

autism spectrum as well as therapists delivering interventions. Research has overlooked the

variability of robot types used and their efficacy beyond the immediate intervention period [7,

22, 30]. In addition, important features such as the settings in which intervention is delivered,

and characteristics such as the number of sessions needed to bring about meaningful benefit

remains unclear. Examining if and how environment influences the efficacy of robot interven-

tions is fundamental to enhance learning gain and to consider if any setting is more suitable to

overcome the challenge of generalizing skills [30, 31]. It is important to know the outcomes

targeted by robot-mediated interventions and to consider meaningful skills development in

these domains to inform future directions for robotic research in autism, and importantly,

applied clinical value. A recent study reviewed evidence from randomised control trials

(RCTs) with children and adults on the autism spectrum, finding that most utilised humanoid

robots, focusing on outcomes such as job interview skills, gesture production and recognition,

social, mental, physical, and verbal skills [32]. While Salimi and colleagues [32] reported that

15/19 RCTs demonstrated positive gains in targeted skills development, the authors did not

undertake meta-analysis to quantify efficacy for specific clusters of skill development and this

is key if social robots are to be advanced in everyday care.

In the current systematic review and meta-analysis, we aimed to summarise the evidence-

based on the use of social robots with children and young people on the autism spectrum, con-

sidering data from all study types and grey literature. The objectives of the review were to eval-

uate the different robot platforms that have been used with individuals on the autism

spectrum, the settings in which interventions have been implemented, the role of robots within

interventions, and the range of outcomes targeted for therapeutic gain. Within this, we

explored any specific trends related to randomised and non-randomised studies. Further, we

aimed to use meta-analysis to pool data from RCTs, widely accepted as the most rigorous

study design, to assess the efficacy of interventions for specific domains of learning.

Methods

The current systematic review and meta-analysis was completed in accordance with the Pre-

ferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) (S1 Table) [33].

The study protocol was preregistered on PROSPERO [CRD42019148981].

Identification of studies

Papers were eligible for inclusion in the review if (a) participants were diagnosed on the autism

spectrum using established diagnostic criteria (ICD-11, DSM-V or previous versions); (b) they

were aged under 18 years; (c) the study included an intervention based on any robotic plat-

form; (d) data were reported for at least one outcome against which to measure intervention

gains. We only included primary studies and all designs were considered (e.g., randomised,

case controlled, case reports). Lateral search techniques were also used to identify additional

papers for inclusion in the review.

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Criteria for exclusion in the review were the following: (1) lack of separate presentation of

study outcomes for children on the autism spectrum; (2) individual aged over the age of 19; (3)

lack of recording study procedures including number, duration and frequency of robot-medi-

ated sessions; (4) no reference to the robot type/model; (5) commentary papers, protocols, sur-

veys and reviews; (6) qualitative studies; (7) qualitative elements in mixed-method designs; (8)

studies that were not published in English.

We searched the following databases: PubMed, Scopus, EBSCOhost, Google Scholar,

Cochrane Library, ACM Digital Library, and IEEE Xplore. Grey literature was also searched

using PsycExtra, OpenGrey, British Library EThOS, and the British Library Catalogue.

Search terms aligned to the following core domains: ‘autism’ AND ‘robot’ AND outcome-

specific terms aligned to the concepts of social, emotions, communication, education, aca-

demic attainment, behaviour, or health (S2 Table). Databases were searched from inception

until April (6th) 2021. Additional studies were identified through a manual search of the refer-

ences in relevant studies.

Study selection

Identified records were exported into Mendeley (v1.19.8) and duplicates removed. The first

author (AK) screened all the titles and abstracts, and a second reviewer (AB) independently

screened a random sample of 20% of the originally identified records, both using pre-deter-

mined inclusion criteria, to establish reliability for study selection. The full texts of potentially

relevant papers were retrieved and independently assessed for eligibility by the first author

(AK). Twenty percent of full text of the eligible studies were independently screened for eligi-

bility by the second reviewer (AB). There was 100% agreement between reviewers in the selec-

tion of studies which met the inclusion/exclusion criteria.

Data extraction

A pre-piloted form was used to extract data, including the following items: authors and year of

publication, the study aims, design, methods, and a summary of the outcomes. We extracted

characteristics of study participants such as mean age of all study participants, gender distribu-

tion, diagnostic tool, and intelligent quotient (IQ), where available. Sample ethnicity was also

recorded, where available. The socioeconomic status was not recorded in any of the included

studies and so these data could not be extracted.

Characteristics of the interventions were extracted across the articles and included: robot

type, duration of intervention, frequency and length of sessions, location where intervention

was delivered, and outcome measured. Other intervention characteristics such as the type of

robot used, and intervention location were also extracted. Authors of 10 separate studies were

contacted to extract information which was not clearly reported in published work (e.g., con-

firm diagnosis, session location, number and/or duration of a session, moderator of session

delivery, mean and standard deviation per participant). Three authors shared information

about their studies. Findings were summarized narratively, and where relevant, using means,

standard deviations, and percentages.

Meta-analysis

Meta-analyses were conducted using Comprehensive Meta-Analysis Version 3.0 for Windows

[34]. We calculated Hedge’s g effect sizes (and 95% confidence intervals) for end-of-trial data

comparing robot-mediated interventions and control groups in RCTs. Hedge’s g adjusts effect

sizes according to sample size. Comparisons were made for intervention and control group at

end-of-trial on the primary outcomes of: social, emotional, and motor benefits (which

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emerged as the main clusters of outcomes in RCTs). All meta-analyses used a random effects

approach. We classified effect sizes as small (0.2) medium (0.5) and large (0.8) according to

Cohen’s nomenclature. Heterogeneity was assessed using the I2 statistic, and for interpretation

we followed Cochrane guidance (Higgins et al., 2019) where I2 values were identified: 0%-40%

as might not be important; 30%-60% as may represent moderate heterogeneity; 50%-90% may

represent substantial heterogeneity; 75%-100% representing considerable heterogeneity.

Quality assessment

Two reviewers (NK and AB) independently measured the quality of the included studies using

the Quality Assessment Tool for Quantitative Studies [35]. The assessment tool assesses six

components of study validity including: selection bias, study design, confounders, blinding,

data collection methods, withdrawals, and dropouts. Each component is rated as strong (1),

moderate (2), or weak (3). Each paper receives an overall mark ranging between “strong (no

weak rating)”, “moderate (one weak rating)” and “weak (two or more weak ratings)”. All stud-

ies were appraised independently by the first reviewer (NK). The second reviewer (AB)

reviewed independently 20% of the included studies. The inter-rater reliability between the

authors, using Cohen’s Kappa was ‘strong’ (0.87 agreement). Any disagreements between the

two reviewers were resolved through discussion or by consulting a third reviewer (SS). The

results of the quality analysis were further tabulated to identify any types of bias common to

the included studies (S3 and S4 Tables).

Results

The study selection process and a summary of included articles will be presented first, followed

by a general overview of the quality of research. Next, the main results will be presented

according to our review objectives.

Selection and inclusion of studies

The search generated 2145 records. After removing duplicates, 1646 records were screened,

with 151 deemed relevant and full texts reviewed, of which 44 articles reporting 40 studies

were deemed eligible for inclusion into the systematic review. The most common reason for

exclusion was the lack of information concerning diagnostic method for autism (n = 57) fol-

lowed by studies which did not meet the inclusion criteria such as reviews, protocols, surveys,

feasibility trials, opinion letters (n = 23). A smaller number of studies were excluded because of

the following reasons: 1. a new robotic platform/ intervention was developed (n = 9); 2. adults/

children with diagnoses other than autism were examined (n = 7); 3. the robot name/type was

missing (n = 4); 4. a qualitative study had been conducted (n = 2) (Fig 1).

Characteristics of included studies

The description of the study characteristics is based on 40 studies. Four articles [21, 36, 37]

had overlapping samples and were not included in the average sample size. The majority of the

studies were non-randomized (n = 23, 57%), followed by RCTs (n = 17, 43%) (Table 1).

Thirty-four studies utilised video data to analyse the study findings. The average sample size of

children and young people on the autism spectrum across the studies was 10 (range 1–30),

(M = 10.22, SD = 6.58, k = 40).

Most studies recruited more males than females, with percentage of males ranging from

67% to 100%. Ethnicity of participants was reported in nine studies (22%), of which five studies

included Chinese participants [38–42], three comprised Caucasian, African American, Asian

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Hispanic, Mixed African American and Caucasian, Mixed Caucasian and Hispanic and Latino

participants [20, 43, 44] and one study included Italian children [45].

Comparing RCT and non-RCT designs, the former had more complete reporting about the

autism diagnosis process (for example, from which type of healthcare professional). Only six-

teen studies (40%) measured the cognitive capacity (IQ) of children. Further characterisation

of participants (e.g., school type, parent’s demographics) was generally poor. Child/adolescent

participants ranged in age from 2 years to 16 years (M = 7.4; SD = 3.08). Overall, the studies

were published between 2008 and 2020, in Europe (k = 18, 41%) [i.e., Romania (k = 7), Portu-

gal (k = 3), France (k = 2), Italy (k = 2), Netherlands (k = 2), Belgium (k = 1) and Luxembourg

(k = 1)] followed by the United States of America (k = 11, 27%). Some studies were based in

East Asia (k = 11, 25%) including in Hong Kong (k = 7), Korea (k = 1) and Japan (k = 3). Only

one study was conducted in Canada. Most studies (k = 33, 82%) had received funding to con-

duct their work.

Quality assessment and risk of bias in included studies

The quality assessments revealed that few studies were rated as strong (k = 7), with most rates

as moderate (k = 16) or weak (k = 17) (S2 and S3 Tables). Most studies did not adequately

report on participant selection, resulting in three-quarters being rated as moderate in selection

bias (n = 30, 75%). This trend was comparable across both randomised and non-randomised

studies. The poor quality of many studies can also be partly attributed to the confounding vari-

able rating. Reporting on ethnicity, age, family or socioeconomic status of families was often

poorly described with limited matching again across most study groups (n = 29, 72%); and

again, this was as much a feature of RCTs as non-RCTs. Effort was made to contact authors for

Fig 1. PRISMA flow diagram of the study selection process.

https://doi.org/10.1371/journal.pone.0269800.g001

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Table 1. Summary of study characteristics sorted by study design and mostly used robot.

Reference;

Country;

Funding

Robot group Control group IQ Robot type Adverse

events

Session

details

Risk of

bias

(overall)

Measure Outcome

RANDOMISED CONTROLLED TRIALS

Huskens et al.,

2013;

Netherlands;

funded [48]

N = 3 (100%

male); 8–12 years

old

Human

therapist; N = 3

(100% male);

8–12 years old

85–111 NAO–

Humanoid

robot

Not

reported

5 sessions;

30 minutes;

Clinic room

Moderate Video recording No significant

between-group

differences in

self-initiated

questions at 19-

21-week

assessment.

Marino et al.,

2020

Italy; funded

[45]

N = 7 (86%

male); 4–8 years

old; Italian

Human

therapist; N = 7

(86% male); 4–8

years old;

Italian

82–121 NAO–

Humanoid

robot

Not

reported

12 sessions;

90 minutes;

Laboratory

Strong Test of

Emotional

Comprehension

(TEC) &

Emotional

Lexicon Test

(ELT)

Improved

emotional

recognition and

comprehension in

robot group at

12-week

assessment.

So et al., 2018;

Hong Kong;

funded [38]

N = 7 (71%

male); 6–12 years

old; Chinese

Waitlist group

robot sessions

after research

completion;

N = 6 (83%

Males); 6–12

years old;

Chinese

49–67 NAO–

Humanoid

robot

Not

reported

24 sessions;

30 minutes;

School

Weak Video recording Improved motor

imitation (e.g.,

gestural accuracy)

at 12-week

assessment for

robot group.

So et al., 2018;

Hong Kong;

funded [39]

N = 15 (87%

male); 4–6 years

old; Chinese

Waitlist group

robot sessions

after research

completion;

N = 15 (93%

Males); 4–6

years old;

Chinese

Not reported NAO–

Humanoid

robot

Not

reported

8 sessions;

30 minutes;

School

Weak Video recording Improved motor

imitation (e.g.,

gestural accuracy)

at 10-week

assessment for

robot group.

So et al., 2019;

Hong Kong;

funded [40]

N = 13 (85%

male); 4–6 years

old; Chinese

Waitlist group

robot sessions

after research

completion;

N = 11 (93%

male); 4–6 years

old; Chinese

Not reported NAO–

Humanoid

robot

Not

reported

12 sessions;

45 minutes;

Clinic room

Weak Video recording Improved

narrative skills at

12-week

assessment for

robot group.

So et al., 2019;

Hong Kong;

funded [41]

N = 12 (83%

male); 6–12 years

old; Chinese

Human

therapist;

N = 11 (91%

male); 6–12

years old;

Chinese

46–74 NAO–

Humanoid

robot

Not

reported

4–5

sessions; 30

minutes;

School

Weak Video recording No significant

between-group

differences in

motor imitation

(e.g., gestural

accuracy) at

10-week

assessment.

So et al., 2020;

Hong Kong;

funded [40]

N = 12 (83%

male); 4–6 years

old; Chinese

Waitlist group

robot sessions

after research

completion

N = 11 (91%

male); 4–6 years

old; Chinese

Not reported NAO–

Humanoid

robot

Not

reported

9 sessions;

45 minutes;

Clinic room

Weak Video recording Improved joint

attention in robot-

based drama

sessions at 9-week

assessment.

(Continued)

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Table 1. (Continued)

Reference;

Country;

Funding

Robot group Control group IQ Robot type Adverse

events

Session

details

Risk of

bias

(overall)

Measure Outcome

Srinivasan

et al., 2015;

USA; funded

[20]

N = 12 (92%

male); 5–12 years

old; Caucasian,

African

American, Asian

Hispanic, Mixed

African

American and

Caucasian,

Mixed Caucasian

and Hispanic

Human

therapist;

N = 12 (83%

male);

Comparison

group (tabletop

activities)

N = 12 (83%

male); 5–12

years old;

Caucasian,

African

American,

Asian Hispanic,

Mixed African

American and

Caucasian,

Mixed

Caucasian and

Hispanic

Not reported NAO–

Humanoid

robot &

Rovio robot

Not

reported

32 sessions;

15 minutes;

Home

Moderate Video recording Improved gestural

imitation at

10-week

assessment in

robot group.

Srinivasan

et al., 2015;

USA; funded

[21]

(overlapping

sample)

N = 12 (92%

male); 5–12 years

old; Caucasian,

African

American, Asian

Hispanic, Mixed

African

American and

Caucasian,

Mixed Caucasian

and Hispanic

Human

therapist;

N = 12 (92%

male);

Comparison

group (tabletop

activities); 5–12

years old;

Caucasian,

African

American,

Asian Hispanic,

Mixed African

American and

Caucasian,

Mixed

Caucasian and

Hispanic

Not reported NAO–

Humanoid

robot &

Rovio robot

Not

reported

32 sessions;

15 minutes;

Home

Weak Video recording Improved

repetitive

behaviour at

10-week

assessment for

human therapist

group.

Srinivasan

et al., 2016;

USA; funded

[37]

(overlapping

sample)

N = 12 (92%

male); 5–12 years

old; Caucasian,

African

American, Asian

Hispanic, Mixed

African

American and

Caucasian,

Mixed Caucasian

and Hispanic

Human

therapist;

N = 12 (83%

male);

Comparison

group (tabletop

activities)

N = 12 (88%

male); 5–12

years old;

Caucasian,

African

American,

Asian Hispanic,

Mixed African

American and

Caucasian,

Mixed

Caucasian and

Hispanic

Not reported NAO–

Humanoid

robot &

Rovio robot

Not

reported

32 sessions;

45 minutes;

Home

Moderate Video recording Improved social

skills at 10-week

assessment in

robot group.

(Continued)

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Table 1. (Continued)

Reference;

Country;

Funding

Robot group Control group IQ Robot type Adverse

events

Session

details

Risk of

bias

(overall)

Measure Outcome

Srinivasan

et al., 2016;

USA; funded

[37]

(overlapping

sample)

N = 12 (92%

male); 5–12 years

old; Caucasian,

African

American, Asian

Hispanic, Mixed

African

American and

Caucasian,

Mixed Caucasian

and Hispanic

Human

therapist;

N = 12 (83%

male);

Comparison

group (tabletop

activities)

N = 12 (88%

male); 5–12

years old;

Caucasian,

African

American,

Asian Hispanic,

Mixed African

American and

Caucasian,

Mixed

Caucasian and

Hispanic

Not reported NAO–

Humanoid

robot &

Rovio robot

Not

reported

32 sessions;

45 minutes;

Home

Moderate Video recording Improved

repetitive

behaviour at

10-week

assessment for

human therapist

group.

Zheng et al.,

2020; USA;

funded [54]

N = 11 (gender

not reported);

1.64–3.14 years

old

Waitlist group

robot sessions

after research

completion

N = 9 (gender

not reported);

1.64–3.14 years

old

Mean = 58.81 NAO–

Humanoid

robot

Two

children in

waitlist and

one child in

robot group

left at first

session due

to distress

4 sessions;

10 minutes;

Clinic room

Weak Video recording No difference in

joint attention

skills at 9-week

assessment.

De Korte et al.,

2020;

Netherlands;

funded [51]

N = 24 (83%

male); 3–8 years

old

Parent Pivotal

Response

Treatment

N = 20 (85%

male); 3–8 years

old

Not reported NAO–

Humanoid

robot

Not

reported

20 sessions;

45 minutes;

Home

Strong Video recording Improved self-

initiation in

robot-assisted

Pivotal Response

Treatment at

3-month

assessment.

So et al., 2020;

Hong Kong;

funded [47]

N = 18 –Tier 1

(N = 6 (67%

male), Tier 2

N = 6 (100%

male), Tier 3

(N = 6; 100%

male); Tier 1

received the

intervention

earlier than Tiers

2 and 3); all aged

6–8 years old;

Chinese

Not applicable <70 HUMANE–

Humanoid

robot

Not

reported

6 sessions;

30 minutes;

School

Moderate Video recording Improved joint

attention at 4–8

weeks assessment

in all Tiers.

Yun et al.,

2017; Korea;

funded [67]

N = 8(100%

male); 4–7 years

old

Human

therapist; N = 7

(100% male);

4–7 years old

>60 iRobiQ &

CARO–

Humanoid

robot

None 8 sessions;

30–40

minutes;

Unknown

location

Strong Video recording No significant

between-group

differences in

eye-contact at

10-week

assessment.

(Continued)

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Table 1. (Continued)

Reference;

Country;

Funding

Robot group Control group IQ Robot type Adverse

events

Session

details

Risk of

bias

(overall)

Measure Outcome

Costescu et al.,

2015; Romania;

funded [16]

N = 12 (74%

male); 6–12 years

old

Human

therapist;

N = 15 (74%

male); 6–12

years old

Not reported Keepon-

humanoid

snowman

robot

Not

reported

6 sessions;

120

minutes;

School

Moderate Video recording Improved

emotional

intensity and

reduced frequency

of irrational

beliefs in robot

group.

Pop et al.,

2013; Romania;

funded [55]

N = 7 (100%

male); 4–9 years

old

Computer-

based session;

N = 6/ control

group no

intervention;

N = 7; (100%

male); 4–9 years

old

Not reported Probo–

Mammoth

robot

Not

reported

1 session;

10–15

minutes;

Clinic room

Strong Video recording

and 7-point

Likert scale

Decreased level of

prompt in robot

group.

Pop et al.,

2014; Romania;

funded [57]

N = 5 (100%

male); 4–7 years

old

Human

therapist; N = 6

(100% male);

4–7 years old

>70 Probo–

Mammoth

robot

Not

reported

1 session;

unknown

duration;

Clinic room

Strong Video recording

and 7-point

Likert scale

Improved level of

engagement in

robot group.

Simut et al.,

2016; Belgium;

funded [59]

N = 30 (90%

male); 5–8 years

old

Human

therapist;

N = 30 (90%

male); 5–8 years

old

70–119 Probo–

Mammoth

robot

Not

reported

1 session,

15 minutes;

School

Moderate Video recording No significant

between-group

differences in

social skills (e.g.,

eye-contact, joint

attention).

Kim et al.,

2013; USA;

funded [43]

N = 24 (87%

male); 4–12 years

old; white, two

were black and

two were

Hispanic or

Latino

Human

therapist;

N = 24 (87%

male); 4–12

years old; white,

two were black

and two were

Hispanic or

Latino

72–119 Pleo–

Dinosaur

robot

Not

reported

1 session; 6

minutes;

Clinic room

Moderate Video recording No significant

between-group

differences in

number of

utterances.

Kim et al.,

2015; USA;

funded [36]

(overlapping

sample)

N = 24 (87%

male); 4–12 years

old

Human

therapist;

N = 24 (87%

male); 4–12

years old

72–119 Pleo–

Dinosaur

robot

Not

reported

1 session; 6

minutes;

Clinic room

Moderate Video recording Improved level of

enjoyment and

number of words

in robot group.

NON-RANDOMISED CONTROLLED TRIALS

Huskens et al.,

2015; USA;

funded [49]

N = 3 pairs of

1ASD:1sibling

(67% male); 5–10

years old

Not applicable >80 NAO–

Humanoid

robot

Aggression

to sibling

6–8

sessions; 30

minutes;

Clinic room

Moderate Video recording No significant

difference in

collaborative

behaviour at

12-week

assessment.

Kaboski et al.,

2015; USA;

funded [50]

N = 8 pairs of

1ASD:1TD

(100% male); 12–

17 years old

Not applicable Mean

ASD = 106

Mean

TD = 112

NAO–

Humanoid

robot

Not

reported

5 sessions;

180

minutes;

Robotic

camp

Strong Social Anxiety

Scale for

Children-Revised

(SASCR), Social

Anxiety Scale

Adolescents

(SAS-A), Social

Skills

Improvement

System (SSIS)

Partial success.

Significant

decrease in social

anxiety for ASD

group only. No

significant

changes in social

skills for both

groups at 2-week

assessment.

(Continued)

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Table 1. (Continued)

Reference;

Country;

Funding

Robot group Control group IQ Robot type Adverse

events

Session

details

Risk of

bias

(overall)

Measure Outcome

So et al., 2016;

Hong Kong;

not reported

[46]

N = 20 (75%

male); 6–12 years

old; Chinese;

Not applicable 51–72 NAO–

Humanoid

robot

Not

reported

8 sessions;

30 minutes;

School

Weak Unclear Improved motor

imitation (e.g.,

gestural accuracy)

at 12–14 week

assessment for

robot group.

Tapus et al.,

2012; Romania;

not reported

[52]

N = 4 (100%

Male); 2–6 years

old

Human

therapist; N = 4

(100% Male);

2–6 years old

Not reported NAO–

Humanoid

robot

Not

reported

23–26

sessions;

2–5

minutes

with

10minutes

break;

unclear

duration;

Clinic room

Moderate Video recording Partial success.

Mixed results for

eye-contact,

initiations,

attention between

groups at 4-week

assessment.

Individual data

presented per

child.

Warren et al.,

2015; USA;

funded [53]

N = 6 (100%

male); 2.5–4

years old

Not applicable Not reported NAO–

Humanoid

robot

Not

reported

4 sessions;

unclear

duration;

Laboratory

Weak Video recording Improved

attention at

2-week

assessment.

Zheng et al.,

2016; USA; not

reported [44]

N = 6 (100%

male); 2.5–4

years old;

Caucasian

Not applicable Not reported NAO–

Humanoid

robot

Not

reported

6 sessions;

unclear

duration;

Laboratory

Weak Video recording The robot

attracted the

attention at

8-month

assessment.

Kumazaki

et al., 2018;

Japan; funded

[68]

N = 11 (82%

male); mean

age = 15.91

Human

therapist;

N = 11 (82%

male) mean

age = 15.91

Not reported ACTROID-F

& CommU–

Humanoid

robot

Not

reported

1 session; 5

minutes;

Clinic room

Moderate Audio recording Improved in

length self-

disclosure

statements in

CommU (simple)

robot group.

Kumazaki

et al., 2018b;

Japan; funded

[82]

N = 16 (75%

male); 5–6 years

old

Human

therapist;

N = 12 (58%

male); 5–6 years

old

>70 CommU–

Humanoid

robot

one child in

robot group

distressed–

unable to

complete

session

1 session;

15 minutes;

Unknown

location

Moderate Video recording Improved joint

attention in robot

group.

Yoshikawa

et al., 2019;

Japan; funded

[70]

N = 4 (100%

male); 15–18

years old

Human

therapist; N = 4

(100% male);

15–18 years old

Not reported Actroid-F–

Humanoid

robot

Not

reported

5 sessions;

one day;

Laboratory

Weak Video recording

& eye tracker

Improved eye-

contact in robot

group.

Srinivasan

et al., 2013;

USA; not

reported [71]

N = 1 (100%

male); 7 years old

Child-led

condition;

N = 1 (100%

male); 7 years

old

Not reported Isobot–

Humanoid

robot

Not

reported

8 sessions;

30 minutes;

Unknown

location

Moderate Video recording;

Sensory

Integration and

Praxis Test

(SIPT)

Improved motor

imitation skills in

robot group at

6-week

assessment.

Srinivasan &

Bhat, 2014;

USA; funded

[66]

N = 2 (100%

male); 7–8 years

old

Not applicable Not reported Isobot–

Humanoid

robot

Not

reported

8 sessions;

30 minutes;

Home

Moderate Video recording Decreasing

attention at

6-week

assessment.

Costa et al.,

2018;

Luxembourg;

funded [19]

N = 15 (100%

male); 4–14 years

old

Human

therapist;

N = 15 (100%

male); 4–14

years old

80–120 Qtrobot–

Humanoid

robot

Not

reported

1 session;

1.5–4

minutes;

Human vs

robot;

Laboratory

Moderate Video recording Improved

attention and

repetitive

behaviours in

robot group.

(Continued)

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Table 1. (Continued)

Reference;

Country;

Funding

Robot group Control group IQ Robot type Adverse

events

Session

details

Risk of

bias

(overall)

Measure Outcome

Duquette et al.,

2008; Canada;

funded [14]

N = 2 (100%

male); 4–5 years

old

Human

therapist; N = 2

(50% male); 5

years old

Not reported Tito–

humanoid

robot

Not

reported

22 sessions;

3–4

minutes;

Laboratory

Weak Video recording Partial success.

Mixed findings in

imitation (e.g.,

verbal, motor,

facial) skills

between groups at

7-week

assessment.

Scassellati

et al., 2018;

USA; funded

[69]

N = 12 (58%

male); 6–12 years

old

Not applicable >70 No name–

Humanoid

robot

Not

reported

30 sessions;

30 minutes;

Home

Weak Video and audio

recor6ding

Improved social

skills (e.g.,

initiations, joint

attention eye-

contact,

engagement) at

4-week

assessment.

Pop et al.,

2013; Romania;

funded [56]

N = 3 (100%

male); 5–6 years

old

Not applicable Not reported Probo—

Mammoth

robot

Not

reported

1 session;

Clinic room

Strong Video recording

and qualitative

notes

Improved

emotional

recognition.

Simut et al.,

2012; Romania;

funded [58]

N = 4 (50%

male); 4–9 years

old

Human

therapist; N = 4

(50% male); 4–9

years old

Not reported Probo—

Mammoth

robot

Not

reported

6 sessions;

15 minutes;

Clinic room

Moderate 7-point Likert

scale

Decreased level of

prompt in robot

group.

Vanderborght

et al., 2012;

Romania;

funded [60]

N = 4 (50%

male); 4–9 years

old

Human

therapist; N = 4

(50% male); 4–9

years old

Not reported Probo—

Mammoth

robot

Not

reported

6–8

sessions;

10–20

minutes;

Clinic room

Moderate Video recording Decreased level of

prompt in robot

group at 4-week

assessment.

Silva et al.,

2018

Portugal; not

reported [61]

N = 10 (100%

male); 6–9 years

old

Living dog;

N = 10 (100%

male); 6–9 years

old

Not reported Zoomer–Dog

robot

Not

reported

3 sessions;

10 minutes;

Home

Weak Video recording Improved

emotional

regulation in

living dog

condition at

4-week

assessment.

Silva et al.,

2019

Portugal;

funded [18]

N = 10 (100%

male); 6–9 years

old

Living dog;

N = 10 (100%

male); 6–9 years

old

Not reported Zoomer–Dog

robot

Not

reported

1 session; 3

minutes;

Home

Weak Video recording Improved

emotional

regulation and

social

communication in

living dog

condition at

4-week

assessment.

Silva et al.,

2020; Portugal;

funded [62]

N = 10 (100%

male); 5–8 years

old

Living dog

N = 10 (100%

male); 5–8 years

old

Not reported Zoomer–Dog

robot

Not

reported

1 session;

not

reported

minutes;

Home

Moderate Video recording Improved

imitation in living

dog condition.

Puyon &

Giannopulu,

2013

France; not

reported [62]

Game group;

N = 11 (72%

male); 7–8 years

old

No game group;

N = 11 (72%

male); 7–8 years

old

Not reported "POL"–

chicken robot

Not

reported

1 session;

10 minutes;

Clinic room

Weak Video recording Improved eye-

contact, number

of words, better

posture in game

play robot

condition.

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further information, and five author responses were received, which resulted in information

being classified as missing and thus reducing study quality. Finally, outcome assessors (e.g.,

researchers) were not blinded in (n = 38, 95%) of the studies, including often in RCTs as well

as non-RCTs. Promisingly, most studies (n = 36, 90%) used appropriate methods to collect

data including video data. Studies provided details about the position of the cameras and the

use of at least two coders including inter-rater reliability between coders. Most studies (n = 28,

70%) also reported the number of participants approached, screened, and completed the

intervention.

Robot types

Four robot types were used that can be characterised according to their appearance in the fol-

lowing categories: humanoid, animaloid, and other. A humanoid robot is distinguished by its

resemblance to the human body. In general, a humanoid robot has a head, torso, two arms

and legs. Some humanoid robots may have facial characteristics including eyes, nose and

mouth whereas other humanoids may model part of the body from the waist up. Humanoid

robots were used in 67% (27 out of 40) of the included studies. The robot platforms that facili-

tated a session with children and young people on the autism spectrum were the following:

NAO, QTrobot, CommU, ACTROID-F, Isobot, Tito, iRobiQ, Caro, Keepon and HUMANE.

The most frequently used robot was NAO which was used in 17 studies [20, 38–42, 44–54].

Studies used humanoid robots to examine a range of skills including eye-contact, imitation,

joint attention, social skills and emotional regulation.

The use of animaloid (or animal-like) robots, such as an elephant, chicken, dinosaur and

dogs, was examined in 11 out of 40 studies (27%). In the review, the Probo robot (elephant-

like) was referenced in six studies [55–60]. Other animaloid robots were the robot, Pleo, [36],

the dog robot Zoomer, [17, 18, 61] and POL (chicken-like) [62]. These robots facilitated ses-

sions focusing on eye contact, imitation, joint attention and social skills.

The remaining ‘other’ robot category included a robotic arm and a plant robot. The robotic

arm was used with children and young people on the autism spectrum to examine imitation

and eye-contact [63]. The plant robot, called ‘Pekoppa’, was fully programmable with inte-

grated sensors that allowed the robot to model a range of functions. Pekoppa was used with

neurotypical children and young people and children and young people on the autism spec-

trum to compare the differences in heart rate, verbal fluency, and emotional response [64].

When exploring trends across study type, it appeared that humanoid robots were used in

both RCTs (n = 12) and non-RCTs (n = 14). In particular, the robot NAO was utilised in 10

Table 1. (Continued)

Reference;

Country;

Funding

Robot group Control group IQ Robot type Adverse

events

Session

details

Risk of

bias

(overall)

Measure Outcome

Pierno et al.,

2008

Italy; funded

[63]

N = 12 (50%

male); aged 10–

13 years old

Human

therapist;

N = 12 (50%

male); aged 10–

13 years old

Not reported Robotic arm–

industrial

robot

Not

reported

1 session;

60 minutes;

Laboratory

Weak Video recording Improved

attention in robot

group.

Giannopulu

et al., 2014

France; not

reported [64]

N = 15 (73%

male); 6–7 years

old

Human

therapist;

N = 15 (73%

male); 6–7 years

old

Not reported “Pekoppa”–

other robot

Not

reported

1 session;

15 minutes;

Clinic room

Weak Unclear Improved

expressie

language in robot

group.

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RCTs compared to six non-RCTs (Table 1). Non-RCTs were therefore more likely to include a

broader range of robot platforms.

Settings

Intervention sessions with robots took place in five different settings. The most common loca-

tion was autism centres/clinic rooms (n = 15) followed by home (n = 7), school (n = 7) and lab-

oratories (n = 7). RCTs showed a trend to be more likely to take place in autism centres/clinics

(n = 7 out of 17; 41% versus n = 8 or in a familiar environment such as school (n = 6 out of 17;

35% versus n = 1 out of 23; 4%). Sessions at home were more common in non-RCTs (n = 5;

22%) compared to RCTs (n = 2; 12%). Similarly, sessions in a laboratory were more common

in non-RCTs (n = 6; 26%) than RCTs (n = 1; 5%) (S5 Table).

Sixty-five percent (n = 11) of RCTs and 78% (n = 18) of non-RCTs indicated a positive ben-

efit of intervention. The mean duration of robot-intervention was 8.45 sessions and each ses-

sion lasting an average of 33.5 minutes—most likely occurring once (22%) or twice weekly

(28%) over the intervention period, though there was considerable variability (Table 2). The

first session was usually a familiarisation meeting with the child and the robot/play partner.

Intervention sessions tended to be longer in RCTs, with a mean of 35 (range: 6–120) minutes

versus 27 (range: 3–180) minutes in non-RCTs. Similarly, the number of sessions was greater

in RCTs with a mean of 9 sessions (range: 1–32) compared to 7 (range: 1–30) in non-RCTs.

Notably, play partners across studies were more often ‘professionals’ for example, researchers

or healthcare workforce (90%). Children and young people on the autism spectrum had indi-

vidual sessions apart from in three studies [49, 50, 65].

Robot’s role in intervention

During intervention, robots took on the role of a social interface. Hence the robot moved its

head and eyes to express emotions via facial expressions (e.g., happy, sad) or verbally, became

a storyteller, an imitation agent, an intermediate to attract the eye gaze of the child on the

autism spectrum or facilitated collaboration within a small group of two children and young

people or an object where the child on the autism spectrum engaged in free play. In most

Table 2. Summary of features of robot-mediated intervention.

Robot session characteristics

Number of sessions Mean (SD; range) 8.45 (9.52; 1–32 sessions)

Duration per session (mins) Mean (SD; range) 32 (35.85; 3–180 mins)

Session frequency N (%)

Single session 2 (5%)

Daily 4 (10%)

Once a week 9 (22%)

Twice a week 11 (28%)

Three times a week 1 (2%)

Varied frequency 4 (10%)

Not reported 9 (23%)

Play partner N (%)

Researcher 26 (65%)

Child/Clinical Psychologist/ Psychotherapist 10 (25%)

Parent 1 (2%)

No play partner 3 (8%)

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studies (n = 38, 95%), children and young people engaged in a triadic relationship with an

adult/therapist where the robot acted as a mediator. A typical session involved the play partner

controlling the robot via a laptop/computer. Two studies used a fully autonomous robot to

play independently without the guidance of an adult partner. The control group in RCTs was

often a human therapist engaging the child with the same or similar activities apart from five

studies that used a waitlist and so, received the robot session after study completion. It was

unclear whether the children in waitlist studies were receiving no treatment interventions as

part of their educational and community settings.

Targeted skills and outcomes

Studies included in this review targeted a number of skills that can be clustered into 3 main

categories: (1) social and communication skills [narrative skills (n = 1), self-initiated questions

(n = 4), engagement (n = 2), self-disclosure (n = 1), collaborative play (n = 1), level of prompt-

ing (n = 3), joint attention (n = 6), eye-contact (n = 6), imitation (n = 7)]; (2) emotional devel-

opment [recognition and/or understanding (n = 2), emotional regulation (n = 4)]; and (3)

motor skills [stereotyped or repetitive behaviour (n = 1)]. Over two-thirds (n = 29; 72%) of the

articles reported a positive impact of a robot-mediated intervention in children and young

people on the autism spectrum. Less than a quarter of the included articles (n = 10, 25%)

reported no difference in targeted skills development. Finally, one article reported a decline in

attention skills during the intervention period [66].

The majority of the targeted skills were measured through the examination of video record-

ings and coding procedures completed by researchers whereas only two studies [45, 50] used

standardised assessment tools to examine social skills and emotional comprehension

(Table 1). Nine studies [20, 38–42, 45, 55, 67] used blinded researchers to administer the ques-

tionnaires and one study [50] relied on parent-reported outcomes. Another three studies [55,

57, 58] utilised child self-report methods, three used qualitative methods (e.g., audio recording,

notes by researchers) [56, 68, 69] and two used eye-tracking [70] or a sensory integration and

praxis test [71] in conjunction with video recordings. Two studies made explicit reference to

the benefits of the robot-mediated intervention at two weeks [48] and four weeks [40] follow-

ing the end of the intervention. Finally, Marino and colleagues [45] reported that children on

the autism spectrum in both groups (robot and human) spontaneously practiced the trained

skills addressing generalisation issues. No studies included evaluation of health economics

related to intervention delivery.

Meta-analysis

Hedge’s g was calculated for RCTs examining outcomes relating to: social (k = 7), emotional

(k = 2) and motor (k = 3) abilities and for all three areas combined (k = 12). This provided a

total of 346 participants (175 assigned to robot and 171 assigned to control conditions). The

control condition of the included studies comprised of children in a human therapist group

apart from three studies [39, 41, 42] that had a waitlist group (where the children received the

robot session after study completion).

Nine RCTs were excluded from the analyses because of: (1) overlapping samples [20, 21, 36,

37]; and (2) use of waitlist group and/or no reporting (or sharing when contacted directly) of

means, SDs or effect sizes [38, 42, 54, 55, 57]. The included RCTs had quite good quality rat-

ings: strong (k = 4), moderate (k = 6) and weak (k = 3). All three of the weak ratings were for

the studies by So and colleagues [39–41] and all were assessing motor outcomes.

RCTs providing sufficient data for emotion-based outcomes to be examined revealed a

nonsignificant effect size (g = 0.63 [95%CI -1.43 to 2.69]; k = 2). Heterogeneity was high

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(I2 = 88.65). For trials assessing motor outcomes, the effect size was again non-significant (g =

-0.10 [95%CI -1.08 to 0.89]; k = 3) and heterogeneity was again high (I2 = 79.63). For social

outcomes, the effect size was significant (g = 0.35 [95%CI 0.09 to 0.61; k = 7) and heterogeneity

was low (I2 = 0.00). When we combined all three sets of outcomes to assess any pooled benefit

of robot-mediated interventions (Fig 2), the effect size was significant (g = 0.33 [95%CI 0.08 to

0.57; k = 12) and heterogeneity was moderate and significant (I2 = 54.48). Visual analysis of

funnel plots did not suggest any asymmetry and evidence of obvious publication bias (S1 Fig).

Although there is no definitive minimum number of studies required for meta-regression,

we follow the general recommendation of at least 6 to 10 studies for a continuous variable

(Higgins et al., 2019). Given this, we used meta-regression to assess possible moderators across

all 12 RCTs in the meta-analysis. We found that age was a significant moderator (z = -1.97,

df = 12, p = .02) (S2 Fig), with effect sizes being significantly larger in younger samples. Indeed,

age accounted for nearly a third of the variance in effect sizes (analog r

= .32). None of the

other continuous variable moderators were significant including: total length of time in ses-

sions (z = 0.40 df = 12, p = .35); proportion of male participants (z = 0.97, df = 12, p = .17); and

IQ (z = 1.44 df = 8 p = .07).

We also used sub-group analysis to see if the context (home, school, clinic) impacted effect

sizes across all RCTs. This analysis showed a significant impact of robots in the clinic (g = 0.57

(95%0.16 to 0.98; k = 5) with low heterogeneity (I2 = 21.96). By contrast robots were not effica-

cious in either the home g = 0.16 (-0.56 to 0.89; k = 2; I2 = 55.55) or in school g = -0.16 (-0.85

to 0.53; k = 4, I2 = 75.19), though few trials examined the latter.

Discussion

The current systematic review summarises evidence on the use of social robots with children

and young people on the autism spectrum. We aimed to examine the typology of robots, the

Fig 2. Forest plot showing efficacy of robot intervention on emotional, motor and social outcome variables.

https://doi.org/10.1371/journal.pone.0269800.g002

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settings of robot-mediated interventions, the function of the robot during intervention and the

specific skills targeted for therapeutic gain. Notably, the current review provides the first meta-

analysis to estimate the efficacy of robots to bring about meaningful gains, particularly in social

processing. We also highlight key moderators of effect sizes–these include age, with younger

individuals appearing to benefit more and with effects being significantly larger in the clinic

compared to school. Additionally, we found that effect sizes were not moderated by the length

of therapeutic intervention. This suggests that relatively brief forms of robot-based interven-

tions might be helpful (most protocols include an average of 8 sessions each lasting approxi-

mately 30 minutes), with meaningful improvement in social communication at least in the

immediate post-intervention period. Longer-term follow-up was lacking and limits the extent

to which we can generalize learned skills to daily life. For example, of the RCTs included in the

review, the median duration of follow-up data reported was 10 weeks since baseline assess-

ment. To understand the generalization of skills, and both to support commissioning decisions

and to set parent expectations about intervention outcomes, longer-term data will be essential.

Most studies in this review focused on evaluating humanoid robotic platforms, with a small

minority assessing animaloid types. In line with the social nature of the robots, the most com-

mon outcomes could be grouped into three clusters, with most studies focusing on social com-

munication, emotional outcomes, and motor imitation. Most often outcomes were assessed

using video data over other assessment types. However, in studies using video-recording data,

raters were not blinded in 95%, typically because the data being coded was during the interven-

tion when the robot is clearly visible. To overcome this bias, we advance that future trials use

naturalistic observations (e.g., free play) to better assess the extent of skills generalisation and

by raters who are blinded to the intervention the child/young person received.

The most common intervention settings were autism centres/clinics, with fewer studies

based in other everyday learning environments such as the child’s school and home. In this

context, a recent qualitative study found that educators are not ‘uncritically approving’ of the

use of social robots in schools [72]. Though robots are recognised as being motivating, engag-

ing, predicable, and consistent, educators have called for clear protocols on how and why

robots are being used to ensure effective learning support [72]. Few school-based RCTs could

be included in our meta-analysis and so, the non-significant effect is unsurprising given the

heterogeneity and lack of power. Future research needs to optimise intervention protocols and

their practicability in educational settings, as well as effective training and on-going support

for the educators involved. Though autism centres/clinics currently yield the clearest therapeu-

tic benefit, everyday settings potentially offer more feasible routes to embed intervention and

may be more cost-effective, though health economic evaluation data are currently lacking. Fur-

ther, community settings have been identified as underutilised in autism research and offer a

naturalistic environment where a range of expertise can be harnessed for the shared goal of

improving everyday functioning for both children and families [73]. Effectively integrating

robots in a range of settings remains crucial to translate research into practice.

In 80% of the studies, the robots acted to mediate between the child and play partner, who

typically controlled the robot through a keypad. Although some robots were designed with the

architecture of being autonomous (e.g., NAO), human therapists use robots as focal points to

engage children and youth on the autism spectrum in a session in case they find human inter-

action challenging [15, 74]. Human therapists also use robots to demonstrate movements and/

or facial expressions so that children can mimic these. In this way, robots preserve a human

therapist’s time and energy [10, 74, 75] whilst also benefitting from consistency as robots are

more likely to produce the same movement or expression each time. Unsurprisingly our find-

ings evidence the advantage of social robot-mediated interventions in learning or skill

domains that map onto social communication. The meta-analytic evidence here provides

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support for a small-moderate benefit of robots on social-related outcomes; however, specific

impact on emotional and motor outcomes remains elusive because of the small numbers of tri-

als, the small samples per trial and the high heterogeneity currently associated with those out-

comes–all of which reduce power to detect efficacy in the emotion and motor domains.

Turning to risk of bias assessment, most studies were rated as either weak or moderate

using the Quality Assessment Tool for Quantitative Studies [35]. Only seven (17%) studies

were rated as strong, and five of these were RCTs, where higher standards of rigour and report-

ing might be expected. Common weaknesses related to selection bias [k = 36 (90%); 15 RCTs;

21 non-RCTs] and the reporting on confounding variables [k = 35 (87%); 17 RCTs; 20 non-

RCTs]. Other researchers have also commented on selection bias being common in autism

research, particularly the exclusion of youth with a diagnosis of autism and intellectual disabil-

ity [76]. Similarly, many studies did not assess intelligence. The current meta-analysis found a

trend toward larger effect sizes in samples with higher IQ; however, the meta-analysis had data

missing from four RCTs and the samples were somewhat bimodal with two studies having a

mean IQs in the 5–60 range, while the remainder were 90 to 105. Transparency and consis-

tency in reporting sample characteristics is therefore essential in future research. This will help

delineate for whom robot-mediated interventions are more effective, therefore allowing better

targeting and adaptation of intervention protocols.

Finally, the increase in research studies in the field of autism and robot-mediated interven-

tion in the past 15 years and the proportion of studies that have received funding clearly evi-

dences the growth in interest in this form of therapy for autistic individuals, mostly in the

United States and Europe. The relative lack of research on autism and social robotics from

other countries may however signal a need for more global perspectives on human-robot

interaction and cultural influences on autism intervention design [77]. For example, Hashim

& Yussof [77] suggest that robots could be humanised more to support ethical, spiritual and

religious learning, acting to increase cross-cultural appeal in autism research. Given the out-

comes of our meta-analyses, we encourage approaches that seek to adapt interventions for

cross-cultural benefit.

Strengths and limitations

As far as we are aware, the current review is both the first to be preregistered and the first to

meta-analyse some evidence on robot-mediated intervention for autistic children and young

people. In doing so, we have provided novel insights absent from other reviews [7, 32]. Our

search of the grey literature generated articles that are included in this review and so, offers a

more balanced picture of the current literature, minimising potential for publication bias. Evi-

dence from the meta-analysis did not point to any obvious publication bias amongst RCTs.

Nonetheless, the number of RCTs that provided sufficient data for meta-analysis were small

and the findings should be interpreted in that context. Nonetheless, we believe this new evi-

dence will be helpful to researchers, trialists, clinicians, educators, and parents, especially as

the field of technology-assisted learning in autism is seeing an expansion [78–81]. Other limi-

tations might be the large number of articles (k = 102) we excluded owing to their poor report-

ing on autism diagnosis. Systematically excluding these studies was in line with our

preregistered inclusion criteria but may have excluded data from the true population of inter-

est. Studies might however benefit from using clear diagnostic criteria. Further, the evidence

from meta-analysis should be considered with the caveat that we could derive data from only

12 RCTs, although we did manage to capture data from almost three quarters of the trials (12/

17). We also note that our examination of moderator variables was limited to analyses that

were pooled across all three outcomes (social, emotional and motor) in order to obtain

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sufficient data points. Despite such limitations, the findings for social outcomes look especially

promising, while those relating to emotional and motor abilities require further studies. We

were unable to report on adverse events as these data have been poorly reported in studies.

Only four studies reported adverse events [49, 54, 67, 82]. Finally, as commented on earlier,

the studies included in this review have reported on the outcome of brief exposure and its

impact at best in the immediate post intervention or short follow-up period. As such, though

robot mediated interventions appear to be promising, the extent to which skills are generalised

into everyday life and sustained is unclear.

Future recommendations

The outcomes of the review suggest the emergence of considerable interest in evaluating the

therapeutic benefits of social robots for children and young people on the autism spectrum.

Robot (humanoid and animaloid) platforms are suggested as suitable to personalise, scalable

and economical and so offer immense opportunity as a form of autism intervention [83, 84].

For intervention benefits to be maximised, however, better reporting across study designs on

sample recruitment and characteristics and adverse events, as well as further standardisation

of outcome measures is needed. Further, clinical utility will remain limited without evaluation

from randomised designs that assess the evidence of immediate as well as more sustained treat-

ment gains. It has been emphasised that any intervention should be implemented consistently

across settings and multiple professionals working together to support children and young

people on the autism spectrum will increase the likelihood of more rapid progress [85]. Design

and evaluation of robotic research would benefit from a multi-disciplinary approach to har-

ness technological developments, methodological considerations, and evaluation of beha-

vioural and psychological outcomes. Further, building consensus across the social robotics

research community about intervention evaluation would be advantageous and can draw on

existing approaches that have informed similar frameworks in other areas of autism interven-

tion [86].

Conclusions

Humanoid robots are the most common form of robot intervention employed with children

and young people on the autism spectrum. Intervention protocols tend to be brief, and usually

implemented in autism centres/clinics, home or to a lesser extent, at school, where robots typi-

cally take on the role of a therapeutic mediator. Evidence from the current meta-analyses sug-

gests that effects are larger when trials have been conducted in the clinic rather than at home

or in schools, and for younger children suggesting better developmental match. Current

research findings however should be interpreted cautiously given the lack of high-quality RCT

evidence. To increase assessment of clinical effectiveness, this review identifies a need for more

research based on experimental designs and with transparent reporting on sample selection,

characteristics, and adverse events, as well as assessment of intervention gains beyond the

immediate study period.

Supporting information

S1 Fig. Funnel plot exploring publication bias.

(TIF)

S2 Fig. The impact of age on robot interventions.

(TIF)

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S1 Table. PRISMA 2020 checklist.

(DOCX)

S2 Table. Search terms per bibliographic database.

(DOCX)

S3 Table. Quality assessment for included studies.

(DOCX)

S4 Table. Individual study quality assessment overview.

(DOCX)

S5 Table. Vote count outcomes by setting.

(DOCX)

Author Contributions

Conceptualization: Athanasia Kouroupa, Karen Irvine, Silvana E. Mengoni, Shivani Sharma.

Data curation: Athanasia Kouroupa.

Formal analysis: Athanasia Kouroupa, Keith R. Laws.

Methodology: Athanasia Kouroupa, Keith R. Laws, Alister Baird.

Project administration: Athanasia Kouroupa.

Software: Keith R. Laws.

Supervision: Karen Irvine, Silvana E. Mengoni, Shivani Sharma.

Validation: Athanasia Kouroupa, Keith R. Laws.

Visualization: Athanasia Kouroupa, Keith R. Laws.

Writing – original draft: Athanasia Kouroupa.

Writing – review & editing: Athanasia Kouroupa, Keith R. Laws, Karen Irvine, Silvana E.

Mengoni, Alister Baird, Shivani Sharma.

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