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International Journal of Developmental Disabilities logoLink to International Journal of Developmental Disabilities
. 2021 Sep 8;69(4):472–486. doi: 10.1080/20473869.2021.1972741

Evaluating augmented reality as evidence-based practice for individuals with autism spectrum disorder: a meta-analysis of single-case design studies

Hicran Denizli-Gulboy 1, Derya Genc-Tosun 1,✉, Emrah Gulboy 2,*
PMCID: PMC10281417  PMID: 37346255

Abstract

The purpose of this study was to determine whether the augmented reality in children with autism spectrum disorder is an evidence-based practice. For this purpose, a systematic literature review was conducted for determining research that implemented the augmented reality. The review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. As a result of the review, nine single-case experimental design (SCED) research that met the inclusion criteria were analyzed using the quality indicators. At the end of the quality evaluation, the effect size of eight SCED research that were determined to have evidence of a strong or adequate quality was calculated by using Tau-U. The results of the study revealed that the augmented reality was a promising and highly effective intervention (Tau U = 0.98) in teaching new skills for children with autism spectrum disorder.

Keywords: augmented reality, evidence-based practice, autism spectrum disorders, virtual reality, mixed reality

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impaired social communication and social interaction skills and restrictive/repetitive interests and behavior (American Psychiatric Association 2013). Additionaly, individuals with ASD have some cognivite deficits such as, lack of attention and motivation, short-term memory and executive dysfunction (Bogdashina 2003, Edmonds and Worton 2006, Gabriels 2007, Hagland 2010). Due to these inadequacies, it is difficult to provide individuals with ASD with instruction using traditional methods such as verbal instruction and memorization tasks. Most individuals with ASD have difficulty in perceiving and processing verbal stimuli, on the other hand they are successful in understanding and processing visuospatial stimuli (Bogdashina 2003, Grandin, 2008). These strengths of individuals with ASD have been utilized in their education life for many years. For instance, visual support systems (e.g. real objects, printed images or a smartphone, tablet or computer), one of the evidence-based practices (EBP), have been developed based on these strengths. In recent years, research findings show that individuals with ASD tend to be more interested in visual content presented via electronic screen (Shane and Albert 2008).

In today's education system, it is necessary to effectively utilize the power of technology in the learning and teaching process by keeping up with the innovations required by the information age. Technology facilitates both the daily lives of typically developing individuals and individuals with ASD and contributes to the development of their academic, social, and behavioral skills (Berenguer et al. 2020). The use of technology is rapidly becoming widespread worldwide to support the development and learning of individuals with ASD and the institutions engaged in determining EBPs in the field of autism (e.g. National Autism Center (NAC), National Professional Development Center (NPDC), Autism Focused Intervention Resources and Modules (AFIRM)) also recommend the use of technology-aided instruction and intervention (TAII) to support these individuals and increase their independence (NAC 2015, Hedges and AFIRM Team 2017, Steinbrenner et al. 2020).

TAIIs focus on how interventionists can teach their students the target behaviors determined by them by using the most appropriate technology-based interventions (Hedges and AFIRM Team, 2017). TAIIs used in the education of individuals with ASD are robots, computer or web-based software, mobile applications and virtual networks (Steinbrenner et al. 2020). Among these technology-based interventions, the use of virtual reality (Schwienhorst 2002) and augmented reality interventions in education and training activities has become increasingly widespread, especially in recent years (Billinghurst et al. 2015). Moreover, it is possible to come across some examples in which mixed reality interventions, which are expressed as an advanced dimension of augmented reality, are also used in education (İmamoğlu 2018).

Virtual reality (VR) is a technology that is created in computer or similar environments, presented with various imaging devices (Pan et al. 2006), focuses on visual and auditory stimuli (Dede et al. 2017). VR is a system that presents a situation related to the real world to students in a 3 D form using technological tools. VR, offering many opportunities in every field, increases interaction between the student and device and is effective in creating behavioral changes in the learning process by attracting the student’s attention. VR interventions can be customized in such a way that students can actively use all sense organs. Therefore, the use of VR interventions in the field of education is very useful for both students and teachers and also alleviates the burden of the teacher (e.g. preparation of teaching material) (Piovesan et al. 2012).

Augmented reality (AR) is one of the remarkable intervention among technological developments in recent years. AR interventions have started to take more place in our life in many areas with each passing day (Billinghurst et al. 2015). AR, which has started to be used actively, especially in the educational setting, emerges as a type of VR. AR is a technological system created by adding virtual objects to a real environment (Azuma 1997, Milgram and Kishino 1994). In other words, AR is the instant display of the real-world environment by enriching it with virtual objects through various technological tools (Billinghurst et al. 2015).

Mixed reality (MR) is a computer system that combines images of the real world with virtual world images. VR and AR technologies are basically used together (Freeman et al. 2005). In this intervention, virtual objects are included in the real environment as 3 D, or real objects are included in a virtual environment (Pan et al. 2006). In summary, MR interventions present real and virtual world objects on a single screen (Milgram and Kishino 1994).

Despite differences between VR, AR, and MR interventions, these concepts can sometimes be used interchangeably. Milgram and Kishino (1994) tried to clarify these concepts on the plane, named ‘virtuality continuum’ (Figure 1). These concepts, located between the real environment and the virtual environment, are called “augmented reality” and “augmented virtuality,” and all of these concepts are defined as “mixed reality” (Jerald 2016).

Figure 1.

Figure 1.

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Virtuality continiuum.

To mention differences between them, VR creates a virtual world independent of the real world for the user, and when the user enters this environment, his/her relationship with the world is completely disconnected (Bricken and Byrne 1993). On the contrary, the AR intervention maintains the user’s connection with the real world and adds objects to the real environment (EI Sayed et al., 2011). Although individuals with ASD think that VR technology is acceptable it is still not practicle to wear required VR equipments in their daylife (Dechsling et al. 2020, Newbutt et al. 2020). On the other hand, AR equipments (mobile phone or tablet) used commonly for different purposes, are practicle to use and carry. Therefore, AR enables interaction that is not as artificial as in VR interventions in the real world. Interaction with VR usually requires the use of a dedicated VR headset, which can be difficult for many individuals with ASD. However, in AR interventions, interaction with devices such as phones and tablet computers, which are relatively easy to use, is adapted to the real world (Chen et al. 2015). Thus, in comparison with VR, AR interventions allow individuals with ASD to interact in different ways and facilitate the process of learning different skills (Berenguer et al. 2020).

AR interventions have various advantages for individuals with ASD. Firstly, AR interventions support the strengths of these individuals since some individuals with ASD process visual stimuli more easily (Rao and Gagie 2006). Secondly, some individuals with ASD may be interested in using digital tools or technology. Therefore, AR interventions can help individuals with ASD reduce the stress they frequently encounter in social situations in the real world. Thus, AR interventions increase the participation of individuals with ASD in activities and their concentration and positively affect the learning process (Berenguer et al. 2020, Escobedo et al. 2014, Karamanoli et al. 2017)

The use of AR interventions in the education of individuals with ASD has been spreading rapidly in recent years. The research findings demonstrate that AR is effective in teaching social, self-care, daily life and academic skills to individuals with ASD (Baragash et al. 2020, Berenguer et al. 2020). There are research in the literature that examine the effect of AR interventions and also a few review studies on AR interventions for individuals with special needs. Köse and Güner-Yildiz (2020) descriptively analyzed 19 studies, published between 2013 and 2019 and using AR interventions in the education of individuals with special needs. The findings obtained from the research demonstrate that AR is mostly used as a learning material in teaching social and communication skills and has positive results in the education of individuals with special needs.

Berenguer et al. (2020) reviewed a total of 20 studies examining the effect of AR interventions on children and adolescents with ASD, 13 of which were single-case experimental designs (SCEDs) and 7 of which were group experimental designs. Studies were analyzed descriptively in terms of variables such as participants, research design, the technology used, dependent variable, and findings and evaluated in terms of the qualitative indicators developed by Reichow (2011). Most of the studies have demonstrated that AR interventions have positive effects on developing various cognitive, social, and motor skills of children and adolescents with ASD. The research findings show that 13 studies met the quality indicators and reveal that AR interventions are a promising intervention for individuals with ASD.

Baragash et al. (2020) systematically reviewed 16 studies conducted using SCEDs and investigating the effect of AR interventions on the education of individuals with special needs. The authors evaluated the studies in terms of quality standards (Horner et al. 2005) and calculated the effect sizes of the studies that met the standards. The findings demonstrate that the studies have strong effect sizes and AR is a promising intervention for individuals with special needs (e.g. intellectual disability, Down syndrome, ASD, hearing impairment). However, this study not specificialy focused on determining the effect of AR on individuals with ASD.

AR interventions support the strengths of individuals with ASD and can appeal to their interests, these interventions may have more effects on individuals with ASD than individuals with other special needs. Therefore, it is thought that the quality of AR studies in which individuals with ASD participate should be determined using a rubric specific to ASD, and the effect of AR on the learning of individuals with ASD should be revealed. Thus, this study’s purposes are: (a) to evaluate AR studies in terms of their qualitative features, (b) determine the descriptive features of AR studies, (c) determine the general effect level of AR, and (d) determine whether AR is an EBP.

Method

A meta-analysis was used in the current study. We followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The PRISMA guidelines include a checklist of 27 items and a four-phase flow diagram. The checklist consists of items deemed necessary to transparently report the results of a systematic review (Moher et al. 2009).

Search procedure

We performed a literature review to determine studies conducted using AR interventions between January 1990 and March 2021. The screening interval was determined as the last 30 years since AR interventions had been used since the 1990s. The literature review was performed on March 5, 2021, in the PsycINFO, JSTOR, PsycARTICLE, ERIC, Education Source, Scopus, Academic Search Complete, MEDLINE Complete, MEDLINE, Springer Nature Journals EBSCOhost, Web of Science, and Science Direct databases using the keywords in Table 1. Five hundred twenty-one studies were reached as a result of the review. After duplicate studies were removed, 424 studies remained. The titles and abstracts of the remaining studies were examined, and 356 studies not related to AR were excluded. All of the remaining 68 studies were examined as full text in terms of the inclusion criteria of the research, and eight studies meeting the inclusion criteria were determined. Finally, an ancestral search was done, and one more research meeting the inclusion criteria of the study was reached.

Table 1.

Lists of keywords used in the database searches

List 1   List 2   List 3
‘Augmented reality’ OR AR OR ‘Augmented Realit*’ OR ‘augmented environment’ AND Autis* OR ASD OR “Autism Spectrum Disorder*” ‘Asperger*’ OR ‘pervasive developmental disorder’ OR PDD NOT ‘mixed realit*’ OR ‘virtual realit*’ OR ‘virtual environment’
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Eligibility criteria

Inclusion and exclusion criteria were determined to select articles evaluated in the study. Accordingly, the inclusion criteria of the study were as follows: (a) the presence of at least one individual diagnosed with ASD among the research participants, (b) AR interventions being one of the independent variables of the research, (c) the carry of the research with one of SCEDs, and (d) the publication of the research in a peer-reviewed journal in English. The process of determining the studies included in the research is presented in the flowchart in Figure 2.

Figure 2.

Figure 2.

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Flow chart of the PRISMA-based selection process.

Quality appraisal

To qualitatively evaluate the nine studies meeting the inclusion criteria, the evaluation system developed by Reichow et al. (2008) to EBPs in individuals with ASD and revised by Reichow (2011) was used. The evaluation system consists of three instruments: (a) rubrics for the evaluation of research report rigor, (b) guidelines for the evaluation of research report strength, and (c) criteria for determining if an intervention has the evidence needed to be considered an EBP. To evaluate the rigor of research reports, two rubrics were developed, one for research conducted using group research methods and one for research conducted using SCEDs. These rubrics provide a grading scheme that evaluates the quality (i.e. the rigor) of methodological elements of individual research reports. Two levels of methodological element are primary quality indicators and secondary quality indicators

Primary quality indicators are elements of the research methodology deemed critical for demonstrating the validity of a research. The primary quality indicators are as follows: (a) participant characteristics, (b) independent variable, (c) baseline level, (d) dependent variable, (e) visual analysis, and (f) experimental control. The secondary quality indicators are elements of research design that, although important, are not deemed necessary for the establishment of the validity of a research. The secondary quality indicators are as follows: (a) inter-observer agreement, (b) kappa, (c) blind raters, (d) fidelity, (e) generalization or maintenance, and (f) social validity. While research is evaluated using the triple classification scale in terms of primary quality indicators as (a) high quality, (b) acceptable quality, and (c) unacceptable quality, it is evaluated in terms of secondary quality indicators as (a) contains evidence or (b) does not contain evidence.

The second instrument of the evaluative method provides scoring criteria to synthesize the ratings from the rubrics into a rating of the strength of the research report. There are three levels of research report strength: (a) strong, (b) adequate, and (c) weak. A research reported at a strong should be rated as ‘high quality’ in all of the primary quality indicators and as ‘contains evidence’ in three or more of the secondary quality indicators. While a research reported at an adequate is rated as ‘high quality’ in at least four of the primary quality indicators, it should not be rated as ‘unacceptable’ in any primary quality indicator, and it should be rated as ‘contains evidence’ in at least two of the secondary quality indicators. Finally, research rated as ‘high quality’ in fewer than four of the primary quality indicators or rated as ‘contains evidence’ in fewer than two of the secondary quality indicators is considered weak research.

The third author evaluated the quality characteristics of the research. Before the evaluation, the second and third authors evaluated a research they determined together simultaneously and independently of each other and reached an agreement on how the sub-items of each quality indicator should be evaluated. After an agreement on each sub-item was reached, the studies were analyzed in terms of their quality characteristics.

Descriptive characteristics of studies

Studies that were empirically strong and adequate were analyzed descriptively. At this stage, the first and second authors developed a coding key and analyzed a research together. Thus, they reached an agreement on what to pay attention to during coding. The research included in the study was analyzed descriptively in terms of: (a) participant characteristics (age, number, gender, type of disability, (b) setting and interventionists, (c) research design, (d) dependent variable, (e) independent variable, (f) technological device, (g) reliability (inter-observer agreement/procedural fidelity), (h) social validity, (i) acquisition, (j) maintenance, and (k) generalization.

Effects size calculations

The effect sizes of empirically strong and adequate studies were calculated. Firstly, the graphs related to each research were converted into the PNG (Portable Network Graphics) format by the third author using Adobe Acrobat Reader. A total of 26 graphs were obtained from eight studies. Graphical data were digitized using the WebPlotDigitizer program. As a result of this digitization, 354 data points were obtained from 26 graphs. After the digitization process, the effect size was calculated via Tau-U. Tau-U, one of the effect size calculation methods based on non-overlapping data, was used to calculate the effect size. A web-based calculator was used for Tau-U calculation (http://www.singlecaseresearch.org). According to the values obtained in Tau-U calculation, (a) 0-.20 indicate a small effect, (b) .20 −.59 a medium effect, (c) .60 − .79 a large effect, and (d) .80 − 1.0 a very large effect (Vannest and Ninci 2015).

Determining the EBP status of AR

The evaluation system includes some criteria for determining the evidence base of practices (Table 2). After the strength ratings of studies are determined according to the status of meeting the quality indicators, empirically strong and adequate studies are synthesized, and the evidence base of practices is evaluated. To reveal the evidence base of practices: a dual classification system, including (a) established and (b) promising, is used.

Table 2.

Criteria for treatments to be considered EBP

Level of EBP Example criteria
Established (≥ 60 points from the EBP status formula)

  • Five SCEDs studies of strong research report strength with a total sample size of at least 15 participants across studies conducted by at least three research teams in three different geographic locations

  • Ten SCEDs studies of adequate research report strength with a total sample size of at least 30 different participants across studies conducted by at least three research teams in three different geographic locations
Promising (> 30 points from the EBP status formula)

  • Five SCEDs studies of at least adequate research report strength with a total sample size of at least 16 different participants across studies conducted by at least two research teams in two different geographic locations
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Note: ‘Z Points (EBP status formula)= (SCEDS X 4) + (SCEDA X 2)’.

Reliability

We used Cohen’s Kappa to quantify the degree of agreement between raters (Cooper et al. 2009) regarding the following stages: (a) screening and elimination process, (b) quality evaluation of research, (c) descriptive analysis, (d) data digitization, and (e) effect size calculation. Agreement was interpreted as almost perfect across all categorical coding items (mean kappa 0.91; range 0.80 − 1.00) and strong across continuous coding items (mean correlation 0.96; range 0.90 − 1.00; Walker and Snell 2013). At the first stage, the first and second authors performed the screening and elimination process independently of each other and simultaneously. The kappa coefficient obtained for this stage was determined as 1.0. At the second stage, the reliability data regarding the evaluation of the quality indicators of studies were collected by the second author in 55% (n = 5) of the studies. The kappa coefficient for this stage is 0.77. At the third stage, the reliability data related to the descriptive analysis process were collected by the second author in 50% of the studies, and the kappa coefficient was 0.69. Finally, the reliability data related to the data digitization and effect size calculation stages were collected by the second author in 50% of the studies, and the kappa coefficient for both stages was found to be 1.0. While calculating reliability at the data digitization stage, ‘± 2’ differences between the digitized data were considered as agreement. In the process of all reliability calculations, disagreement between the researchers was discussed, and the next stage was initiated after the final decision was reached.

Results

Quality findings of the studies

Nine studies meeting the inclusion criteria were qualitatively evaluated.As a result of the evaluation, four studies were determined as strong rigor rating (Cihak et al. 2016; McMahon et al. 2015a, 2015b, 2016); four studies as adequate rigor rating (Chen et al. 2015, Chen et al. 2016, Lee et al. 2018, Lee 2020), and one research as weak rigor rating (Chung et al. 2015). Findings regarding the qualitative characteristics of the research included in the study are presented in Table 3.

Table 3.

Application of quality indicators for single-case experimental research quality indicators

Primary Quality Indicators Lee (2020) Lee et al. (2018) Cihak et al. (2016) Chen et al. (2016) McMahon et al. (2016) Chen et al. (2015) Chung et al. (2015) McMahon et al. (2015a) McMahon et al. (2015b)
Participant Characteristics 1 Age and gender are provided for all participants (mean age is acceptable). ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒
2 All participants’ diagnoses are operationalized by including the specific diagnosis and diagnostic instrument ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒
3 Information on the characteristics of the interventionist are provided ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒
Information on any secondary participants (e.g. peers) is provided (If available). ☒ ☐ ☐ ☐ ☐ ☐ ☐ ☒ ☐
4 If a study provides standardized test scores, the measures used to obtain those scores are indicated. ☒ ☒ ☒ ☒ ☒ ☒ ☐ ☒ ☒
      H H H H H H U H H
IV 1 Defines independent variables with replicable precision or If a manual is used, the study passes this criterion. ☐ ☐ ☒ ☐ ☒ ☐ ☐ ☒ ☒
2 Defines many elements of the independent variable but omits specific details. ☒ ☒ ☐ ☒ ☐ ☒ ☒ ☐ ☐
3 Does not sufficiently define the independent variables. ☐ ☐ ☐ ☐ ☐ ☐ ☐ ☐ ☐
      A A H A H A A H H
Baseline 1 Encompass at least three measurement points ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒
2 Appear through visual analysis to be stable ☒ ☒ ☒ ☒ ☒ ☒ ☐ ☒ ☒
3 Have no trend or a counter-therapeutic trend ☒ ☒ ☒ ☒ ☒ ☒ ☐ ☒ ☒
4 Have conditions that are operationally defined with replicable precision ☐ ☐ ☒ ☐ ☒ ☐ ☐ ☒ ☒
      A A H A H A U H H
DV 1 The variables are defined with operational precision. ☒ ☒ ☒ ☒ ☒ ☒ ☐ ☒ ☒
2 The details necessary to replicate the measures are provided. ☐ ☒ ☒ ☐ ☒ ☐ ☐ ☒ ☒
3 The measures are linked to the dependent variables. ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒
4 The measurement data is collected at appropriate times during the study for the analysis being conducted. ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒
      A H H A H A U H H
Visual Analysis 1 Have data that are stable (level or trend) ☒ ☒ ☒ ☒ ☒ ☒ ☐ ☒ ☒
2 Contain less than 25% overlap of data points between adjacent conditions, unless behavior is at ceiling or floor levels in the previous condition ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒
3 Show a large shift in level or trend between adjacent conditions that coincide with the implementation or removal of the IV. ☒ ☒ ☒ ☒ ☒ ☒ ☐ ☒ ☒
      H H H H H H U H H
Experimental Control 1 Contains at least three demonstrations of the experimental effect, occurring at three different points in time and changes in the DVs ☒ ☒ ☒ ☒ ☒ ☒ ☐ ☒ ☒
2 There are two demonstrations of the experimental effect at two different points in time and changes in the DVs ☐ ☐ ☐ ☐ ☐ ☐ ☐ ☐ ☐
3 there are fewer than two demonstrations of the experimental effect occurring at two different points in which changes in the DVs ☐ ☐ ☐ ☐ ☐ ☐ ☒ ☐ ☐
 
  
  
 H
 H
 H
 H
 H
 H
 U
 H
 H
    Secondary quality indicators
Indicators
Lee (2020)
 Lee et al. (2018)
 Cihak et al. (2016)
 Chen et al. (2016)
 McMahon et al. (2016)
 Chen et al. (2015)
 Chung et al. (2015)
 McMahon et al., (2015a)
 McMahon et al. (2015b)
Inter-observer Agreement ☐ ☐ ☒ ☐ ☒ ☐ ☒ ☒ ☒
Kappa ☐ ☐ ☐ ☐ ☐ ☐ ☐ ☐ ☐
Blind Raters ☐ ☐ ☐ ☐ ☐ ☐ ☐ ☐ ☐
Fidelity ☐ ☐ ☒ ☐ ☒ ☐ ☐ ☒ ☒
Generalization/ Maintenance ☒ ☒ ☒ ☒ ☐ ☒ ☐ ☐ ☐
Social Validity Socially important DVs ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒
Time- and cost-effective intervention ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒
Comparisons between individuals with and without disabilities ☐ ☐ ☐ ☐ ☐ ☐ ☐ ☐ ☐
A behavioral change that is large enough for practical value ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒ ☒
Consumers who are satisfied with the results ☒ ☒ ☒ ☒ ☒ ☒ ☐ ☒ ☒
IV manipulation by people who typically come into contact with the participant ☐ ☒ ☒ ☐ ☐ ☐ ☐ ☐ ☐
A natural context ☐ ☐ ☐ ☐ ☐ ☐ ☐ ☐ ☐
Study Rigor Adequate Adequate Strong Adequate Strong Adequate Weak Strong Strong
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IV: independent variable; DV: dependent variable; H: high quality; A: acceptable; U: unacceptable.

Descriptive analysis

Strong and adequate rigor rating studies were analyzed descriptively, and only the data of the participants with ASD were reflected in the findings. Therefore, data on a total of 11 individuals with intellectual disability who participated in the studies conducted by McMahon et al. (2015a, 2015b, 2016) were not included in the descriptive analysis findings. Findings regarding the research included in the descriptive analysis process are presented in Table 4.

Table 4.

Descriptive characteristics of the studies

Authors / Region ASD Sample Size, Age Range Intervention Agent Setting Research Design Dependent Variable Independent Variable Device IOA PF Acquisition Maintenance Social Validity
Lee (2020);
Taipei, Taiwan		 N = 3
7-8		 Therapist Classroom MBaP Body language and facial expressions Kinect Skeletal Tracking + AR + RP Computer – – 3/3 6 week
(4-8 session)		 +
Lee et al. (2018a);
Taipei, Tainan
Taichung, Taiwan		 N = 3
8-9		 Therapist TR MBaP Social cues when meeting and greeting AR + CM + SS + RP Tablet – – 3/3 6 week
(4-8 session)		 +
Chen et al. (2016)
Tainan, Taiwan		 N = 6
11-13		 Therapist TR MBaP Facial expressions and emotions Vuforia System
AR + VM + SS		 Tablet – – 6/6 4 week
(3-10 session)		 –
Cihak et al. (2016); TN, WA, NC, USA N = 3
6-7		 SET Bathroom MPaP Tooth brushing Aurasma+
LMP		 iPod + + 3/3 9 week
(1 session)		 +
McMahon et al. (2016); TN and WA, USA N = 1
25		 Researcher CL MPaB Science
Vocabulary		 Aurasma + DI Tablet + + 1/1 – +
Chen et al. (2015) Tainan, Taiwan N = 3
10-13		 Therapist TR MBaP Emotional expression Qualcomm AR (QCAR)+SST Computer – – 3/3 2 week
(8 session)		 –
McMahon et al., (2015a); TN and WA, USA N = 1
21		 Researcher CS AAT Navigation Navigator Heads Up Display + DÖ SP + + 1/1 – +
McMahon et al. (2015b); TN, WA, NC, USA N = 1
18		 Researcher UC AAT Navigation Navigator Heads Up Display + DI SP + + 1/1 – +
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Note: TN: Tennessee; WA: Washington; NC: North Carolina; USA: United States of America; F:female; M: male; ASD: autism spectrum disorder; SET: special education teacher; TR: therapy room; CL: computer lab; CS: community setting; UC: university campus; MBaP: multiple baseline across participants; MPaP: multiple probe across participants; MPaB: multiple probe across behaviors; AAT: adapted alternating treatments; AR: augmented reality; RP: role play; CM: concept map; SS: social story; VM: video model; LMP: least to most prompting; DI: direct instruction; SST: social skill training; SP: smart phone; IOA: inter-observer agreement; PF: procedural fidelity; FM: family members.

Participants

Participants in the studies were analyzed in terms of number, age, gender, and type of disability. A total of 21 individuals with ASD in the analyzed studies were included as participants. Nine participants in the studies were in the 6-9 age group (e.g. Cihak et al. 2016, Lee 2020), nine participants in the 10-13 age group (e.g. Chen et al. 2015, Chen et al. 2016), and three participants were in the 18 and above age group (e.g. McMahon et al. 2015a, 2015b). Of the participants, 15 were male, and 6 were female.

Setting and interventionist

When studies were reviewed in terms of settings, they were observed to be conducted in different settings: three in the therapy room (e.g. Chen et al. 2015), the others in the classroom (Lee 2020), school bathroom (Cihak et al. 2016), computer laboratory (McMahon et al. 2016), social setting (McMahon et al. 2015a), a university campus (McMahon et al. 2015b). Of the analyzed studies, four were applied by therapists (e.g. Chen et al., 2015), three by researchers (e.g. McMahon et al. 2015a), and one by a special education teacher (Cihak et al. 2016).

Research design

Of the studies included in descriptive analysis, four were conducted using the multiple baseline across participants design (e.g., Lee 2020), two the adapted alternating treatments design (e.g. McMahon et al. 2015a), one the multiple probe across behaviors design (McMahon et al. 2016), and one using the multiple probe across participants design (Cihak et al. 2016).

Dependent variable

When studies were examined in terms of the dependent variable, while navigation skills (McMahon et al. 2015a, 2015b) were taught in two studies, the other studies taught body language and facial expressions (Lee 2020), social cues when meeting and greeting (Lee et al. 2018), facial expressions and emotions (Chen et al. 2016), tooth brushing (Cihak et al. 2016), science vocabulary (e.g. sense organs, bones, and plant cell; McMahon et al. 2016), and emotional expression skills (Chen et al. 2015).

Independent variable

It was determined that different AR applications and teaching methods were used in teaching target skills and behaviors in the studies included in the analysis. Accordingly, while at some of the studies the Navigator Heads Up Display AR application, direct instruction (McMahon et al. 2015a, McMahon et al. 2015b) and Aurasma AR application, least to most prompting hierarchy (Cihak et al. 2016) and direct instruction (McMahon et al. 2016) were used. In other studies, the Kinect Skeletal Tracking AR application and role-play (Lee 2020), AR application, concept map, social story and role-play (Lee et al. 2018), Vuforia AR application, video modeling and social story (Chen et al. 2016) and Qualcomm AR (QCAR) application and social skills training (Chen et al. 2015) were used.

Device

It was revealed that different technological devices were used in the presentation of AR interventions in the studies. Accordingly, three studies used tablet computers (e.g. Chen et al. 2016), two studies used computers (e.g., Lee 2020), two studies used smartphones (e.g. McMahon et al. 2015b), and a study used iPods (Cihak et al. 2016) in the presentation of AR intervention.

Inter-observer agreement and procedural fidelity

Inter-observer agreement and procedural fidelity data were collected in four studies (Cihak et al. 2016, McMahon et al. 2015a, 2015b, 2016). It was determined that inter-observer agreement data were collected in at least 20% of all sessions (range = 20-60), and the reliability coefficient varied between 91-100%. It was revealed that procedural fidelity data were collected in at least 20% of all sessions (range = 20-100), and the reliability coefficient varied between 95-100%.

Social validity

Social validity data were collected in six of the analyzed studies. In two studies, social validity data were collected from child participants through a questionnaire (McMahon et al. 2015a, 2016). McMahon et al. (2015b) collected from child participants through a questionnaire and interview. Lee (2020) collected from the family and therapist through the interview. While Lee et al. (2018a) collected from the family through the interview, Cihak et al. (2016) collected from the teacher and teaching assistant through a questionnaire.

Acquisition

A total of 21 individuals diagnosed with ASD were included in the 8 studies included in the descriptive analysis. The research findings demonstrate that AR interventions are effective in acquiring the target skills and behaviors by all participants.

Maintenance and generalization

Maintenance data were not collected in three of the studies included in the descriptive analysis (McMahon et al. 2015a, 2015b, 2016). In the studies, maintenance sessions were held at least two weeks after the criteria were met, and data were collected in at least one session. Generalization data were not collected in any of the studies included in the descriptive analysis.

Effects of AR

Tau-U was used to evaluate the effectiveness of AR interventions in teaching individuals with ASD. Tau-U calculation was performed for eight empirically strong and adequate rigor rating studies. Findings regarding the Tau-U values of the studies are presented in Table 5. When studies are examined in terms of the Tau-U effect size values, it is observed that AR interventions are highly effective in teaching individuals with ASD in all research. The aggregated weighted effect size value (Tau-U = 0.98) obtained in the study also indicates that AR interventions are highly effective.

Table 5.

Effect Size Estimates Calculated Using Tau U and the Forest Plot

Authors n Pairs AB Tau-U Effect
Lee (2020) 3 136 3 .98 Very effective
Lee et al. (2018) 3 180 3 1.0 Very effective
Chen et al. (2016) 6 287 6 .97 Very effective
Cihak et al. (2016) 3 685 3 .97 Very effective
*McMahon et al. (2016) (labeling) 1 61 3 1.0 Very effective
*McMahon et al. (2016) (definition) 1 61 3 .93 Very effective
McMahon et al. (2015a) 1 12 1 1.0 Very effective
McMahon et al. (2015b) 1 18 1 1.0 Very effective
Chen et al. (2015) 3 105 3 1.0 Very effective
Total/ Aggregated Weighted Tau U
21
 1545
 26
 .98
 Very effective
graphic file with name YJDD_A_1972741_ILG0001_B.jpg
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Determining of EBP status for AR

In this study, a total of eight strong (n = 4) and adequate (n = 4) rigor rating research conducted using AR interventions were analyzed. It was determined that these studies were carried out by 2 different researcher groups in 2 different geographical regions, and the total number of participants in the studies was 21. Finally, the Z point (the EBP point) of AR interventions is 54. The research findings demonstrate that they meet the criteria for promising intervention recommended by Reichow (2011) and, therefore, AR are a promising intervention for individuals with ASD. Findings showing the evidence status of AR interventions are presented in Table 6.

Table 6.

EBP Status of AR.

Study Rigor rating Successful N
Lee (2020) Adequate 3
Lee et al. (2018) Adequate 3
Chen et al. (2016) Adequate 6
Cihak et al. (2016) Strong 3
McMahon et al. (2016) Strong 1
McMahon et al. (2015a) Strong 1
McMahon et al. (2015b) Strong 1
Chen et al. (2015) Adequate 3
Formula for determining EBP status (Z Points) = (6 × 4) + (15 × 2) = 54
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Discussion

This study aimed to evaluate AR interventions in terms of qualitative characteristics, determine the descriptive characteristics of AR studies, determine the overall effects of AR, and reveal whether AR is an EBP. The findings reveal that four of the nine studies investigating the effect of AR interventions on individuals with ASD are strong, four are empirically adequate, and one is empirically weak rigor rating. The weighted effect size value of eight strong and adequate studies is 0.98. This value indicates that AR interventions are highly effective for individuals with ASD. Additionally, it can be said that AR is a promising intervention in acquiring target behaviors/skills by individuals with ASD in line with the quality indicators of Reichow (2011). In this context, the research findings are consistent with the findings of the previous review study (Berenguer et al. 2020).

The meta-analysis study conducted by Berenguer et al. (2020) analyzed both SCEDs and group experimental studies to evaluate the evidence basis of AR interventions for individuals with ASD. The study performed by Berenguer et al. (2020) analyzed 13 SCEDs studies and revealed that 7 of these studies were adequate and 6 were weak research. Within the scope of this study, 9 SCEDs research were analyzed, and it was determined that 4 of these studies were strong and 4 were adequate research. Although similar studies were analyzed in both studies, it is observed that different results were obtained. The main reason for this is related to the inclusion-exclusion criteria. While SCEDs studies not allowing visual analysis within the scope of this study (Escobedo et al. 2014, Liu et al. 2017, Keshav et al. 2018, Nazaruddin et al., 2018, Sahin et al. 2018, Soares et al. 2017, Vahabzadeh et al. 2018) were excluded, Berenguer et al. (2020) was included in the analysis process. Another important finding is that Berenguer et al. (2020) evaluated two studies (Liu et al. 2017, Vahabzadeh et al. 2018) as adequate research, eventhough related studies do not include line graphics and not allowing visual analysis. However, one of the primary quality indicators developed by Reichow (2011) is visual analysis. Therefore, according to the research data, studies that cannot be analyzed in terms of tendency, level, immediacy of effect, stability, and overlap should be evaluated as weak research. Finally, although there are four studies evaluated as strong research within the scope of current study (Cihak et al. 2016, McMahon et al. 2015a, 2015b, 2016), there is no strong research in the studies analyzed in Berenguer et al. (2020). It is observed that three of the studies evaluated as strong research in this study (McMahon et al. 2015a, 2015b, 2016) were not included in the evaluation process in the study performed by Berenguer et al. (2020). In this context, it can be said that the study of Berenguer et al. (2020) has a problem with the search procedure or keywords. Although a study (Cihak et al. 2016) was evaluated as strong research in this study, it was evaluated as adequate research in the study of Berenguer et al. (2020). Therefore, this study was re-analyzed by the second author in terms of the quality indicators of Reichow (2011). However, a similar finding was obtained. The reason for this difference could not be revealed since it was not specified to what extent the studies analyzed in the study of Berenguer et al. (2020) met which quality indicators.

Another significant finding that comes to the fore in the process of evaluating the evidence bases of AR interventions is the finding that strong research are conducted by researchers working in the field of special education. Adequate and weak research were conducted by researchers working in medicine, industrial design, planning, and design. Based on this finding, it can be said that researchers working in field other than special education do not take SCEDs research quality indicators into account adequately in their studies. However, it can be said that more empirically strong studies conducted by different researcher groups in different geographical regions should be conducted in order for AR interventions to be included among EBPs for individuals with ASD.

There are 21 participants with ASD in the research included in this study. Most of the participants are in the 6-13 age range. The youngest participant is 6 years old, and the oldest participant is 25 years old. However, this study examined a limited number of studies. Therefore, there is still a need for studies examining the effects of AR interventions, a new research area compared to other technology-assisted intervention methods, on different age groups.

The reviewed studies taught various concepts and skills such as gestures and facial expressions, emotions, greeting, teeth brushing, navigation, and science concepts. However, more research findings are required to determine the areas of development in which AR interventions are effective. Based on this, it may be suggested to focus on studies investigating the effect of AR interventions on teaching various concepts and skills to individuals with ASD in further research.

AR interventions were presented through tablet computers, smartphones, iPods, and computers by therapists in four studies, researchers in three studies, and special education teachers in one research. Nowadays, these technological devices are commonly used as teaching material by individuals with ASD in different age groups (Grynszpan et al. 2014). Therefore, considering that individuals with ASD spend time with different people during the day, researchers can examine the effect of practices offered by individuals, such as parents, siblings, and peers, with whom individuals with ASD spend a lot of time by creating various teaching opportunities during the whole day.

Practices were generally carried out in more structured settings such as the therapy room, classroom, and computer laboratory. However, three studies were conducted in natural settings. These studies were conducted in the bathroom to teach the participants tooth brushing skills (Cihak et al. 2016), on the university campus to teach navigation (McMahon et al. 2015b), and in social settings (McMahon et al. 2015a). These skills are skills that support the independence of individuals with ASD, and it is essential that they perform these skills in their daily lives without the help of an adult. On the other hand, individuals with ASD have difficulties generalizing the skills they have acquired, and teaching skills in natural settings helps them to generalize the skill and ensure its maintenance (Tiger and Hanley, 2008). Therefore, it may be useful to plan more studies in which AR interventions are used in natural settings in teaching new skills to individuals with ASD in further research to expand the literature.

The findings of the analyzed studies demonstrate that all participants have learned the target skills or behaviors with AR interventions. The studies presented AR interventions with the direct instruction method (McMahon et al. 2015a, 2015b), least to most prompting (Cihak et al. 2016), role play technique (Lee 2020), concept map, social stories and role play technique (Lee et al. 2018), video model and social story (Chen et al. 2016) methods. AR interventions alone can be a teaching tool and can be used together with different teaching methods. The efficiency of teaching can be increased using teaching packages consisting of one or a few of the other teaching methods effective in teaching individuals with ASD. Therefore, research can be planned on which participants it will be more effective and efficient to present AR interventions using which methods in the future.

It is remarkable that five research include maintenance studies and none of them include generalization. It is observed that while maintenance sessions were usually held after 4-6 weeks in the studies, long-term maintenance data were collected in only one research (Cihak et al. 2016). However, it draws attention that, except for one of the studies in which maintenance data were collected (Cihak et al. 2016), many maintenance sessions were held in the others. It is thought that collecting long-term maintenance and generalization data in future studies will be useful for expanding the literature. Both inter-observer agreement and procedural fidelity data were collected in four out of nine studies. In the studies in which reliability data were collected, it was determined that the procedural fidelity data were collected in at least 20% of all sessions (range = 20-100), and the reliability coefficient range varied between 95-100%. This finding indicates that AR interventions can be applied reliably. In the research to be included in this study, the criterion of evidence available in at least two of the secondary qualitative indicators in the evaluation tool of Reichow (2011) was sought. Therefore, studies not collecting reliability data were also included in this study. However, collecting reliability data is important for the credibility of the data (Cook et al. 2015, Kratochwill et al. 2013). Thus, it will be beneficial to collect and report reliability data in at least 20% of each population for each participant in future research.

Social validity data were not collected in two out nine studies (Chen et al. 2015, 2016), and it is observed that social validity data were mostly collected from teachers, therapists, family, and participants over the age of 18 (e.g. McMahon et al. 2016). Social validity data were collected through interviews or questionnaire. To expand the current literature on the social validity of AR interventions, it may be recommended to obtain opinions from different people (e.g. undergraduate students, peers) and collect data by social comparison for participants who cannot express their opinions in future studies.

Unlike traditional methods, AR interventions enrich the teaching environment by adding virtual objects to the real world, attracting students' attention by visualizing abstract concepts and making the process fun (Arvanitis et al. 2009). In addition, it enables the student to create self-learning opportunities without the need for adult guidance and allows learning at any time or environment as it is offered through portable devices (McMahon et al. 2016). Considering the findings obtained in this study as well as the features listed above, it can be said that AR may be one of the alternative interventions that can be preferred frequently in the education of individuals with ASD in the near future.

This study has some limitations regarding the findings. Firstly, instead of all the studies, the reliability data on 30% of them were collected. Collecting reliability data for all of the studies in future research may be beneficial for increasing the reliability of the study. Secondly, the evaluation criteria developed by Reichow et al. (2008) for individuals with ASD and adapted by Reichow (2011) were used in this study. The literature can be expanded by conducting studies based on different evaluation criteria in future research. Thirdly, a search was done in the databases using certain keywords in this study. However, a manual search was not done in journals, and publications other than peer-reviewed journals were not included in the review. Another limitation of the study is that gray literature was not included in the study. Therefore, the fact that we missed some unpublished studies may increase the risk of publication bias. The possibility of bias may be reduced by including gray literature which refers sources not having beeen peer reviewed, such as conference papers and dissertations. However, Steinbrenner et al. (2020) stated that studies reviewed by the scientific community (i.e. peer-review) should be evaluated in order to determine the level of evidence for an intervention. Thus, we included the research published in a peer-reviewed journal in this study. For the future studies, we recommend that researchers include the gray literature. Finally, this study examined the effect of AR interventions on individuals with ASD. Therefore, it may be recommended to conduct review and meta-analysis studies investigating the use of AR interventions in individuals with other developmental disabilities.

Conclusion

AR interventions have advantages such as ensuring the active participation of students in activities, concretizing abstract skills, increasing independence, reducing the stress caused by interaction with an adult in the teaching setting, individualizing and making teaching enjoyable (Baragash et al., 2019), and they have been used in the education of individuals with ASD since 2005. The development of AR interventions requires expertise, budget, and effort (Köse and Güner-Yildiz, 2020). However, AR mobile interventions that can be used to teach a limited number of skills to individuals with ASD are available nowadays. Approximately half of the studies examined within the scope of this research were conducted by researchers in the field of industrial design using AR interventions they developed themselves. Others were carried out by researchers in the field of special education with mobile interventions acquired from virtual markets or interventions they developed themselves on accessible web platforms. A certain level of technology literacy is required for AR interventions that can be created using accessible web pages. Therefore, researchers or interventionists in the field of special education may need to improve their technology knowledge to develop AR interventions by themselves. However, design/software experts who develop the infrastructure of AR interventions may not have sufficient knowledge about the learning characteristics of individuals with ASD. Therefore, for the development and spread of the use of AR interventions, it is considered beneficial for experts working in the field of special education and design/software experts (e.g. industrial engineer, software engineer) to conduct teamwork and develop various AR interventions or develop web-based platforms that will serve the development of their AR interventions by interventionists in line with students’ needs.

Disclosure statement

No potential conflict of interest was reported by the authors.

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