Determinants of Technological Interventions for Children With Autism

Abstract

Children with Autism face several significant challenges, including deficits in both verbal and nonverbal communication, difficulties with concentration, limited interest in their surroundings, non-responsiveness, and struggles with adapting to new situations. It is imperative to consider and address these challenges when implementing technological interventions aimed at enhancing the skills of children with Autism. This study aims to identify the research emphasis on the following four determinants of technological interventions for children with Autism - (i) involvement of caregivers, (ii) design considerations, (iii) persuasive technology, (iv) psychological and physiological effects of technologies. These determinants address their deficiencies in communication, coping with new situations, responsiveness and effects of using technologies respectively. A total of seventy six review articles from 2010 to 2022 have been selected through PRISMA guidelines for this study. Most of the selected review articles have not evaluated these four determinants in their studies. This study discusses this research scope and presents future research directions to bridge this gap to develop effective technological solutions to upskill the children with Autism.

Keywords

autism spectrum disorder determinants interventions systematic review technology

Introduction

Autism Spectrum Disorder (ASD) consists of issues relating to cognitive development, impulsive behaviour and communication skills (Pennington, 2010). It is a developmental disorder that can be diagnosed within the first two years of a child’s life (Mazumdar et al., 2021). Communications, social interaction, and repetitive behaviour are three of the areas where major deficits occur due to Autism (First et al., 2022). The symptoms of ASD affected children are: failing to follow instructions, repeated behaviour, avoiding eye contact, poor social skills, inability to comprehend other people’s emotions and inability to focus on a task for a long period (Farzana et al., 2021; Hasan & Nene, 2022d; Ke et al., 2018; Ramdoss et al., 2012). The technology based learning system has emerged into the field of learning for children with ASD (Hasan & Nene, 2022b; Syriopoulou-Delli & Stefani, 2021). Various technologies like robot, virtual reality, augmented reality, video modeling, smartphone, computer programs, mobile applications have already been used in the development of different skills like reading, writing, communication, social behaviour, daily living and vocabulary learning (Dechsling et al., 2021; Desideri et al., 2020; Montes et al., 2023). Due to cognitive developmental delays, this group of children experiences a different learning process. As a result, technological interventions need to be designed and implemented differently for children with Autism and Typically Developed (TD) children. Hence, the general design considerations for the technological solutions will not be appropriate for individuals with Autism (Hasan & Islam, 2020). Children with Autism usually receive technological intervention that focuses on specific skills (Hasan & Nene, 2022c). The design considerations vary based on the skill focused and the technology used (Hasan & Islam, 2020). The effectiveness and efficiency of a technological learning tool largely depends upon its design considerations (Hasan et al., 2023). The objective of design considerations is to incorporate the requirements for successfully executing the tasks (Qian et al., 2009). Further, it is imperative to consider multiple users and their interaction patterns simultaneously in the design phase to improve collaboration skills (Rick et al., 2009; Sharma et al., 2016).

The learning tool must be persuasive enough to overcome the responsiveness deficiency of children with Autism (Ozdowska et al., 2021). Technological persuasion, coupled with social factors, is the key to change human behaviour through persuasive technology (Oyibo, 2021). Through the application of persuasive technology, the target user is encouraged to interact with the learning tool, which helps to gain their attention, enable to identify appropriate next step and guide to navigate through the learning tool (Mintz, 2014).

The study evaluating the psychological and physiological impacts of technological learning tools on ASD affected children is equally crucial. It needs to be investigated whether the use of technology for a long period of time affects them in terms of depression, anxiety, sleep apnea and impairment of their cognitive abilities (Limone & Toto, 2021).

Table 1 compares the scopes of the study of this paper with the existing review studies in this field.

Table 1.

Comparison of This Study With Existing Review Studies.

Focused Skills	Considerations for Review Studies
Focused Skills	Role of Caregivers	Analysis of Technology	Design Issues	Persuasiveness of Learning Tool	Psychological and Physical Effects of Technology	Future Research Directions
Academic	×	√	×	×	×	√
Academic	(Alresheed et al., 2018; Dzulkifli et al., 2016; Knight et al., 2013; Larwin & Aspiranti, 2019; Pennington, 2010; Ramdoss, Mulloy, Lang et al., 2011b; Root et al., 2017; Sansosti et al., 2015; Verma & Lahiri, 2021)
Daily living	×	√	×	×	×	√
Daily living	(Domire & Wolfe, 2014; Cullen & Morgan, 2015; Chia et al., 2018; Chen & Yakubova, 2021; Gardner & Wolfe, 2013; Hrabal et al., 2022)
Emotion Recognition (ER)	⋄	√	×	×	×	√
Emotion Recognition (ER)	(Berggren et al., 2018; Farashi et al., 2022; Lee et al., 2018; Landowska et al., 2022; Monteiro et al., 2017; Rashidan et al., 2021; Taj-Eldin et al., 2018)
Social communication	⋄	√	⋄	×	×	√
Social communication	(Alzrayer et al., 2014; Aljameel et al., 2016; Al-Rashaida et al., 2021; Caprì et al., 2021; Desideri et al., 2020; Dechsling et al., 2021; Deniz et al., 2022; Fletcher-Watson, 2014; Fragale, 2014; Farzana et al., 2021; Gilroy et al., 2017; Hong et al., 2016; Ke et al., 2018; Kowallik & Schweinberger, 2019; Khowaja, Salim, et al., 2020; Lorah et al., 2015; Lorah et al., 2021; Lorenzo et al., 2021; McCoy et al., 2016; Mubin & Poh, 2019; Mosher & Carreon, 2021; Mosher et al., 2021; Ploog et al., 2013; Qi et al., 2017; Ramdoss, Lang, Mulloy et al., 2011a; Reed et al., 2011; Shukla-Mehta et al., 2010; Trevisan et al., 2019)
Miscellaneous	⋄	√	⋄	×	×	√
Miscellaneous	(Almurashi & Bouaziz, 2020; Banire et al., 2017; Bozgeyikli et al., 2018; Bailey et al., 2021; Bartl-Pokorny et al., 2021; Chen, 2012; Epifânio & Da Silva, 2020; Grynszpan et al., 2014; Hanna et al., 2021; Heng et al., 2021; Jouaiti & Hénaff, 2019; Khowaja et al., 2019; Khowaja, Banire, et al., 2020; Larco et al., 2018; Lorenzo et al., 2019; Mónica et al., 2020; Ramdoss et al., 2012; Stephenson & Limbrick, 2015; Spiel et al., 2019; Saleh et al., 2020; Syriopoulou-Delli & Stefani, 2021; Syriopoulou-Delli & Eleni, 2021; Tsikinas & Xinogalos, 2019b; Virnes et al., 2015; Wainer & Ingersoll, 2011; Yakubova et al., 2021)
ER, social communication, daily living, academic	√	√	√	√	√	√
ER, social communication, daily living, academic	The study of this paper

Legends: √ = Considered, × = Not considered, ⋄ = Partially considered.

The objective of this study is to explore the determinants for the effective design and development of technological learning solutions for children with Autism by addressing their limitations related to learnability.

The research questions (RQ) of this study are:

RQ1

What are the determinants to develop the technological learning solutions for ASD affected children considering their limitations on communication, cognitive development, concentration and responsiveness?

RQ2

What is the impact of technology on the skill development process of individuals with ASD?

RQ3

What is the current research emphasis on considered determinants?

RQ4

What is the future research avenue for effective design and development of technological interventions for children with Autism?

The major contributions of this study are summarized as follows:

- Considering four determinants for technology-based interventions to address the challenges due to Autism, which is unique of this kind of research.

- The comparative analysis of this study with existing recent review studies is presented.

- The proliferation of Information and Communication Technology (ICT) based interventions for children with Autism is phrased.

- The present research gap is identified through a Systematic Literature Review (SLR) to explore the determinants in present research to upskill the children with Autism in ER, social communication, daily living and academics.

- The multi-facet functional needs are presented as future research direction.

The organization of this paper is illustrated in Figure 1. The list of acronyms and legends used in this paper is illustrated in Table 2.

Figure 1.

Organization of the paper.

Table 2.

List of Used Acronyms/Legends.

Acronyms/Legends	Description
AAC	Augmentative and Alternative communication
ABA	Applied behaviour analysis
AI	Artificial intelligence
AR	Augmented reality
CAI	Computer assisted instruction
CBI	Computer based intervention
EEG	Electroencephalography
EPN	Early posterior negativity
ER	Emotion recognition
XR	Extended reality
FE	Facial expression
HCI	Human computer interaction
ML	Machine learning
MR	Mixed reality
MT	Mobile technology
PEC	Picture exchange communication
RBI	Robot assisted behavioral intervention
SG	Serious games
SLR	Systematic literature review
TT	Tabletop technology
VM	Video modeling
VR	Virtual reality
√	Considered/Discussed/Reported
×	Not considered/Not discussed/Not reported

Background

This section aims to discuss the severity levels of Autism and the challenges faced in multiple aspects of the affected individuals, including verbal and non-verbal communication, social interaction, responses, and coping with the changes. This section also highlights the constituent of this study and the determinants to address the challenges due to Autism.

Autism Spectrum Disorder

Autism spectrum disorder is characterized by deficits in two key areas: (i) social communication, and (ii) restricted interests and repetitive actions as outlined by the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, fifth Edition) (“American Psychiatric Association,” 2013). Each deficit area is divided into the following three severity levels: Level 1 (requiring support), Level 2 (requiring substantial support) and Level 3 (requiring very substantial support). Summary of the deficit factors due to ASD is illustrated in Table 3.

Table 3.

Deficit Factors Considering the Severity Levels of ASD.

Severity Level of ASD	Deficit Areas
	Social Communication Factors				Restricted Interests and Repetitive Behaviour
	Verbal Communication	Non-Verbal Communication	Social Interaction	Response	Interest in Surroundings	Coping With Change	Repetitive Behaviour	Changing in Focus/Action
1	Support required	Marked deficit	Face difficulty	Limited	Fixated interests	Face difficulty	Frequent	Face difficulty
2	Marked deficit	Marked deficit	Limited	Limited	Fixated interests	Face difficulty	Very frequent	Face distress and/or difficulty
3	Severe deficit	Severe deficit	Very limited	Very limited	Fixated interests	Face extreme difficulty	Regular	Face great distress and difficulty

Challenges and Motivations

There are some common challenges faced by the individuals due to Autism. First, verbal and non-verbal communication deficiency. Communication deficiency includes the following issues: delay in speaking, inability to initiate a conversation, repetitive use of words and lacking in participation in play with peers (Landa, 2007). Communication development processes do not become effective without the involvement of the caregivers (Koegel, 2000). Appropriate interaction styles of the caregivers with the ASD affected children help in their early communication development (Koegel, 2000).

Second, challenge in coping with new situation. Usually, children with ASD are not comfortable with the new situations (Laurent & Rubin, 2004). Thereby, the design and interaction pattern of the technological learning tools need to maintain the familiar action sets by incorporating common social cues for those users’ (Frauenberger et al., 2017). Usability is the consideration of the technology based tools which concerns about the design issues with respect to different types of users’ requirements (Handayani et al., 2020).

Third, less interest in surroundings and non-responsiveness. Individuals with Autism do not show interest to the events happening around them and they remain engaged into their own task (Hasan & Nene, 2022a). This makes challenging to attract them towards any learning tools. It is desired that individuals with Autism get persuaded by various means and methods to use or remain engaged with the tools/applications for learning. Fourth, psychological and physiological effects of technological tools. Inappropriate and excessive use of technology can cause hazards in both psychological and physical aspects (Rosen et al., 2013), whereas the children with Autism already suffer from various neurodevelopmental aspects. Detail investigation is required to identify if there is any psychological and physiological effect of using the technological learning tools for ASD affected children (Wu et al., 2014).

These challenges need to be addressed during the implementation of the technological interventions to upskill the children with Autism. Thereby, the constituent of this study is to explore the underlying technologies to upskill the children with Autism to analyze the following four determinants:

(1) Involvement of caregivers (teachers/parents) in the existing technological interventions to address the communication deficiency factor.

(2) Examine the emphasis on design considerations in the present technological solutions to address the challenge of coping with new situation factor.

(3) Identify the present research emphasis on persuasive technology into the learning solutions to address the less interest towards surroundings and non-responsiveness factors.

(4) Analyze the psychological and physical effects of technological learning tools on individuals with Autism.

This study explores these four determinants in the recent related review articles by a SLR.

Proliferation of Technology in the Learnability Intervention Process

Technology based learning solutions have significant impacts on facilitating the caregivers to teach individuals with Autism due to their attractive interaction patterns. This section highlights some of the prominent technology based upskilling solutions for children with Autism.

Computer Assisted Instructions

Computer Assisted Instructions (CAI) provides visual representations of the instructions (Pennington, 2010). It uses pictography or visual representation which is easily understandable (Sansosti et al., 2015). It uses consistent learning mechanism which helps to grasp the instructions easily as the ASD affected children face difficulty with the diversity of instructions (Dzulkifli et al., 2016; Root et al., 2017). Moreover, CAI provides predictable set of actions which motivates the target users to perform the tasks (Ramdoss, Mulloy, Lang et al., 2011b). CAI has been used in the following skills development process of individuals with ASD: vocabulary, reading, writing, numeracy, laundry, setting up a table, washing dishes and social communication (Alresheed et al., 2018; Cullen & Morgan, 2015; Chia et al., 2018; Verma & Lahiri, 2021).

Video Modeling

Video Modeling (VM) is the teaching mechanism where video clips are used to demonstrate the sequence of actions for a complete task (Domire & Wolfe, 2014). This approach motivates and attracts the target users due to its visual representations. Modeling focuses a particular behaviour or skill to demonstrate (Chen & Yakubova, 2021). VM follows two strategies: first-person perspective and third-person perspective (Gardner & Wolfe, 2013). VM mostly uses for learning the daily living skills, like - putting away groceries, sweeping and wiping tables, laundry, watering plants, shoe tying, response and request (Fragale, 2014; Shukla-Mehta et al., 2010; Spiel et al., 2019).

Mobile Technology and Web Apps

Handheld devices like smartphones and tablets are using effectively to teach various skills to individuals with Autism (Chia et al., 2018; Larwin & Aspiranti, 2019). The advantage of MT is its usage pattern, which is familiar to the caregivers of the target users (Alzrayer et al., 2014). For Autism intervention, mobile apps have emerged as valuable tools for skill development, offering tailored, interactive, and easily accessible solutions. These apps enable the caregivers to customize interventions and provide targeted support, thus enhancing skill acquisition for individuals with autism (Gallardo-Montes et al., 2022; Montes et al., 2021). Moreover, the portability of these technologies provide advantage to carry the device in comfortable places (Lorah et al., 2015; Caprì et al., 2021). Touchscreen based MT devices equipped with different learning apps (Epifânio & Da Silva, 2020). Interactive learning apps generally attract users’ attention and motivate them to use the tool (Hanna et al., 2021).

Robot Assisted Behavioural Intervention

Both humanoid and no-humanoid robots are using in the skill development process of children with Autism (Lorenzo et al., 2021). Robots are mainly used in the learning process of social norms, conversation, motor skill and cognitive development (Hong et al., 2016; Reed et al., 2011). Some of the robots demonstrate the actions while some are interactive in nature (Jouaiti & Hénaff, 2019; Spiel et al., 2019). Most of the robotic intervention process follows one-to-one learning system (Bartl-Pokorny et al., 2021).

Augmented and Virtual Reality

Augmented Reality (AR) applies the physical objects to interact with the system, while VR uses the control within the system (What’s the Difference Between AR and VR?, 2022). AR and VR presents the virtual contents in the form of real world objects (Aljameel et al., 2016). These technologies helps to remain engaged and motivate the users into the task by providing the real world experience (Dechsling et al., 2021). AR/VR technologies are used to teach social communication, conversation initiation and collaboration skills (Banire et al., 2017; Bozgeyikli et al., 2018). Interactive AR/VR apps help to improve the motor skill along with cognitive development skill (Mosher & Carreon, 2021; Mosher et al., 2021).

Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) identifies the human behaviour with the machine, and megaliter extracts the information from the machine by analyzing the data (Difference Between Artificial Intelligence and Machine Learning, 2022). AI and megaliter based learning tools increase the performance monitoring scope (Farzana et al., 2021). It helps the caregivers (parents/teachers/therapists) and researchers to identify the appropriate requirements for the development of individuals with Autism (Farzana et al., 2021; Hasan & Nene, 2022b).

Internet of Things

Internet of Things (IoT) is a state of the art technology where individually identifiable devices can automatically communicate with each other on a requirement basis (Shafique et al., 2020). Generally, IoT set up comprises various sensors, communication devices, and actuators where the system can take input by sensing from the environment, communicate with other devices to perform the necessary actions and release the output to the environment again (Lee & Lee, 2015). This technology is mainly used for monitoring and analyzing the behaviour pattern of individuals with Autism (Sula et al., 2014; Shi et al., 2017). IoT is also used for behavioural therapy and evaluation of cognitive learning performance of children with Autism (Lavanya et al., 2019; Tang & Winoto, 2018).

Cyber Physical System

Cyber Physical System (CPS) is the combination of hardware and software in an embedded system. The system takes input through sensors from the environment and acts accordingly through actuators to make the environment stable (Alur, 2015). The CPS is mainly used for the pedagogical rehabilitation and classroom modeling for children with Autism addressing their cognitive deficiency (Atanasova & Yosifova, 2019; Dimitrova et al., 2020b, 2020; Kostova & Dimitrova, 2018).

The determinants discussed in Background section in order to address the challenges due to ASD pertain to all the above discussed technology-based solutions to upskill the children with Autism.

Synopsis of Considered Determinants

This section aims to discuss the synopsis of the four explicit determinants considered for this study and discussed in Background section. The purpose of this discussion is to evaluate the scope of these determinants in the present research.

Inclusion of Caregivers

Both caregivers and individuals with Autism need to use assistive technologies simultaneously to improve joint attention in any specific skill development purposes (Cardon et al., 2011; Swanson, 2020). The role of parents in the technological intervention process is important, especially in verbal communication aspects of the individuals with Autism (Allen & Shane, 2014). The participation of the caregivers and the children with Autism will enhance the learning capability along with social and communication skills for all sorts of technological interventions. The teachers/therapists need to be the part of the technological intervention process, especially during the pandemic situation when distance education support is required (Stenhoff et al., 2020).

Design Considerations

In Human Computer Interaction (HCI) perspective, usability is one of the parameters for measuring the effectiveness of technological interventions (Hasan & Islam, 2020). Common design considerations of technological solutions will not be effective for children with ASD considering their limitations (Hasan & Islam, 2020). Few of the primary design considerations of technologies for ASD affected children are - hints of actions to be done, the referential meaning of objects, convenient design due to deficit of motor skill, mapping of the objects, reciprocated behaviour and meaningful symbolic representation (Aguiar et al., 2020; Britto & Pizzolato, 2016; Hasan & Islam, 2020; Ntalindwa et al., 2021; Tsikinas & Xinogalos, 2019a).

Persuasive Technology

Persuasive Technology focuses on the influential factors of computers/technological devices to human (Fogg, 2003). This technology approaches the credible solution to address the less interest towards surroundings and non-responsiveness factors of individuals with Autism (Hasan & Nene, 2022a, 2022d). Persuasive technologies focuses on four following factors (Fogg, 1998) - reduce the gaps and increase the accessibility of technology focusing a particular behaviour, projecting the task as achievable with the available options, assisting to take decision for the navigation and influencing the psychology by provoking the actions.

Psychological and Physical Effects of Technology

Extensive and inappropriate use of technologies like excessive screen time can causes harm to children with Autism like sleeplessness, increasing stress, obstructing brain development, hindering social and communication skills development, increasing anxiety, damaging sensory processing (Westby, 2020). Moreover, there is a possibility that individuals with Autism may get addicted to technological learning tools like gamification through smartphones/tablets, which affects their behavioural development (Sahin et al., 2018). Actions to sudden disengagement from the technologies also affect their behaviour (Sahin et al., 2018).

Method

The objective of this section is to explore the existing review articles to understand the present research emphasis on the four determinants considered in this study to address the challenges due to Autism. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Page et al., 2021) have been followed for the SLR to analyze the review articles from 2010 to 2022. The sequential steps for the SLR of this study are illustrated in Figure 2.

Figure 2.

Sequential steps for the SLR.

Selection of Survey Questions

Table 4 shows a list of ten Survey Questions (SQ) formulated for the SLR. The purpose of these research questions is to attain the objectives of this study.

Table 4.

Survey Questions (SQ).

Survey Questions (SQ)
SQ 1	What was the time interval for the consideration of the review study?
SQ 2	How many researches have been analyzed?
SQ 3	Who all were the target users?
SQ 4	Which technologies have been focused on?
SQ 5	Have the design considerations been analyzed?
SQ 6	Has persuasiveness of the technology been considered?
SQ 7	Was the psychological and physical impact of technologies considered?
SQ 8	Was the outcome of each study evaluated?
SQ 9	Were future research directions laid out in the study?
SQ 10	Which skills have been focused on?

Selection of Search Terms

Before starting the searching into the search engines following search terms have been selected to use as the keywords: “ASD”, “Autism”, “technology”, “education”, “learning”, “training”, “social”, “skill”, “academic”, “communication”, “emotion”, “detection”, “recognition”, “design”, “consideration”, “persuasive”, “psychological”, “physical”, “effect”. Several search terms have used simultaneously by using the AND and OR operators.

Selection of Search Engines/Libraries

The explored search engines and libraries to find out the review articles for this study are: PubMed, Google Scholar, IEEE Xplore, Web of Science, ACM Digital Library, Springer Link and Sage Publications. Most number of the articles have been received from Google Scholar. Afterwards, the Mendeley Desktop application has been used for reference management.

Inclusion and Exclusion Criteria

After receiving the review articles from the libraries through search terms, the first scrutiny has been done by analyzing the title and abstract. If confusion arises, then it has been considered for the full reading. Later, the articles are compared with the inclusion and exclusion criteria mentioned in Table 5. The SLR process has been conducted for extracting the targeted results from the finally selected research articles. The PRISMA flow diagram for the article selection is demonstrated in Figure 3.

Table 5.

Inclusion and Exclusion Criteria for the SLR.

Inclusion	Criteria	Exclusion
2010–2022	Time frame	Before 2010
Peer reviewed	Type	White paper
Full article		Comment paper
Published		Unpublished
English	Language	Other than English
PubMed, google scholar, IEEE xplore, web of science, ACM library, Sage, springer link	Database	Outside of PubMed, google scholar, IEEE xplore, web of science, ACM library, Sage, springer link

Figure 3.

PRISMA flow diagram for the SLR.

Results

After the finalization of the review studies, all the research articles were read carefully to extract the targeted results. During the analysis of the articles, it has been observed that some of the articles have emphasized on the skills development where the technologies are used as a means to foster it (Tables 6 –9). Again, some of the articles have emphasized on technologies contribute to the skill development process, where the skills are facilitators (Table 10).

Table 6.

Findings of the Review Studies on ER.

I	II	III	IV	V	VI	VII	VIII	IX	X	XI
Monteiro et al., 2017	Up to 2016	16	P	EEG, EPN	×	×	×	√	√	Recognizing FE: happy, sad, angry, fearful, disgust
Berggren et al., 2018	Up to 2015	13	P	CBI, CAI	×	×	×	√	√	ER training
Lee et al., 2018	2000–2016	15	P	CAI, VM	×	×	×	√	√	ER training through FE analysis
Taj-Eldin et al., 2018	2015–2018	55	P, S	Wearable technology	×	×	×	√	√	ER by analyzing heart rate, respiration rate, physical and movement data
Rashidan et al., 2021	2000–2020	20	P	Infrared, ECG, EEG	×	×	×	√	√	ER by analyzing thermal imaging and FE
Landowska et al., 2022	2019–2021	27	P	ECG, EMG, fMRI	×	×	×	√	√	ER by analyzing heart rate, respiration rate, physical movement and temperature
Farashi et al., 2022	2000–2022	23	P	CAI, VR	×	×	×	√	√	Correct response score, Spence Children’s Anxiety Scale (SCAS)

Column structure: I = reference, II = considered time interval (SQ 1), III = number of studies analyzed (SQ 2), IV = target user: P for primary user (children with ASD), S for secondary user (teachers/parents) (SQ 3), V = revealed technologies (SQ 4), VI = analysis of design considerations (SQ 5), VII = analysis of persuasiveness (SQ 6), VIII = analysis of psychological and physical effects of technologies (SQ 7), IX = analysis of the outcomes (SQ 8), X = future research guidelines (SQ 9), XI = discussed major skills/behaviour (SQ 10).

Table 7.

Findings of the Review Studies on Social/Communication Skills.

I	II	III	IV	V	VI	VII	VIII	IX	X	XI
Shukla-Mehta et al., 2010	1980–2008	26	P	VM	×	×	×	√	√	Play behaviour, conversation, eye contact, sharing, greetings, response, request
Ramdoss, Lang, Mulloy et al., 2011a	1990–2010	10	P	CBI	×	×	×	√	√	PEC, verbal expression, initiation of communication, response, request, greetings
Reed et al., 2011	1989–2010	29	P	CAI, MT, VR, RBI	×	×	×	√	√	Conversation, play, response, reciprocity, friendship
Fletcher-Watson, 2014	1968–2011	52	P	CAI	×	×	×	√	√	Adaptive behaviour, communication, social behaviour
Ploog et al., 2013	1970–2011	45	P	CAI, VM	×	×	×	√	√	Language, PEC, social communication, vocalization, play, response
Fragale, 2014	Up to 2013	22	P	VM	×	×	×	√	√	Play, verbalization, communication
Alzrayer et al., 2014	2011–2014	15	P	MT	×	×	×	√	√	Word production, verbal communication, identification skill
Lorah et al., 2015	2007–2014	17	P	MT, TT	×	×	×	√	√	Request, unlock screen, response, greetings, answering,
Aljameel et al., 2016	2005–2015	19	P	VR, MT, AR, CAI	×	×	×	√	√	Social communication, expression, language
Hong et al., 2016	2002–2016	12	P	CBI, RBI	×	×	×	√	√	Interaction, reading, social functions, eye contact, game
McCoy et al., 2016	Up to 2015	29	P	VM, CBI	×	×	×	√	√	Waving, response, greetings, eye contact, answering, collaboration,
Gilroy et al., 2017	1995–2016	56	P	MT, AAC	×	×	×	√	√	Request, response, question, PEC
Ke et al., 2018	1995–2015	42	P	VR, VM, CAI, CBI	×	×	×	√	√	Social engagement, interaction, initiation, response, peer relationship
Qi et al., 2017	1985–2015	24	P	VM	×	×	×	√	√	Social communication
Mubin & Poh, 2019	2012–2018	08	P	SG	√	×	×	√	√	Interaction
Trevisan et al., 2019	2007–2018	62	P, S	RBI, CAI, AR	×	×	×	√	√	Applied behaviour
Kowallik & Schweinberger, 2019	2009–2019	36	P	CBI	×	×	×	√	√	Facial movement, eye gaze, body movement, social behaviour
Desideri et al., 2020	2010–2019	15	P	MT, CAI	×	×	×	√	√	Self-monitoring, task completion, organization, planning
Caprì et al., 2021	2009–2019	11	P	MT	×	×	×	√	√	Adaptive skills
Khowaja, Salim, et al., 2020	2000–2015	14	P	CBI	×	×	×	√	√	PEC, reading, identification, social communication, essay writing
Mosher & Carreon, 2021	2000–2020	41	P	AR, VR, MR	×	×	×	√	√	Relationship, social awareness, cooperation
Lorah et al., 2021	Up to 2019	09	P	MT, AAC	×	×	×	√	√	Request, verbal behaviour, PEC
Lorenzo et al., 2021	2000–2019	69	P	RBI	×	×	×	√	√	Social interaction, repeated and stereotyped patterns of behaviour, interest
Mosher et al., 2021	2000–2020	41	P	AR, VR, MR, XR	×	×	×	√	√	Cooperation, sharing, relationship, reciprocity, task completion
Farzana et al., 2021	2015–2020	60	P	AI, ML, AR, AAC	×	×	×	√	√	PEC, expression, social communication
Dechsling et al., 2021	2010–2021	49	P	VR, AR, CAI, AAC	×	×	×	√	√	Collaboration, play, social skill, interaction, greetings
Al-Rashaida et al., 2021	2002–2019	24	P	MT	×	×	×	√	√	PEC, request, on task behaviour
Deniz et al., 2022	2000–2021	110	P, S	VM, VR, CBI, TT	√	×	×	√	√	Social communication and language

Table 8.

Findings of the Review Studies on Daily Living Skills.

I	II	III	IV	V	VI	VII	VIII	IX	X	XI
Gardner & Wolfe, 2013	2005–2013	13	P	VM	×	×	×	√	√	Putting away groceries, sweeping and wiping table, setting up table, laundry
Domire & Wolfe, 2014	2005–2013	12	P	VM	×	×	×	√	√	Setting up and cleaning table, food preparation, washing dishes, shoe tying, watering plants
Cullen & Morgan, 2015	1986–2012	36	P	CAI, MT	×	×	×	√	√	Setting up and cleaning table, food preparation, washing dishes, watering plants, laundry, gardening, bag packing, using ATM card
Chia et al., 2018	1992–2017	12	P	CAI, MT, VM	×	×	×	√	√	Self-management, on task behaviour, play skills, social initiation, homework related management, appropriate response
Chen & Yakubova, 2021	2014–2019	22	P	VM	×	×	×	√	√	Photocopying, setting up and cleaning table, faxing, label making, food preparation, emailing, dish washing, planting, vacuuming
Hrabal et al., 2022	Up to 2020	27	P	TT, CAI, VM	×	×	×	√	√	Meal preparation, shopping, laundry, money transaction

Table 9.

Findings of the Review Studies on Academic Skills.

I	II	III	IV	V	VI	VII	VIII	IX	X	XI
Pennington, 2010	1997–2008	15	P	CAI	×	×	×	√	√	Vocabulary, reading, lette×r, numeracy
Ramdoss, Mulloy, Lang et al., 2011b	1990–2010	12	P	CAI	×	×	×	√	√	Vocabulary, reading, letter, sentence
Knight et al., 2013	1993–2012	25	P	CBI	×	×	×	√	√	Letter, word, reading, math, PEC, spelling, vocabulary, rhyme
Sansosti et al., 2015	1995–2013	28	P	CAI	×	×	×	√	√	Word, verb, PEC, spelling
Dzulkifli et al., 2016	1999–2009	06	P	CAI	×	×	×	√	√	Vocabulary
Root et al., 2017	1995–2015	29	P	CAI	×	×	×	√	√	Math, PEC, map reading, sentence, spelling, writing, verb, alphabet
Alresheed et al., 2018	1995–2014	54	P	CBI	×	×	×	√	√	Storytelling, letter, word, reading, math, time, geography, PEC
Larwin & Aspiranti, 2019	2012–2017	34	P	MT	×	×	×	√	√	Numeracy, math
Verma & Lahiri, 2021	2007–2018	17	P	CAI	×	×	×	√	√	Handwriting

Table 10.

Findings of the Technology Focused Review Studies.

I	II	III	IV	V	VI	VII	VIII	IX	X	XI
Wainer & Ingersoll, 2011	1995–2010	14	P	CAI, VR	×	×	×	√	√	Functional and receptive language, attention, memory, ER, vocabulary
Ramdoss et al., 2012	1990–2010	26	P	CBI	×	×	×	√	√	Interaction, response, conversation, ER
Chen, 2012	1997–2011	7	P, S	TT	×	×	×	√	√	Collaboration, social competence, language
Grynszpan et al., 2014	1990–2011	22	P	CAI, VR, RBI	×	×	×	√	√	FE, ER, vocabulary, alphabet, language, self-management
Stephenson & Limbrick, 2015	Up to 2013	36	P	MT	×	×	×	√	√	Using handheld device, PEC, interaction, task completion
Virnes et al., 2015	2000–2010	255	P	CAI, VR, RBI	√	×	×	√	√	Play, reading, language, conversation, hand washing, collaboration
Banire et al., 2017	2008–2017	10	P	VR	×	×	×	√	√	Attention, engagement, focusing
Bozgeyikli et al., 2018	Up to 2017	24	P	VR	√	×	×	√	√	Fear/phobia overcoming, street crossing, non-verbal communication
Larco et al., 2018	Up to 2018	65	P	Web apps	×	×	×	√	√	Social skill, language, communication
Khowaja et al., 2019	2005–2018	14	P	AR	×	×	×	√	√	Social behaviour and engagement, eye contact, PEC
Tsikinas & Xinogalos, 2019b	2005–2018	54	P	SG	×	×	×	√	√	Language, numeracy, following instruction, safety, time
Spiel et al., 2019	1990–2017	185	P	MT, VR, CAI, RBI, AR, VM	×	×	×	√	√	Social interaction, verbal and non-verbal communication, collaboration, ER
Lorenzo et al., 2019	1996–2017	12	P	VR	×	×	×	√	√	Social activities, non-verbal communication, daily living, ER,
Jouaiti & Hénaff, 2019	Up to 2019	13	P	RBI	×	×	×	√	√	Attention, non-verbal communication, motor skill
Almurashi & Bouaziz, 2020	Up to 2019	16	P	AR	×	×	×	√	√	Interaction, questioning, sharing, attention, entertainment
Epifânio & Da Silva, 2020	2010–2018	33	P	Web Apps, RBI, MT, VR	×	×	×	√	√	Attention, interaction, language
Saleh et al., 2020	2008–2017	166	P	RBI	×	×	×	√	√	Interaction, collaboration, eye contact, response, FE, ER, attention
Mónica et al., 2020	2005–2020	19	P	AR	×	×	×	√	√	Social relation, visual and verbal contact, language, social skills
Khowaja, Banire, et al., 2020	2005–2018	30	P	AR	×	×	×	√	√	Social reciprocity, motor skill, play, FE, writing
Hanna et al., 2021	Up to 2020	15	P	Web apps	×	×	×	√	√	Relationship, safety, conversation, interest, friendship, sharing
Bailey et al., 2021	Up to 2019	69	P	VR, AR	×	×	×	√	√	Response, conversation, play, reading, FE, collaboration, interaction
Syriopoulou-Delli & Eleni, 2021	2009–2019	20	P	AAC	×	×	×	√	√	Request, reading, spelling
Yakubova et al., 2021	2007–2020	8	P	AR	×	×	×	√	√	Self-monitoring, reading, writing, numeracy, identification
Syriopoulou-Delli & Stefani, 2021	2000–2020	63	P	CBI, MT, VM, VR, AR, AAC	×	×	×	√	√	Initial communication, vocational,
Heng et al., 2021	1994–2019	158	P	VM, CAI, RBI	×	×	×	√	√	Attention, motor skills, play, communication
Bartl-Pokorny et al., 2021	Up to 2021	60	P	RBI	×	×	×	√	√	Greetings, singing, sharing, interaction, attention, response, ER

Out of seventy six finally selected review studies, fifty studies have found to emphasize on skills (Tables 6 –9). Among these, seven studies have focused on the ED/ER, twenty eight studies have focused on communication skills (social skills, PEC, sign language, verbal communication, FE, ABA), six studies have focused on the daily living skills (setting a table, doing laundry, food preparation, washing dishes, monetary transaction) and nine studies have focused on academic skills (reading, writing, word construction, mathematics, handwriting). The Figure 4 demonstrates the relative statistics among the focused skills which reflects that, most of the researches have taken place on improving the communication skills.

Figure 4.

Relative statistics among the skills.

The rest of the twenty six review studies have emphasized on technologies to analyze the effect on mediating various skills of children with Autism (Table 10). The statistical comparison of the technology focused review studies is illustrated in Figure 5. Correlation and impact of the technological intervention on various skills is shown in Figure 6. This spiral web depicts that, almost all the technologies have used for the development of the communication skills for the children with ASD. The CAI is playing an important role for the development of academic skills. The VM and VR technologies are used mostly for the development of the daily living skills, whereas AI/ML/EEG are mainly used for the analysis of ER and FE.

Figure 5.

Comparative study on applied technologies.

Figure 6.

Correlation between technologies and skills.

Findings

The thematic analysis (Braun & Clarke, 2012) has been conducted to transcribe the results. These findings are based on the results pertaining to the SQs (SQ 1 to SQ 10) outlined in Table 4.

The findings of technological intervention in emotion recognition skills are illustrated in Table 6. A total of seven review studies have been found in between 2010 and 2022 in this aspect. Two skills are mainly focused in this learning process, those are FE and ER. The CAI, CBI, EEG, EPN, VM, wearable technologies like chest straps and arm straps are used almost equally in the revealed studies. The result shows that only one (14.29%) review study (Taj-Eldin et al., 2018) has focused on both caregivers (teachers, parents) and children with Autism as the target users. None of the studies has focused on design considerations, persuasiveness of the used technologies and psychological and physical effects of the technologies on children with Autism.

The findings of the technological intervention in social/communication skills are illustrated in Table 7. A total of twenty eight review studies have been found in between 2010 and 2022 focusing the social/communication skills learning process. Social behaviour, interaction, collaboration, PEC, sign language, ABA and cooperation are the main focused skills in this development process. The AR, VR, CAI and CBI technologies have contributed highest in this learning process with 44.45% weightage collectively. More ten various technologies like VM, MT, RBI, TT, AAC, SG, MR, XR, AI, megaliter are also contributing to develop the social and communication skills of children with ASD. The result shows that only two (7.15%) review study (Deniz et al., 2022; Trevisan et al., 2019) has focused on both caregivers (teachers, parents) and children with Autism as the target users. Only two (7.15%) study (Deniz et al., 2022; Mubin & Poh, 2019) has focused on the design considerations, and none of the studies has focused on the persuasiveness of the used technology and the psychological and physical effects of technologies on children with Autism.

The Table 8 demonstrated the findings of review studies on technological intervention in daily living skills. Six review studies have been found from 2010 to 2022 which have explicitly focused on these skills. The revealed skills in this subject are: setting up table, laundry, food preparation, washing dishes, watering plants, self-management, gardening, bag packing, using ATM card, on task behaviour, emailing, vacuuming, shoe tying, social initiation, play skills and appropriate responses. The VM contributes highest in this skill development process that is 50.00%, whereas the contribution of MT and CAI is 25.00% each. None of the studies has focused on the design considerations, persuasiveness of the used technologies, and psychological and physical effects of the technologies on children with Autism.

The Table 9 depicts the findings of review studies on technological intervention in academic skills. A total of nine review studies have been found from 2010 to 2022 which have explicitly focused on the improvement of the academic skills of children with Autism. The skills which are discussed in these studies are: learning numeracy, enriching vocabulary, learning to read and write, construction of words and sentences, spelling, map reading, storytelling, time, geography, and handwriting. Out of nine review studies, 66.67% have utilized CAI in the skill development process, where the impact of CBI is 22.22%. The least used technology is MT in this learning process containing 11.11% weightage. None of the studies has focused on the design considerations, persuasiveness of the used technologies, and psychological and physical effects of the technologies on children with Autism.

The Table 10 has demonstrated the findings of the review studies focusing the technologies used for associated skills development for children with Autism. Total twenty-six review studies have been found within 2010–2022 focusing any particular technology associated with the various skills development process for children with Autism. Focused skills in these studies are ER, social behaviour, social communication, vocabulary, non-verbal communication, cooperation, collaboration, FR, FE, pointing, showing, sharing, verbal communication, self-management, alphabet, reading and writing.

The result shows that only one (3.85%) review study (Chen, 2012) has focused on both the caregivers (teachers, parents) and children with Autism as the target user. Only two studies (Bozgeyikli et al., 2018; Virnes et al., 2015) have focused on the design considerations (7.70%) and none of the studies has focused on the persuasiveness of the used technologies, and psychological and physical effects of the technologies on children with Autism.

All the review studies projected in Table 6 to 10 have analyzed the outcomes of the evaluated studies and projected the directions of the future research scope.

Outcomes

The outcomes of the SLR on review articles are illustrated in Figure 7. It has been concluded that, in the explored review articles, less emphasis has been placed on the four determinants considered in this study to address the challenges of children with Autism. The outcomes of the SLR show the research scope on the considered four determinants for the technological intervention for children with Autism. This leads to the following section which discusses the future research direction based on these outcomes.

Figure 7.

Outcomes of the SLR.

Summary and Discussions

The study of this paper aimed to investigate the factors influencing the implementation of technological interventions for individuals with Autism. To achieve this goal, the paper has uncovered several key findings. First, it addressed the challenges associated with Autism and identified four essential determinants crucial for mitigating these challenges. Subsequently, the paper delved into the proliferation of technology-based learning solutions aimed at enhancing the skills of individuals with Autism. The exploration of these determinants was conducted through SLR, which scrutinized a total of seventy six review articles published between 2010 and 2022. The results of the SLR indicated that recent review studies have placed relatively less emphasis on these four determinants, highlighting a significant research gap in this area. This SLR’s findings underscore the vast research potential surrounding these determinants and their role in designing effective technological interventions to address the limitations associated with Autism. The study of this paper has presented a multifaceted perspective on the determinants crucial for designing and developing technological interventions in the context of learning for individuals with Autism, shedding light on the need for further research and exploration in this vital field.

Significant research efforts have explored technology-based interventions for children with Autism, yet not all have reached mass commercialization. Achieving widespread success hinges on engaging a diverse group of stakeholders, including academic researchers, technology experts, clinicians, behavioral therapists, and caregivers throughout the design and implementation phases (Jaliaawala & Khan, 2020). Conducting a requirement elicitation study is highly essential before designing and developing a technological intervention system, ensuring its adaptability to address the prevalent challenges associated with autism (Hasan & Islam, 2020). To evaluate research outcomes effectively, comprehensive documentation with detailed technological specifications and cost estimates is vital (Hasan et al., 2023). These insights play a pivotal role in formulating successful market strategies and conducting feasibility assessments, making these technological devices accessible to a broader audience and better meeting the needs of a diverse array of stakeholders.

Limitations of this research include its focus on review articles from a specific timeframe (2010–2022). Additionally, the study may not encompass all possible determinants affecting technological interventions for children with Autism, and the findings might not be applicable to all individuals with Autism due to the heterogeneity of the condition. Future research may need to encompass a wider range of sources and investigate the specific impacts of these interventions on individuals with Autism.

Future Research Directions

State of the art technologies have already been used in the technology-based skills development process for children with Autism. However, technological solutions need to design and develop effectively to address the challenges due to Autism to attain the target skills. Moreover, appropriate determinants need to incorporate into the learning solutions to address the deficit factors due to Autism. Considering these issues, multifaceted design considerations for technological intervention for children with Autism are discussed in this section.

The design considerations ensure the ease of using the system to accomplish the objectives. Several studies have already explored various criteria for designing the technologies for children with Autism (Aguiar et al., 2020; Azahari et al., 2016; Bozgeyikli et al., 2016a, 2016b; Bozkurt et al., 2015; Britto & Pizzolato, 2016; Hussain et al., 2016; Ntalindwa et al., 2021; Tsikinas & Xinogalos, 2019a). Design considerations depend upon the target skills, interaction style, and intended technologies to apply for the development of the learning tool. Children with Autism perform better in their learning when the appropriate design considerations are taken into account (Hasan & Islam, 2020; Hasan et al., 2023).

The effects of the technological learning tools need to be classified into two categories, firstly, effects on learning performance, which needs to be observed periodically to move towards the next higher level of the learning actions (Hasan & Nene, 2022d). Secondly, psychological and physical effects of the used technologies. The physical parameters need to be measured with sleep quality, eye strain, dizziness, heartbeat, pulse rate, eye gaze tracking and facial expression. The psychological effects of the technological learning tool are required to be measured through addiction, depression, anxiety and solitariness. Moreover, there is a need to analyze the long term effect on brain signal analysis (Kumar & Bhuvaneswari, 2012). Behavioral analysis through observation is necessary for the evaluation of the long term cognitive impacts of the technology (Nene & Gupta, 2019).

The following three criteria enable the technological learning tools to be persuasive. Firstly, presence of the five primary types of social cues for technology to become persuasive are - physical, psychological, language, social dynamics and social roles (Fogg, 2003). Secondly, conformity to Fogg’s eight-step design process to create persuasive technology (Fogg, 2009), which concerns on the targeted task, selected users, reason for not accomplishing the task, familiar technologies, motivations, citing examples, rigorous testing and design evolution. Thirdly, Fogg Behaviour Model (FBM), B = MAP, which explains that motivation (M), ability (A) and prompt (P) influence an individual’s behavior (B) (Fogg, 2021).

The last determinant is the acquaintances of the caregivers in the intervention process. The caregivers (parents/teachers/therapists) required to observe the learning outcomes/effects of the learning tool (Hasan & Nene, 2022b). Caregivers need to have a joint participation with the children with ASD to execute the learning systems/applications. The megaliter approach need to be employed to transcribe the performance of each individual user and generate metadata (Gorai & Nene, 2019) which will be stored in a cloud based storage. The access of this storage will be given to the academicians/physicians/researchers to understand the new requirements for the individuals with Autism.

Finally, the learning mechanism need to generate the data sets to train the model through megaliter techniques, which will systematically observe the behavioural patterns and learning preferences of the users which will empower the stakeholders to enhance the lifestyles of individuals with Autism.

Conclusion

This study has identified the existing research gap in considering the determinants for technological interventions in learning for children with Autism through a rigorous SLR. The findings of this study will help the research community to focus on the effective design and development of the technological interventions for children with Autism considering their limitations and requirements. The proposed multifaceted determinants will enable the researchers to capture data for developing heuristics for the effective development of technology-based intervention process. The proposed determinants have the potential to form the basis to develop effective persuasive technology-based learning solutions to improve their quality of life.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Nazmul Hasan

Manisha J. Nene

Author Biographies

Nazmul Hasan is a research scholar in the School of Computer Engineering and Mathematical Sciences at Defence Institute of Advanced Technology, India. He has received his BSc and MSc in Computer Science Engineering discipline from Military Institute of Science and Technology, Bangladesh. His research interests include HCI, Special Education, Affective Computing and Persuasive Technology.

Manisha J. Nene is the Associate Professor and Director of School of Computer Engineering and Mathematical Sciences at Defence Institute of Advanced Technology, India. She obtained her BE and ME from University of Pune, India in the field of Computer Science and Engineering. She obtained her PhD in the year of 2014 from Defence Institute of Advanced Technology, India in the same department. Her research domain includes HCI, Affective Computing, CPS, IoT, Network Security and Quantum Computing.

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Determinants of Technological Interventions for Children With Autism - A Systematic Review

Abstract

Keywords

Introduction

Background

Autism Spectrum Disorder

Challenges and Motivations

Proliferation of Technology in the Learnability Intervention Process

Computer Assisted Instructions

Video Modeling

Mobile Technology and Web Apps

Robot Assisted Behavioural Intervention

Augmented and Virtual Reality

Artificial Intelligence and Machine Learning

Internet of Things

Cyber Physical System

Synopsis of Considered Determinants

Inclusion of Caregivers

Design Considerations

Persuasive Technology

Psychological and Physical Effects of Technology

Method

Selection of Survey Questions

Selection of Search Terms

Selection of Search Engines/Libraries

Inclusion and Exclusion Criteria

Results

Findings

Outcomes

Summary and Discussions

Future Research Directions

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iDs

Author Biographies

References