Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
xx.xxxx/ACCESS.xxxx.DOI

<EMAIL_ADDRESS><EMAIL_ADDRESS>
# From Assistive Technologies to Metaverse – Technologies in Inclusive Higher
Education for Students with Specific Learning Difficulties: A Review

GOKUL YENDURI1  RAJESH KALURI1  DHARMENDRA SINGH RAJPUT1  KURUVA LAKSHMANNA1
THIPPA REDDY GADEKALLU1,2,4,5  MUFTI MAHMUD3  DAVID J. BROWN3  School of
Information Technology and Engineering, Vellore Institute of Technology, Tamil
Nadu, India (e-mail: gokul.yenduri, rajesh.kaluri, dharmendrasingh,
lakshman.kuruva @vit.ac.in)  Department of Electrical and Computer
Engineering, Lebanese American University, Byblos, Lebanon (e-mail:
<EMAIL_ADDRESS> Nottingham Trent University, Clifton Lane, Nottingham,
UK (e-mail: mufti.mahmud<EMAIL_ADDRESS> Zhongda Group, Haiyan
County, Jiaxing City, Zhejiang Province, China, 314312  College of Information
Science and Engineering, Jiaxing University , Jiaxing 314001, China

###### Abstract

The development of new technologies and their expanding use in a wide range of
educational environments are driving the transformation of higher education.
Assistive technologies are a subset of cutting-edge technology that can help
students learn more effectively and make education accessible to everyone.
Assistive technology can enhance, maintain, or improve the capacities of
students with learning difficulties. Students with learning difficulties will
be greatly benefited from the use of assistive technologies. If these
technologies are used effectively, students with learning difficulties can
compete with their peers and complete their academic tasks. We aim to conduct
this review to better understand the role of assistive technologies in
providing inclusive higher education for students with learning difficulties.
The review begins with the introduction of learning difficulties and their
causes; inclusive education and the need for assistive technologies; the
reasoning for conducting this review; and a summary of related reviews on
assistive technologies for students with learning difficulties in inclusive
higher education. Then, we discuss the preliminaries for the learning
difficulties type and assistive technology. Later, we discuss the effects of
assistive technology on inclusive higher education for students with learning
difficulties. Additionally, we discuss related projects and support tools
available in inclusive higher education for students with learning
difficulties. We also explore the challenges and possible solutions related to
using assistive technology in higher education to provide inclusive education
for students with learning difficulties. We conclude the review with a
discussion of potential promising future directions.

###### Index Terms:

Learning Difficulties, Inclusive Higher Education, Assistive Technologies.

=-15pt

TABLE I: List of key acronyms. Acronyms | Description
---|---
ADD | Attention Deficit Hyperactivity Disorder
AI | Artificial Intelligence
AR | Augmented Reality
ARC | Augmented Classroom
ATIA | Assistive Technology Industry Association
BCI | Brain Computer Interface
CAPD | Central Auditory Processing Disorder
DT | Digital Twin
DCD | Developmental Co-ordination Disorder
HMD | Head Mount Display
HCI | Human-Computer Interaction
IoT | Internet of Things
IEEE | Institute of Electrical and Electronics Engineers
MHD | Mental Health Disorders
NDD | Neuro Developmental Disorder
NVLD | Nonverbal Learning Disorder
NGSS | Next Generation Science Standards
PD | Psychological Disorders
PSO | Particle Swam Optimization
SVM | Support Vector Machine
UNESCO | United Nations Educational, Scientific and Cultural Organization
VR | Virtual Reality
WHO | World Health Organization
WIPO | World Intellectual Property Organization
XR | Extended Reality
3D | 3Dimension

##  I Introduction

A learning difficulty is any abnormality of the body or mind that hinders a
person’s ability to do certain tasks and interact with the outside world.
According to the World Health Organization (WHO), 15% of the global population
is disabled, of whom 2% to 4% have significant learning difficulties.
According to the WHO, this global estimate of learning difficulties is rising
due to an ageing population, the rapid spread of chronic diseases, and
advances in the methodologies used to diagnose learning difficulties. [1].
According to a UNICEF study, around 240 million children worldwide are facing
issues with learning difficulties. Most assessments of child well-being
indicate that children with learning difficulties have a worse quality of life
than children without learning difficulties [2]. Students with learning
difficulties may suffer from emotional, mental, physical, or developmental
problems. Education will provide students with learning difficulties with a
feeling of self-worth. Learning difficulties are an underrated but major
element in educational discrimination. Students with learning difficulties are
among the most underserved categories regarding adequate higher education. All
students with learning difficulties must receive a proper inclusive education
for their growth and social development.

###  I-A Causes of Learning Difficulties

With the assistance of modern technologies, researchers could pinpoint all
likely causes of learning difficulties. Some learning difficulties are caused
by prenatal and neonatal hazards, psychological and physical stress, and
environmental exposure. Several risk factors are present at birth and run in
families, according to recent studies [3]. Therefore, there is an increased
risk of learning difficulties in children of those with learning difficulties.
To better understand learning difficulties, it is essential to investigate how
students’ brains adapt to reading, writing, and solving mathematical problems
[4]. More individuals with learning difficulties suffer from physical and
psychological illnesses. There are curable conditions associated with a
person’s learning difficulties. Autism, Attention Deficit Hyperactivity
Disorder, Schizophrenia, Mania, and Pica are a few of the conditions that may
lead to learning difficulties [5]. Problematic behaviour may also indicate
underlying mental or physical health issues. Additionally, family members,
educators, and caretakers must assist the student in overcoming these
obstacles [6]. Students with learning difficulties have fewer opportunities to
get an inclusive technical education, but this barrier may be overcome with
the use of assistive technology. Numerous studies have identified methods for
assisting individuals with learning difficulties.

###  I-B Inclusive Education for Students with Learning Difficulties

Traditionally, students with learning difficulties were seen as inferior and
barred from normal classes due to their cognitive disorders. Then, they were
educated at specialized institutions of higher learning. Inclusive education
allows for integrating students with learning difficulties into regular
classrooms with their normally developing classmates and helps them to face
real-world problems [7]. 1994’s Declarations of Salamanca on Special Education
outlined the ideas of inclusive education. The declaration emphasizes with a
commitment to education for all students, acknowledging the need and urgency
of providing education to all students, adolescents, and adults within the
regular education system [8]. Regular education with an inclusive focus is the
most effective strategy for eradicating discriminatory attitudes, developing
welcoming communities, building an inclusive society, and achieving education
for all [9]. In addition, inclusive education will provide an effective
education for most students, hence boosting the efficiency and cost-
effectiveness of the education system as a whole [10].

###  I-C Need of Assistive Technologies for Students with Learning
Difficulties in Higher Education

Students with different types of learning difficulties confront various
obstacles in higher education. Students with learning difficulties are
excluded from school, society, and mainstream development programs due to a
lack of crucial support and fair participation opportunities [11]. If
technology is implemented properly, students with learning difficulties will
be able to participate in the general education curriculum because they will
have access to simpler and more adaptable methods of completing their tasks.
It is vital to pick assistive technology based on a student’s requirements,
not their difficulties category. As assistive technology is intended to
accomplish a certain objective, it is crucial to choose the appropriate tool
for the task [12]. Assistive technology may include hardware, software, and
peripherals that aid students with learning difficulties in completing their
assignments [13]. Utilizing assistive technology in higher education will aid
students with learning difficulties in remaining competitive with their peers,
fostering social engagement, boosting self-confidence, and enhancing academic
success.

###  I-D Motivation

In this section, we discuss the motivation for conducting this review, and the
motivation for doing this review is depicted in Fig.1. Our study is limited to
those assistive technologies that make it possible for students with learning
difficulties to overcome the challenges they face while competing with their
peers. Our work is restricted in the case of students with physical
impairments, as these assistive technologies reviewed in our study may not
address all physical disabilities, which require a separate focus. These
assistive technologies for physical impairments not only need to focus on
alternative or customized devices but also need to address the student’s
learning difficulties along with their physical impairments as a whole.

Figure 1: Motivation for Review

####  I-D1 To ensure Students with Learning difficulties to have the same
access to Higher Education

Students with learning difficulties, such as dyslexia or autism, struggle to
adapt to the conventional learning environment. They have difficulty following
instructions, course content, and even their textbooks. They need
individualized instruction and cannot withstand the pressure of competition.
Moreover, these students are often bullied by their classmates, which may
further demoralize them [14]. Innovative assistive technology and a
personalized learning environment can help students get past these problems
and get ready to compete with their classmates and do well in academics [15].

####  I-D2 Deconstructing Challenges faced by the Students with Learning
Difficulties in Higher Education

Academically struggling students with learning disabilities may experience
stress [16]. Possible stressful sentiments of low self-worth may arise as a
result. Many students may not perform in the classroom because they fear
making some mistakes in their work [17]. Those with learning difficulties with
self-determination and the help of assistive technology can overcome the
stigma of having a learning difficulty and deal with the problems of higher
education effectively [18].

####  I-D3 To enable Students with Learning Difficulties to participate in
Academic Activities and interact with Peers

The use of assistive technology will provide students with chances for
experiential learning. Students can engage in independent academic activities
or work collaboratively with other students instead of idly waiting for
assistance [19]. The use of assistive technology allows students with learning
difficulties to learn at their own pace. This self-paced learning results in
less pressure and improved communication skills, attention, and behaviour
[20]. Assistive technology can help students with learning difficulties engage
more readily in cooperative learning activities, as students with learning
difficulties may not possess either the needed academic or collaborative
skills to participate fully [21]. With the help of assistive technologies
students’ gaining independence as they were able to complete written
assignments with minimal or no assistance [22].

TABLE II: Summary of Related Reviews on Assistive Technologies for Students
with Learning Difficulties in Inclusive Higher Education.

Ref |  Contributions |  Limitations
---|---|---
[23] |  This study investigated the effectiveness of assistive technology for students with learning difficulties. |  This study limits the most recent technical developments in assistive technology, which can help in inclusive higher education.
[24] |  This study explored the impact of assistive technology on mathematics in higher education. |  This study did not provide insights into how this assistive technology can help students with learning difficulties in inclusive higher education.
[25] |  This study explored the role of artificial intelligence in personalized assistive technology for neurodevelopmental disorders students in their education. |  This study did not address the problems associated with incorporating AI as an assistive technology for students with learning difficulties in inclusive higher education.
[26] |  This study compared an assisted e-learning interface amongst students with and without visual and auditory impairments. |  This study did not address the difficulties in adapting the e-learning interface for students with visual and hearing difficulties.
Our work |  Our work explored the role of assistive technologies in providing inclusive education for students with learning difficulties in higher education |  –

###  I-E Related Works and Contributions

Several researchers have worked on assistive technology for students with
learning difficulties in inclusive higher education. The overview of these
studies is provided in Table II.

To assist reading and writing, tablets featuring text-to-speech and speech-to-
text capabilities have been launched in recent years [27]. Since the 1980s,
the challenges they face in written and spoken language, arithmetic, reasoning
and memory that are the result of their learning difficulties have been
mitigated with the use of various assistive technology. However, few
scientific studies have examined the advantages of this method. Idor Svensson
et al. evaluated the influence of assistive technology on students with
learning difficulties. In their research, 149 individual students
participated. The intervention group was provided with 24 training sessions in
assistive technology, whereas the control group received routine care. In a
single year, the intervention and control groups attained the same degree of
improvement as the normative population. However, neither immediately after
the intervention nor one year later did gains vary across groups. They found
that the use of assistive technology improved reading skills, especially for
students with learning difficulties. It was also observed that the
intervention boosted motivation leading to task completion. Furthermore, their
research demonstrated the challenges of assessing individuals with learning
difficulties in terms of their capacity to comprehend and interpret
information [23].

The landscape of higher education is constantly changing as a result of the
quick adoption and spread of new technologies in teaching and learning methods
[28]. Assistive technologies, which include a variety of specialised tools,
are used to help students access education and participate freely and actively
in the learning process, which improves learning and promotes the educational
system [29]. Pritika Reddy et al. examined students’ perceptions of the use of
various assistive technologies, including mobile learning, tablet learning,
lecture capture, gamification, and online intelligent systems for learning and
student assistance in higher education in mathematics teaching at the
university level. They also assessed the opinions of assistive technology
among mathematics students in online mode. Their findings concluded that
assistive technologies helped students with learning disabilities to
understand mathematics better, and the students showed a positive outlook on
the use of these technologies in mathematical education [24].

Neurodevelopmental disorders (NDDs), which also include developmental
disabilities and specific learning difficulties like attention deficit
hyperactivity disorder (ADHD), dyslexia, and autism spectrum disorders (ASD),
as well as a wide range of mental health disorders (MHDs), including stress,
anxiety, psychotic disorders, and severe depression, are frequently associated
with psychological disorders (PDs) that first emerge in adolescence or early
childhood. Over the last 20 years, there have been notable increases in the
diagnosis of mental diseases on a global scale as well as rapid growth in the
rate of a number of mental health problems. Depending on the kind of PD,
students may struggle with socialisation, communication, and adapting to
changes in their environment, which could make it difficult for them to
concentrate effectively. To improve outcomes for students, treatment has to be
carried out quickly and effectively [30]. In order to address learning
difficulties in students with a variety of NDDs, Prabal Datta Barua et al.
examined the complexity and effectiveness of AI-assisted solutions created
using machine learning models. Their study provided a summary of the data
showing how AI technology may be utilised to improve social interaction and
support training. They concluded that AI solutions aren’t completely effective
at resolving the problems associated with learning difficulties. They
recommended that in the future, AI technologies may be improved with an
emphasis on assisting students with NDDs [25].

In summary, researchers used a variety of technology to assist students with a
variety of learning difficulties. To our knowledge, no research has been
undertaken on the usage of different assistive technologies to aid higher
education students with learning difficulties which provided the motivation
for this review. This research examines the role that assistive technology in
providing inclusive education to students with learning difficulties in higher
education.

###  I-F Systematic Literature Review

The following phases constitute the literature review that was used in this
study to investigate the role of assistive technology for students with
learning difficulties in higher education. First, we discuss the shortcomings
of previous review articles and the reasons for conducting this study.
Investigating relevant scientific and research publications on the use of
assistive technology for impaired students in higher education is the next
step. We place a strong emphasis on peer-reviewed, high-quality papers
published in reputable books, conferences, seminars, symposiums, and journals.
The references utilized in this study were found on well-known archive
services including Google Scholar and arXiv as well as well-regarded
publications like Springer Nature, Wiley, Elsevier, Taylor and Francis, MDPI,
and IEEE. Additionally, the keywords AI, XR, computer vision, the metaverse,
HCI, and digital twins are utilised to identify relevant references and
publications about assistive technology for learning difficulties in inclusive
higher education such as Dyslexia, Dyspraxia, Dyscalculia, Dysgraphia,
Auditory Processing Disorder, Visual Processing Disorder, Nonverbal Learning
Disorder, and Apraxia of Speech. The retrieved articles are all screened based
on their titles in the next phase. We didn’t include any papers with poor-
quality material from predatory journals. After that, we reviewed the
abstracts of the papers to determine their contributions. The data needed for
our analysis of the use of assistive technology for students with learning
difficulties in higher education is extracted in the final step [31].

###  I-G Paper Organization

Section II presents the preliminaries of assistive technologies and types of
learning difficulties. In Section III, we discuss the impact of assistive
technologies in providing inclusive education for students with learning
difficulties in higher education, which includes AI, XR, the metaverse, HCI,
and digital twins. Then we discuss the projects working toward inclusive
education for students with learning difficulties in higher education in
Section IV. Next, Section V is an overview of assistive technology tools. To
drive further studies on assistive technologies for learning difficulties
students in higher education, in section VI, we discuss challenges in
integrating assistive technologies for slow learners in higher education and
future directions. Section VII is the road map. Finally, we conclude the paper
in Section VIII. For clarity, the organisation of this review is presented in
Fig. 2, and a list of frequently used acronyms is listed in Table I.

Figure 2: The Schematic Organisation of the Role of Assistive Technologies in
Providing Inclusive Education for Students with Learning Difficulties in
Higher Education

##  II Preliminaries

This section provides an overview of learning difficulties, which are
identified based on a systematic literature review, followed by a discussion
of assistive technology for learning difficulties in higher education.

###  II-A Types of Learning Difficulties

The type of various learning difficulties and their effects are depicted in
Fig. 3

####  II-A1 Dyslexia

Dyslexia is a learning disorder. Identifying speech sounds, reading, and
decoding letters and words can be challenging for students with dyslexia.
Students with dyslexia may also have trouble speaking and expressing
themselves, and their ideas [32]. They may also find it hard to organise their
thoughts during talks. Despite the impairment in language processing regions
of the brain caused by dyslexia, students with dyslexia can compete with their
peers with the aid of assistive technologies and appropriate intervention.

####  II-A2 Dyspraxia

Dyspraxia is also known as developmental coordination disorder or DCD.
Dyspraxia is a motor disorder based on the brain. It influences large and fine
motor skills, motor planning, and coordination [33]. Despite the fact that it
can influence cognitive skills, it is unrelated to intelligence. A student
with dyspraxia has difficulty with movement and coordination. Students may
have difficulty handling objects and may also tend to bump into things. The
student may also have trouble speaking, be sensitive to light, touch, taste,
or smell, and have trouble moving his or her eyes.

####  II-A3 Dyscalculia

Dyscalculia makes it challenging to understand, and process arithmetic [34].
In addition to counting and simple mental math difficulties, students have
trouble telling time and following directions. The following abilities are
affected by dyscalculia: problems with comprehending how numbers operate and
their relationships, mathematical problem-solving difficulties, difficulty
with learning basic calculations, and they also face difficulty in compiling
and documenting data.

####  II-A4 Dysgraphia

The neurological condition known as dysgraphia impairs writing and fine motor
skills. It is a learning difficulty that affects almost every element of
writing, including spelling, legibility, word size, and expression of a tense
grip that might result in a hurting hand. Bad spatial planning, inconsistent
writing, poor spelling and missing or incomplete words are all symptoms of
dysgraphia [35].

####  II-A5 Auditory Processing Disorder

It is also known as Central Auditory Processing Disorder (CAPD) because it
affects a person’s ability to detect, understand, and identify sounds while
having normal hearing. Significant difficulty understanding speech,
particularly in noisy environments; difficulty following multi-step spoken
instructions delivered without visual aids; distraction by loud or unexpected
sounds; difficulty paying attention to lengthy lectures or other extended
listening sessions; difficulty remembering and/or efficiently summarising
verbally delivered information; and difficulty reading, spelling, and/or
writing are some of the symptoms of CAPD [36].

Figure 3: Types of Learning Difficulties

####  II-A6 Visual Processing Disorder

A student with Visual Processing Disorder(VPD) has difficulty comprehending
visual information. The student may struggle with reading or distinguishing
between similar-looking objects. Those with visual processing problems may
experience difficulties with hand-eye coordination. Students with visual
processing disorder face difficulties like Confuse words that look similar,
reverse letters or numbers, Lacking adequate reading comprehension, make
copying errors and frequently forgetting letters, numbers, and words, being
bad spellers, having uneven or poorly spaced handwriting and having difficulty
in understanding multi-step instructions, and have trouble telling time and
comprehending the idea of time [37].

####  II-A7 Nonverbal Learning Disorder

The most underdiagnosed, misunderstood, and ignored learning impairment is
nonverbal learning disorder (NVLD). Impaired visual, spatial, and
organisational skills, difficulty recognising and interpreting nonverbal
signals, and poor motor function are all symptoms of the neurological
condition. The symptoms of NVLD also include problems related to social
interactions, reading nonverbal signs, understanding facial expressions, using
appropriate language in social situations, coordination of the body, and fine
motor abilities. Students affected by NVLD may face difficulties with
organisation, planning, and concentration, as well as reading comprehension
and writing expression at a higher educational level [38].

####  II-A8 Apraxia of Speech

A student with apraxia of speech struggles to talk clearly and make
appropriate gestures. In apraxia of speech, the speech muscles are not weak.
Rather, the brain has problems directing and/or coordinating the motions, so
the muscles do not function appropriately. Apraxia in students may cause
difficulty in imitating and producing sounds on their own, may add new sounds,
omit sounds, or pronounce sounds incorrectly, and may pronounce something
correctly one time and incorrectly the next [39].

###  II-B Assistive Technologies

Anything software, hardware or peripherals that aid students with learning
difficulties in overcoming their educational obstacles and developing new
skills fall under the umbrella of assistive technology. Students with learning
difficulties need assistive technology to improve their abilities. They will
be able to receive a high-quality education on par with their peers using
assistive technology.
Definition 1: According to WHO, the systems and services involved in providing
assistive products and services are collectively referred to as assistive
technology. [40].
Definition 2: According to the Assistive Technology Industry Association
(ATIA), any tool, piece of equipment, piece of software, or product used to
enhance, maintain, or strengthen the functional capacities of individuals with
disabilities is known as assistive technology. [41].
Definition 3: The federal defines assistive technology as any tool, apparatus,
or system, whether purchased commercially off-the-shelf, adapted, or
customized, that can be used to enhance, maintain, or improve the functional
capacity of people with impairments [42].
Definition 4: According to the United Nations Educational, Scientific and
Cultural Organization (UNESCO), anything that is utilized to enhance,
maintain, or improve the functional capacities of people with impairments is
considered assistive technology [43].
Definition 5: According to the Institute of Electrical and Electronics
Engineers (IEEE), anything that aids a person in achieving increased
performance, function, or quicker access to information is considered
assistive technology. [44].
Definition 6: According to The European Accessibility Act (EU, 2019),
assistive technology refers to any device, appliance, service, or combination
of processes, including computer programs, that is used to maximize, maintain,
replace, or enhance the functional skills of people with disabilities. [45].
Definition 7: According to the International Organisation for
Standardisation’s (ISO9999(2022)) standard on assistive products, assistive
technology is any item that was produced with a specific focus on serving the
needs of people with disabilities or that is generally available and used by
or for people with disabilities [46].
Definition 8: According to the World Intellectual Property Organization
(WIPO), the term ”assistive technology” refers to a broad range of
technologies and goods, from relatively simple gadgets like a walking stick or
reading glasses to sophisticated, high-tech systems like assistive robots or
software that recognizes gestures or emotions. [45].

##  III  The Significance of Assistive Technology in Higher Education in
Ensuring Inclusive Education for Individuals with Learning Difficulties

Based on the systematic literature review, it is understood that the
development of assistive tools in future will be heavily reliant on
contemporary technologies like AI, XR, IoT, HCI, digital twins, and the
metaverse. There are numerous studies that concentrate on lower-end assistive
technology for learning difficulties [47]. To the best of our knowledge, there
is no review that addresses all of these cutting-edge technologies for
assisting students with learning difficulties. In this section, we address the
role of AI, XR, IoT, HCI, digital twins, and the metaverse as assistive
technologies in inclusive education that assist students to overcome their
learning difficulties and Fig. 4 depicts the assistive technologies covered in
this study.

Figure 4: Assistive Technologies for Students with Learning Difficulties in
Higher Education

###  III-A Artificial Intelligence

Artificial intelligence (AI) represents significant advances in computer
science and data processing that are rapidly revolutionizing numerous sectors
[48]. AI refers to the simulation of human intelligence in machines that are
programmed to replicate human thought and behavior [49]. The use of AI makes
inclusive education a reality. Students with learning difficulties can be
integrated into regular classrooms and educated alongside their peers [50]. AI
is advancing rapidly in the education sector and reducing the gap between
students and teachers. Learning-difficulty-specific cognitive systems are also
being developed by researchers using AI [51]. Google and Microsoft are
developing AI-based tools such as the immersive reader and google docs that
can assist students with learning difficulties. The early identification of
students with learning difficulties can be facilitated by AI [52]. AI can
assist students in enhancing their reading comprehension by reading the text
aloud and also provide comprehensive feedback on written work. AI enhances
reading fluency in reading for students with learning difficulties. Students
with autism struggle with both verbal and nonverbal communication. Social
skill development can be difficult for them. To solve this problem, QTrobot
was created. This humanoid robot was developed to teach social skills to
autistic children. The robot NAO and the virtual assistant Siri are two more
examples that help students with autism spectrum disorder(ASD) learn social
skills. Tools like ActiveMath employs techniques of AI to allow students
greater flexibility in finding a convenient learning environment. The AI-
enabled Widex’s Evoke will help students in improving their hearing
capabilities. Grammarly is an AI-based writing assistant which will improve
the writing skills of students with learning difficulties.

AI can help in the early detection of learning difficulties. This can help
students understand their condition and prepare for future challenges. A.
Jothi Prabha et al. created an eye movement analysis model for the detection
of dyslexia. They used an eye tracker to observe eye movement. The eye
movement data includes fixations, saccades, transients, and distortions of the
participants. Principal component analysis was used to identify high-level
properties from raw eye tracker data. For the diagnosis of dyslexia in
students, a PSO-based Hybrid Kernel (SVM-PSO) was created. Their results
showed the proposed model’s prediction accuracy was 95% more accurate than
that of the Linear SVM model. The model was validated over 187 individuals.
They concluded that with the use of eye movement data and machine learning,
the development of very precise prediction models was possible. Their method
was also offered as a screening tool for dyslexia diagnosis [53].

Students with learning difficulties have more emotional and behavioral
challenges in the classroom than their peers without learning problems. In
order to understand this issue, Nihal Ouherrou et al. conducted research on
the benefits of using information and communication technology to understand
the emotional factors of students with learning difficulties in online
classrooms. In order to analyse the effects of the virtual learning
environment, 42 students were divided into two groups. They considered seven
basic facial expressions (fear, anger, disgust, surprise, happiness, neutral,
and sorrow ) and assessed students’ emotions using AI while they played a
learning game. The results indicated that students with learning difficulties
experience the same range of emotions as children without learning
difficulties. Furthermore, they concluded that students with learning
difficulties report fewer negative emotions compared to their peers in virtual
learning environments [54].

Tools and intelligent learning environments based on AI can be used to create
successful individualised educational techniques for children with learning
difficulties.

Students with special needs are not as likely to use e-learning as students
without special needs. Not much is known about the barriers and facilitators
that cause this difference. The opinions of 21 teachers who took part in
preliminary trials of an adaptive learning system based on multimodal affect
recognition for students with learning disabilities and autism were gathered
by Penny J. Standen et al. through focus groups and interviews. The adaptive
selection of learning materials is driven by the system’s multimodal detection
of emotional state and scoring of performance. The teachers’ thoughts on the
possible effects of the system were summed up in five themes. These themes
focused on learning, engagement, and factors that might affect adoption. These
were how the system could change the way they taught, how it could affect how
well students learned, how it could affect the relationships between teachers
and students and between students, how easy it was to use, and how it could be
set up. Even though the teachers who volunteered as testers were very
interested, they pointed out barriers to adoption that needed to be fixed.
Their finding showed how important it is for teachers and students to be
involved in the process of design and development [55].

Phaedra S. analysed the historical development of intelligent learning
environments. In addition, they reported the significant challenges that arise
with the inclusion of intelligent learning environments. Furthermore, they
examine a variety of novel strategies for addressing these issues, such as
teacher training, the employment of instructional robots, and responsive
systems. They concluded that AI in education is fast altering traditional
views on teaching and learning. They also stated that traditional school and
classroom models will change a lot over the next few years and decades as
technology improves and spreads throughout educational institutions [56].

Using multimodal sensor data and machine learning, Penelope J. Standen et al.
found that learning is linked to three emotional states: engagement,
frustration, and boredom. Then, they figured out how to present the learning
content so that the learner stays in the best emotional state and learns as
quickly as possible. 67 people between the ages of 6 and 18, who were their
own controls, took part in a series of sessions using the adaptive learning
system so that it could be evaluated. Sessions alternated between using the
system to choose the learning content based on how the learner felt and how
well they learned (intervention) and just on how well they learned (control).
Lack of boredom was the state with the strongest link to achievement, with
both frustration and engagement positively related to achievement. They
concluded that the intervention sessions were much more interesting and less
boring than the control sessions, but the amount of work done did not change
much. Their results suggest that activities that match the needs and emotions
of the learner do increase engagement and that the system promotes emotional
states that help learning. They suggested that longer exposure is also needed
to figure out what effect it has on learning [57].

In summary, AI has the ability to transform education by creating cutting-edge
teaching tools for students with special needs. In order to provide more
intelligent solutions, the industry is expanding, demanding more study and
cooperation between educators, app developers, and engineers. When AI is used
as assistant in making decisions, it is challenging to explain the decisions
made by AI because of its black-box nature. The students’ personal data may be
required for training these AI models, the protect the privacy of the students
data is also a concern.

###  III-B Extended Reality

The combination of virtual environments, human-machine interactions, and
wearable technologies is collectively referred to as extended reality (XR).
The term ”XR” encompasses both virtual reality (VR) and augmented reality
(AR). Through the use of VR, users may interact with objects and other people
in a manner that seems authentic. To add digital elements to a live, real-
world situation, AR uses a smartphone, tablet, or headset. Mixed reality (MR)
brings together the real and virtual worlds by using powerful computer
technology, images, and input methods [58].

VR and AR can assist students with learning difficulties in many ways in their
academic endeavors [59]. The students with learning difficulties are immersed
in a 3D world like CoSpaces Edu containing auditory, touch, smell, and
gustatory inputs. Students can interact by wearing a head-mounted display
(HMD) and a haptic glove or by using a regular desktop PC and VR software
[60]. By adding 3D effects to actual information, AR applications such as
Google Lens allow users to remain unbiased observers and recognise the
augmented effects. A VR environment such as Google Expeditions will allow
students with learning difficulties to participate in activities that are
unrestricted by their impairment and allows them to study in the most
effective way possible. VR can also help promote positive views toward people
with learning difficulties among their peers [61]. Individualised VR settings
provide autistic children with the opportunity to learn social interaction and
nonverbal cues [62]. Virtual environments or input stimulation are adaptable
to student preferences [63]. VR and AR can promote motivation, facilitate
engagement, strengthen cognitive skills, and improve memory in students with
learning difficulties . They can also improve communication skills, especially
among students with hearing problems [64]. VR can help autistic students with
social interaction [65]. AR can improve language through the use of games
provided by applications like Assemblr [66]. Narrator AR applications can
inspire students with learning difficulties to improve their handwriting.

Students with learning difficulties have physical, mental, and communicative
limitations [67]. Teaching individuals with learning difficulties requires a
specific blend of methods and tools. VR is one of these technologies that will
serve as an effective learning aid for students with learning difficulties.
Arik Kurniawati et al. offered these students VR games. This game motivates
students to acquire the items in a VR-based educational environment. The game
helps students practise identifying, selecting, and pointing at things in
response to visual and audio stimulation. Students with learning difficulties,
autism, and learning difficulties participated in their studies. The results
indicated that the game is completely simple and accessible for students with
learning difficulties. They determined that all participants understood the
instructions with little assistance [68].

ADHD, sometimes known as hyperactivity among students, is a prevalent
neurodevelopmental disorder. ADHD is characterised largely by hyperactivity,
inattention, and behavioural impulsivity. Traditional treatments often rely on
clinicans and parents who may observe and analyse a patient’s behaviour using
behavioural scales; however, these treatments are time-consuming and
ineffective in measuring behaviour [69]. In their experiment, Yunchuan Tan et
al. combined several sensor technologies, such as eye movement sensors and
electroencephalography sensors (EEG), and used virtual reality technology to
develop an assessment and diagnostic approach for ADHD. This system provided a
virtual classroom environment and included a number of activities, including
an audio exam. The continuous performance tasks and the Wisconsin card sorting
test was used to evaluate the students’ attention, ability to think
abstractly, and cognitive capability. They introduced distracting elements to
their experiment and analysed the test participants’ attention. In order to
assess the subject’s sustained attention and attention shift, they combined
their test results with physiological data such as head and eye movements and
EEG [70]. They concluded that their approach can improve the levels of
concentration in students with learning difficulties.

Jorge Fernandez Herrero et al. suggest a concept and application for an
immersive VR system with a head-mounted display to develop and teach the
emotional and social abilities of students with autism spectrum disorders.
They chose two groups of seven high-functioning ASD students with comparable
intellectual aptitudes. On the first group, they used immersive virtual
reality as a pedagogical tool to recreate virtual socialising contexts over
the course of 10 sessions, using their own intervention design to address
social and emotional competencies. As the control group, the second group is
not subjected to any intervention throughout the intervention period. The
degrees of adaption and the observed improvements suggested that immersive
virtual reality in the format described is consistent with the sensory
preferences and visuospatial abilities of the ASD children who participated in
this research. They concluded that immersive VR can be utilised effectively as
a teaching tool for children with ASD [71].

In summary, XR can be used to successfully improve the skills and abilities of
people with dyslexia, social anxiety, ADHD, linguistic impairments, physical
or motor disabilities, Down syndrome, and cognitive deficits. The
compatibility of these XR devices with other assistive technologies and
devices remains a challenge due to their major design flaws and lack of
standards.

###  III-C Human-Computer Interaction

Human-computer interaction (HCI) is a field of study that looks at the design
of computer technology and, in particular, the users interact with computers.
In HCI, cognitive science principles and methods allow software engineering
and the human aspects of computing systems to work well together. HCI includes
using gesture, touch, and even affords interaction via brain signals [72].

Universal design for HCI promotes inclusive education that produces accessible
products for all students, including those with learning difficulties. The
products accommodate individual preferences and abilities and efficiently
transmit essential information independent of environmental conditions or the
user’s sensory capabilities. They are manipulable, reachable, approachable,
and usable regardless of body size, posture, or mobility [73]. These design
principles result in products that are compatible with assistive technology
and are more usable for everyone [74]. Students with learning difficulties
struggle with conventional forms of expression, such as writing, but can
demonstrate their comprehension in a number of innovative HCI ways by using
video or screen-casting tools including Clips, iMovie, Audacity, and others.
HCI provides a readily accessible collaborative environment in which students
may produce and share project-based and other related materials using online
storage tools such as Dropbox. HCI also helps in the creation of student-
response tools which improve student-teacher interaction. OneNote Web Clipper
is a highly adjustable immersive reader that has options for changing the text
size and line spacing, showing the parts of speech, and more. A student with a
visual difficulty may use VoiceOver to describe what is on the screen using
synthetic voice or braille (with a linked braille display). Students may draw
thoughts with the aid of HCI visual tools like Draw.io and Popplet. These HCI
tools with universal design will support inclusive education and assist
students in overcoming their learning challenges.

The intelligent math e-tutoring system developed by Zikai Alex Wen et al.
attempts to eliminate students’ negative emotional responses. The technology
identifies potential negative emotional actions by evaluating gaze,
touchscreen inputs, and reaction time. The program then employs intervention
strategies to prevent students from becoming irritated. Formative research
carried out with five instructors of students with learning difficulties
helped to develop this design. Teachers believed that the establishment of
these intervention strategies would benefit students with learning
difficulties and stated that among the intervention strategies available to
them, giving students ’brain breaks’ is the newest and most beneficial.
Additionally, the instructors proposed that the system could be customised to
identify negative emotional responses, in order to assist students with
learning difficulties [75].

Brain-Computer Interface technology (BCI) is an important aspect of HCI. Bio-
signals acquired by wearable sensors are attracting significant interest
beyond the traditional medical arena, in new paradigms such as education [76].
Attention is a bio-signal that may be detected and analysed using BCI
technology by measuring the frequency of alpha (8-13 Hz) and beta (14-30 Hz)
waves. Attention and learning are highly interdependent. Typically, students
with attentional difficulties also have learning difficulties. According to
several instructors and professional experts, students’ attention spans are
decreasing [77]. To address this issue, Mohammed Serrhini et al. evaluated
students’ attention in online education. During the learning process,
students’ attention is maintained by an EEG-based attention evaluation system.
Attention data is maintained in a database and used by signal-processing
algorithms to comprehend student knowledge growth. They concluded that BCI can
be used to enhance learning levels in computer-based education[78].

The use of manual sign systems is a means of communication between students
with learning difficulties and their teachers. Due to a lack of learning
support resources, instructors experience several practical obstacles while
instructing children in manual sign language. To address these concerns,
Youjin Choi et al. teamed up with instructors to design the Sondam Rhythm
Game, a gesture-based rhythm game that aids the instruction of manual sign
language. They conducted a four-week study with five teachers and eight
students with learning difficulties. Based on video annotation and interviews,
their game-based method to teach manual sign language has shown significant
results. Their method increased children’s attention span and motivation, as
well as the number of spontaneous motions performed without prompting. In
order to enhance teaching paradigms for eight students with learning
difficulties, additional practical concerns and learning obstacles were
identified. Based on their outcomes, they concluded that their proposed model
method could be used to help learners improve their sign language abilities
[79].

In summary, by enabling students to engage with technology using gestures,
touch, and even brain signals, HCI can assist students in overcoming their
learning challenges [80]. Personalised user interfaces and design issues
continue to be challenging issues for HCI domain.

###  III-D Internet of Things

A system for connecting computers, mechanical and digital devices, things, or
people with distinctive identities and the ability to transfer data via a
human-to-human or computer-to-human interface is known as the Internet of
Things (IoT). Both computers and humans can use IoT devices. IoT devices can
transfer data over a network [81]. The IoT is considered by some to be a
transformative force in education. The application of the digital technologies
is not just making education omnipresent but also it is about making
conventional systems of education more efficient and inclusive. IoT plays an
important role in making education more interactive, collaborative, and
accessible to all. IoT devices affords students reliable access to all
learning resources, communication channels, and helps teachers keep track of
student learning and progression in real-time [82]. Smart boards may be used
in a similar manner to a blackboard for writing with a marker and can also
show topic-related visuals and images to students. Global Positioning System
(GPS) tracker-equipped school buses, smart security cameras, cellphones, and
tablets with instructional applications will alter how schools and educational
institutions have traditionally functioned. Teachers are concerned about
student attendance, which is a daily requirement in schools. IoT can provide a
solution to tracking and analysing student attendance for several purposes.
Students are more engaging in virtual classrooms facilitated by smartphone
applications. When they are able to comprehend more and more clearly, as
described above, they are also able to think outside the confines of the
classroom and to communicate and express their learning and questions [83].
The integration of IoT tools and smart devices will enable the educational
curriculum is being changed and classroom surroundings are being made sound
and light-sensitive to accommodate children with sensory disorders. In
academia, IoT sensors collect data and automatically propose academic topics
of interest to students for future learning processes [84]. These qualities
make it more practical, and accessible for the students, instructors, and
parents. It is a well-known reality that a quick transition of teaching
methods and techniques cannot be done, but these gadgets are being customised
and updated with the necessary software gradually and over time.

According to statistics, ASD has resulted in serious learning disorders. To
address this issue, Raja et al. used Raspberry Pi to construct a system for
evaluating the effectiveness of a smart monitor in assisting students with
autism to learn and enhance their quality of life. They suggested a framework
to support students with autism by assisting them in making choices,
responding by telling parents what they are interested in and identifying
their needs. They implement and evaluate a novel IoT-based system for
supporting learning and enhancing the quality of life for students with
autism. They claimed that their system could assist students with autism in
understand any subject [85].

Anna Lekova et al. designed, produced, and experimentally validated a Speech
and Language Therapy (SLT) system for students with communication impairments.
Their approach is able to interact with the IoT in order to assist SLT
services in many educational and social contexts. It can link various
assistive devices, APIs, online services, and agents to meet the particular
requirements of each student using the intervention. Node-RED is used to link
a humanoid NAO-type robot, an Emotiv EPOC+ brain headset, an emotionally
expressive EmoSan robot, and a Kinect depth sensor. It is a flow-based tool
for visual programming without the need to write code, and it can operate
locally or on the IoT. The proposed system is sufficiently broad to be adapted
to various kinds of therapy and to enable additional assistive devices and
cloud services [86].

Permanent or temporary Vision impairments present a number of obstacles in the
daily living of a student with a learning difficulties. A student with vision
impairment may be unable to distinguish between colours, which is a crucial
aspect of work in various sectors. Humayun Rashid et al. developed a colour-
detecting system for the visually impaired. They addressed two extremely
crucial difficulties for visually impaired individuals overcoming obstacles
and falling. The proposed system combines the most recent hardware components,
including an improved central processing unit, sensors for IoT and cloud-based
architecture that effectively detects colour and obstacles. Moreover, it also
alerts visually impaired individuals about colour and obstacles in multiple
languages. In the event of fall detection, this proposed system also transmits
a fall notice to the caretaker of the visually impaired individual, which is a
major component of this work.

In summary, IoT can transform conventional learning methods and help students
overcome their learning difficulties [87]. Security, standards, and dependence
on AI judgements are challenges associated with the use of IoT in providing
support for students with specific learning difficulties.

###  III-E Digital Twins

The idea of using ”digital twins” comes from NASA’s Apollo program, in which
at least two identical space vehicles were built. This allowed engineers to
replicate the conditions of the spaceship during the trip, and the vehicle
that stayed on Earth was called the twin [88]. In 2002, Michael Grieves was
the first person to talk about a Digital Twin (DT) [89]. Previous research on
DT definitions has shown that each system is made up of two parts: the
physical system and a virtual system that includes all of the physical
system’s knowledge. Siemens describes it ”A digital twin is a digital copy of
a real product or process that is used to study and predict how it will work
in the real world.” Throughout the life-cycle of a product, digital twins are
used to predict, simulate, and improve the product and manufacturing system
before investing in real prototypes and assets [90].

A DT can help teachers to understand their students better and reduce the
integration problems of a students with learning difficulties into inclusive
higher education. Students with learning difficulties may find it difficult if
they are repeatedly challenged to modify their classroom behaviour. The
teachers may not have a good understanding of these students’ behaviour. If a
DT is built for a student, it enables teachers to carry out analysis of
behaviour on the DT to give student related insights and help teachers to best
support the academic and behavioural outcomes for their students. DT’s also
enable students with learning difficulties to work on digitally depicted
scenarios and prepare them for the challenges of the real world. The
combination of DT and VR will assist students in overcoming the challenges
they face that are a result of their learning difficulties.

In conclusion, DT offers significant potential to address educational and
behavioural issues relevant to students with learning difficulties. The
application of DTs as an assistive technology remains largely unexplored
territory. A major challenge for the widespread application of DTs is the
inaccurate representation of the twin, since the construction of the twin
relies on many different technologies, including AI, IoT, among others, and
any errors in those interconnected technologies will result in the definition
of an incorrect twin.

###  III-F The Metaverse

The word ”meta” is a Greek word that signifies ”more complete” or
”transcending” and ”Verse” is an abbreviated form of ”universe.” In his famous
science fiction novel ’Snow Crash’ published in 1992, the idea of the
metaverse was first introduced by Neal Stephenson, in which people use digital
avatars to control and compete with one another in order to advance their
position. The use of VR and AR equipment helps the metaverse become more
widespread [91]. The metaverse is often described as a collection of socially
conscious 3D virtual worlds [92].

The metaverse is a near-term technology offering significant opportunities for
affording inclusive higher education. Regardless of their actual location and
learning difficulties in the metaverse, students and teachers can meet in the
digital world using their virtual reality headsets, promoting inclusive higher
education. This capability can improve the teaching of individuals with
learning difficulties. Inclusive design of the metaverse is however crucial to
long term adoption in a number of domains including education [93]. The
metaverse with XR has endless possibilities, with a potential influence on
higher education that is especially significant for students with learning
difficulties [94]. An inclusive metaverse-based school would allow teachers to
not only speak about their discoveries but also demonstrate them in a 3D
environment. Together with their classmates, students with learning
difficulties can engage in serious questioning and use firsthand experiences
to assist in their academic development. No longer will students with learning
disabilities be forced to sit in a traditional classroom with nothing to do.
They can instead learn alongside their peers due to the educational and social
opportunities afforded by the metaverse [95].

The use of the metaverse in inclusive education offers several potential
advantages over traditional models, allowing students with learning
difficulties to experience historical sites or conduct dangerous experiments
in a secure, virtual setting alongside their classmates [96]. Moreover, the
metaverse learning environments can encourage safety in a manner that
traditional classrooms cannot. Educators will have total control over student
interactions in the metaverse and will be able to prohibit bullying. Thus,
students with learning difficulties may concentrate on their education without
worrying about bullies or other disruptions [97]. Roblox, Minecraft,
Decentraland, Sandbox, Axie Infinity and Fortnite are some of available the
metaverse projects [98].

In conclusion, the metaverse offers significant potential for supporting
students with learning challenges since it is a hybrid of cutting-edge and
established technologies. The standardisation and interoperability of multiple
technologies enable the metaverse to present a significant opportunity as an
assistive technology for students with learning difficulties.

##  IV  Tools

In this section, we discuss a variety of tools for assisting students with
various learning difficulties. An overview of these tools are shown in Table
III.

TABLE III: An Overview of Tools for Assisting Students with Learning Difficulties Tool Name |  Disability Assisted |  AT |  Functionality
---|---|---|---
KURZWEIL 3000 |  Dyslexia |  AI |  It enables Text-to-Speech voices are accessible in 18 dialects and languages.
QTrobot |  Autism |  AI |  It can teach communication, emotions, and social skills through the use of facial expressions, gestures, and games.
ActiveMath |  Dyscalculia |  AI |  It can assist students develop interactive courses depending on the student’s goals, preferences, skills, and prior knowledge.
Widex’s Evoke |  CAPD |  AI |  It learns from the listening preferences of users throughout the world, bringing the development toward improved hearing out of the laboratory and into the real world.
Microsoft Immersive Reader |  Dyslexia, Dysgraphia, CAPD, VPD, and NVLD |  AI |  It Enhance reading and writing for people of all ages and abilities.
Grammarly |  Dysgraphia, CAPD, VPD, and NVLD |  AI |  It recognise faults in writing and look for a suitable replacement.
Google Glass |  CAPD, VPD, and NVLD |  IoT, XR and AI |  It provides basic voice or vision commands for online and Internet engagement.
Google Expeditions |  ALL |  XR |  Itis essentially a collection of XR experiences and ”field excursions” offered by Google.
Merge Cube |  ALL |  XR |  It makes possible to study and interact with manipulate 3D digital things.
CoSpaces Edu |  ALL |  XR |  It helps students to create and engage with interactive media content.
Assemblr |  ALL |  XR |  Assemblr will enable educators to construct 3D objects and scenarios for classroom.
Narrator AR |  Dysgraphia, CAPD, VPD, and NVLD |  XR |  Narrator AR helps students improve their handwriting.
Augmented Classroom |  ALL |  XR |  It lets teachers provide their students access to 3D augmented environment.
Roblox |  ALL |  Metaverse |  it is a resource for teaching students about computer programming, animation, 3D design, and application development.
Minecraft |  ALL |  Metaverse |  It is a digital world designed to foster innovation, teamwork, and problem-solving though gaming.
VoiceOver |  VPD and NVLD |  AI |  It can describe what’s on the screen in synthetic voice or braille through a linked braille display.
Voice Dream Reader |  dyslexia |  AI |  It is a fully functional document manager that enhances the built-in text to speech functionality with additional personalization options.
TouchCast Studio |  ALL |  AI |  It is a application that provides students with all the tools they need to create interactive films with hyperlinked hotspots.
Book Creator |  ALL |  AI |  It isa blank canvas on which students may generate an ebook.

###  IV-A KURZWEIL 3000

It is educational software designed to assist children with reading
difficulties at home, at school, or in the workplace. This freeware includes
OpenDyslexic typeface and text magnification to improve the readability of
text for dyslexic students. Its 31 Natural Text-to-Speech voices are
accessible in 18 dialects and languages, allowing students to access the same
materials as their classmates. For a more meaningful learning experience, a
test-preparation toolbar is accessible to students who wish to build their own
evaluation [99].

###  IV-B QTrobot

QTrobot is a small, expressive AI-enabled humanoid developed for use by
therapists and teachers. Children with autism spectrum disorder are taught
communication, emotions, and social skills through the use of facial
expressions, gestures, and games [100].

###  IV-C ActiveMath

ActiveMath is a web-based AI-learning system that develops interactive
(mathematics) courses depending on the student’s goals, preferences, skills,
and prior knowledge. The material is delivered in an XML-based, semantic
manner. The required information is acquired from a knowledge base for each
student, and the course is constructed based on educational guidelines. The
student is then provided with the course using a conventional web browser.
ActiveMath is distinguished by its incorporation of independent mathematical
service systems. This facilitates exploratory learning, realistically
complicated activities, and the acquisition of proof methods [101].

###  IV-D Widex’s Evoke

Widex’s EVOKE is the first hearing aid in the world to incorporate machine
learning. Every day, it improves the audio experience of every user. In
addition, it learns from the listening preferences of users throughout the
world, bringing the development toward improved hearing out of the laboratory
and into the real world [102].

###  IV-E Microsoft Immersive Reader

The free AI tool Microsoft’s Immersive Reader enhance reading and writing for
people of all ages and abilities. It is integrated into Word, OneNote,
Outlook, Office Lens, Microsoft Teams, Reading Progress, Forms, Flipgrid,
Minecraft Education Edition, and the Edge web browser [103].

###  IV-F Grammarly

Grammarly is a typing assistant that uses AI and the cloud to check for
problems in spelling, grammar, punctuation, clarity, engagement, and delivery.
AI is used to recognise faults and look for a suitable replacement.
Additionally, it gives users the option to customise their language, tone, and
context[104].

###  IV-G Google Glass

Google Glass will provide basic voice or vision commands for online and
Internet engagement. Google Glasses can easily power mobile devices like
smartphones or tablets and may be thought of as wearable computers in this
fundamental sense. The basic identification tool for blind and visually
impaired people using the Google Glass camera. This viewpoint makes it
possible to describe Google Glass as an assistive technology[105].

###  IV-H Google Expeditions

Google Expeditions is essentially a collection of Augmented and Virtual
Reality experiences and ”field excursions” offered by Google. Some of the
’expeditions’ are more supported than others with lesson plans, supporting
links, and background information [106].

###  IV-I Merge Cube

The Merge Cube makes it possible to study and interact with the digital world
in a whole new manner by letting you manipulate 3D digital things. In addition
to many other things, students may inspect a DNA molecule, research the
Earth’s core, dissect a virtual frog, hold and share their own 3D creations,
and explore a galaxy in the palm of their hand [107].

###  IV-J CoSpaces Edu

Users of CoSpaces, a web-based XR tool, may create and engage with interactive
media content. CoSpaces allows students to demonstrate their knowledge in
fresh ways by building interactive virtual settings that might be simple or
complex but are still user-friendly for beginners [108].

###  IV-K Assemblr

Assemblr will enable educators to construct 3D objects and scenarios for
classroom usage. Students will have a better learning experience while
utilising the Assemblr application to engage in AR, and VR [109].

###  IV-L Narrator AR

An augmented reality software, Narrator AR helps students improve their
handwriting. When a child’s handwritten name is scanned, the application
superimposes an animation showing the name blasting off the paper in the form
of a rocket or a rainbow unicorn trail. Using the Narrator AR Mobile
application, students can make a connection with their writing as virtual
letters are lifted from the paper [110].

###  IV-M Augmented Classroom

The purpose of the CleverBooks Augmented Classroom (ARC) is to let teachers
provide their students access engaging, interactive courses by delivering
content in fully immersive, 3D augmented settings. When used in the classroom,
ARC’s 3D environment has been shown to boost student engagement and
motivation, leading to better academic outcomes [111].

###  IV-N Roblox

Roblox Studio is a free, family-friendly resource for teaching students about
computer programming, animation, 3D design, and application development. Using
Roblox Studio in the classroom boosts students’ self-esteem by giving them a
real-world platform on which to practice the scientific inquiry skills
outlined in the Next Generation Science Standards (NGSS). Students can travel
across time and space in Roblox adventures that are set up for exploration,
investigation, and experimentation. Students may experience and analyse
scientific events, go back in time to ancient Rome, and even construct virtual
robots to compete against one another in friendly or hostile team challenges
[112].

###  IV-O Minecraft

The educational version of Minecraft is a digital world designed to foster
innovation, teamwork, and problem-solving through gaming. Teachers worldwide
can use Minecraft education edition to capture their students’ interest in a
wide range of disciplines and make abstract concepts more concrete [113].

###  IV-P VoiceOver

The iOS operating system has a built-in screen reader called VoiceOver. It can
describe what’s on the screen in synthetic voice or braille through a linked
braille display for the visually impaired [114].

###  IV-Q Voice Dream Reader

Voice Dream Reader is a fully functional document manager that enhances the
built-in text-to-speech functionality with additional personalisation options.
These options include masking to display only a small portion of the text,
support for dyslexia-friendly fonts, fully customisable colours for word and
sentence highlighting, and more. Documents may be imported from a number of
sources, such as Google Drive, Dropbox, and Bookshare, a programme that
provides students with qualifying reading difficulties with free access to
books in accessible formats [115].

###  IV-R TouchCast Studio

TouchCast Studio is a free iPad application that provides students with all
the tools they need to create interactive films with hyperlinked hotspots.
Students can utilise a variety of video applications to link to supporting
research on the Internet, ask questions through polls, link to an accessible
script of their film, and more. The application has many complicated features,
like a built-in teleprompter and green-screen features that let students work
themselves from different places. It also works with multiple iPhone cameras
[116].

###  IV-S Book Creator

The Book Creator provides a blank canvas on which students may generate an
ebook to demonstrate their comprehension and incorporate all of their media.
Each book may have text, photographs with descriptive captions, audio, and
video. The free, fully-functional version of Book Creator for iPad may be used
to produce one book. Upgrade to the premium version to have access to
limitless publishing and comic book templates. There is also a Chromebook-
compatible web-based version of Book Creator; with this edition, users may
generate up to 40 free booklets [117].

##  V Projects

In this section, we discuss some related projects that work toward students
with learning difficulties and inclusive education. The overview of the
related projects that work towards students with learning difficulties and
inclusive education is shown in Table IV

TABLE IV: Overview of related projects that work towards the student with learning difficulties and inclusive education Project Name |  Key Action |  Action Type |  Countries covered
---|---|---|---
IncED |  Partnerships for cooperation and exchanges of practices |  Cooperation partnerships in youth |  Latvia, Portugal, Estonia, and Spain
Fairness Inclusive |  Learning Mobility of Individuals |  Youth mobility |  Russia, Romania, France, and Germany
InClusion Team |  Cooperation for innovation and the exchange of good practices |  Strategic Partnerships for school education |  Portugal, Greece, and Spain
ELLeN |  Cooperation for innovation and the exchange of good practices |  Strategic Partnerships for higher education |  Germany, Austria, and Belgium
Accessible Peer Interaction with Disabled Youth |  Accessible peer interaction with disabled youth |  To impact young people, thereby investing in the future and providing the proper basis for a spillover effect into wider society and into the working environment. |  Bulgaria, Belgium, Austria, and United Kingdom

###  V-A IncED

This project is managed by the private organisation Vivere kool MTU and funded
by Erasmus+, the European Union’s education, training, youth, and sports
programme. This project focuses on inclusion, fostering equality and non-
discrimination, and using modern teaching-learning techniques. The project’s
objectives are to better understand concepts of mixed-ability and inclusive
collaboration and to highlight it as a possibility rather than a challenge, to
adapt current systems and build innovative educational games and methodologies
for collaborating in mixed-ability groups that can be used in non-formal and
formal learning contexts for students aged 13 to 18 years old, to promote
inclusive education among various types of stakeholders, and to improve the
youth workforce [118].

###  V-B Fairness Inclusive

This project is handled by the Kreisau Initiative association and funded by
Erasmus+, the European Union’s programme for education, training, youth, and
sport. This project focuses on disability and special needs, equality and
access for the underprivileged. One of the primary objectives of the
initiative is to engage disadvantaged youth in international educational
activities. This engagement help the emancipation of young people so that they
may lead more independent lives and treat others with tolerance, solidarity,
and respect in the future. The underlying belief of this objective is that it
increases their social inclusion and minimises their marginalisation [119].

###  V-C InClusion Team

This project is managed by Fyllingsdale High School in Norway. The partners
include universities, secondary schools, a public teacher training centre, and
educationally focused non-governmental organisations. Erasmus+, the European
Union’s programme for education, training, youth, and sport, is funding this
project. This project focuses on new technologies, digital skills, access for
the disadvantaged, and particular requirements for those with impairments. The
project aims to establish a learning community in which universities, schools,
teacher training centres, and non-governmental organisations share best
practices for teaching ICT to students and individuals with special needs. The
collaborating parties intend to equip educators with training and expand
access to high-quality learning tools, and materials [120].

###  V-D ELLeN

This project is managed by Goethe University Frankfurt and funded by Erasmus+,
the European Union’s education, training, youth, and sport programme. In this
project, a teacher will teach students how to research the needs of a certain
group of learners by interviewing those learners. In other words, neurodiverse
learners will be treated as experts in their own learning process. By helping
pre-service teachers build up their Inquiry-based learning skills and
competencies, this initiative also shows how to partnership with neurodiverse
learners, the strengths and needs of the target population’s learners can be
identified, evaluated, and met in learning environments.[121].

###  V-E Accessible Peer Interaction with Disabled Youth

The National Association of Professionals Working with People with
Disabilities manages this project (NARHU, Bulgaria). This initiative is
supported by Erasmus+, the education, training, youth, and sports programme of
the European Union. This project targets youth workers and leaders, student
leaders, student bodies, youth organisation leaders, and representatives of
disabled youth organisations. The work aims to assist youth workers in
developing and disseminating effective strategies for reaching out to
marginalised youth, refugees, asylum seekers, and migrants, as well as
combating racism and intolerance among young people [122].

##  VI  Challenges and Future Directions

This section discusses challenges and future directions of assistive
technologies for students with learning difficulties in higher education. An
overview of these challenges and future directions is depicted in Fig 5.

Figure 5: Challenges and Future Directions of Assistive Technologies for
Students with Learning Difficulties in Higher Education

###  VI-A Personalization of Assistive Technologies

Challenge: It is not possible for a single assistive technology to represent a
universal solution to the learning challenges faced by all learning
difficulties. The need for these assistive technologies varies with the needs
and preferences of each student with learning difficulties. Some students may
or may not need the full functionality of a particular assistive technology,
while others may require a combination of the functionality of many assistive
technologies. Personalisation of these assistive technologies is very
important for inclusive education, but which still remains a challenge that
must be addressed.

Potential Solution: Students with learning difficulties may customise their
assistive technology to meet their needs in a virtual world by using automated
3D modelling and printing. The organisations must also permit mass
customisation of educational aids that help students overcome their learning
difficulties [123].

###  VI-B Design Issues

Challenge: A variety of computer interfaces and technologies will be used for
academic purposes by students without learning difficulties. While individuals
with learning difficulties need a personalised environment, this may or may
not be available in their classroom environments. If the instructor does not
provide such assistive technology to the students, the inclusive education
concept would be compromised. The challenge with HCI still exists, and it not
feasible for students with learning difficulties to use the same computer
interfaces and technology as effectively as their classmates.

Potential Solution: The usage of personas in designing an HCI takes into
account a variety of characters with and without learning difficulties, which
aids in the better design of services and products that can be utilised by
everyone regardless of any difficulties [124].

###  VI-C Interoperability Problem among Assistive Technologies

Challenge: The devices and technologies used in the creation of various
assistive technologies may not be developed by a single organisation. There
are currently no interoperability standards or regulations in place for
assistive technology like the metaverse. As a result, these assistive
technologies will raise integration and interoperability challenges. This
could result in the student losing focus in an inclusive educational
environment.

Potential Solution: Instead of only providing best practices, there is a need
for standards that address these issues relating to the creation of assistive
technologies. These guidelines need to specify the industry standards for
developing assistive technologies. Furthermore, verification and
certifications from the scientific community are required before an assistive
technology is delivered to the market. There should be specific guidelines for
utilising them while adapting to the real world. This will allow enterprises
to build assistive devices more quickly and at a lower cost. Additionally,
this will also solve the interoperability problems with different assistive
technologies [125].

###  VI-D Challenge with Traditional Classroom Environments

Challenge: Traditional classroom environments may have a negative impact on
all students to some extent, at some time, and in some way, but students with
learning difficulties are more vulnerable. This difficulty stems from their
difficulties with speaking, writing, or thinking. Incorporating assistive
technology into a conventional classroom environment exacerbates the challenge
by requiring students to bring gadgets to class and use them to overcome their
difficulties. These students’ gadgets are unusual among their classmates, and
if any technical difficulties arise, they further exacerbate their problems.

Potential Solution: A Virtual Interactive Inclusive Classroom (VIIC) can help
students with learning difficulties overcome the challenges they face in
traditional classroom settings. Students can interact with one another in this
class while taking classes from a convenient location. The virtual
interactive, inclusive classrooms will use cutting-edge technology such as
Explainable Artificial Intelligence (XAI) to help students make decisions,
sophisticated Mixed Reality (MR) for in-person interactions, and advanced
robotic assistance that can be linked with the brain of a student with a
learning difficulties to help them do academic tasks. Thus, VIIC can help
students overcome their disabilities and compete with their peers [126].

###  VI-E Privacy and Justification Issues

Challenge: AI can be combined with other technologies, such as digital twins,
IoT, HCI, and the metaverse, to produce better outcomes. Students with
learning difficulties must share personal data to receive decision-making
assistance from AI models that support other technologies. These AI models
cannot provide privacy for data provided by students with learning
difficulties, which may create distrust of assistive technologies. The
recommendation from AI-based assistive technologies cannot be completely
depended on as they are black box in nature [127].

Potential Solution: Federated learning can help with the problem of data
privacy in assistive technologies. Federated learning addresses data ownership
and privacy by guaranteeing that data never leaves dispersed node devices.
Simultaneously, the global model is updated and distributed to all network
nodes. This ensures the privacy of the students’ data [128]. XAI can help AI-
based decisions more justifiable and accountable. XAI is a set of procedures
and techniques that allow users to understand and rely on the outcomes. XAI
will increase the accountability of recommendations. XAI can explain the
anticipated outcomes and any potential biases. It aids in describing a
suggestion’s accuracy, fairness, and transparency and improves decision-
making. XAI increases trust and confidence in assistive technology suggestions
[129].

##  VII Roadmap

Based on the findings of the systematic literature review that we carried out,
AI, XR, IoT, HCI, digital twins, and the metaverse are the potential
technologies that can assist students with learning difficulties. In the past
few decades, AI and HCI have played a significant role in supporting students
who struggle with a variety of learning difficulties. As research progresses,
AI and HCI as forms of assistive technology will continue to develop. IoT as
an assistive technology enhances the voice and vision and also provides real-
time data on various challenges faced by students with learning difficulties
using sensors. AR and VR, as assistive technologies, will effectively assist
students with learning difficulties in engaging in activities and social
integration. Even though the IoT, AR, and VR are already being used as
assistive technologies in developed countries, it will take a significant
amount of time for these technologies to catch on in developing or
underdeveloped countries. Assistive technologies like digital twins require a
huge number of IoT sensors to work effectively, and these digital twins, which
are replicas of real-world objects, can help experts mitigate the challenges
of learning difficulties virtually before being applied to the real world. The
metaverse as assistive technology, which is enabled by various technologies
like blockchain, edge computing, quantum computing, 3D modelling, XR, IoT, 6G,
AI, and others, will make students with learning difficulties equivalent to
their peers. The digital twins and the metaverse will be the future of
assistive technologies that will help students overcome the challenges raised
by their learning difficulties.

##  VIII Conclusion

This survey aims to understand the potential role of a range of recent
developments in technology in providing inclusive education for students with
learning disorders in higher education. Throughout this process, we have
analysed the learning and support needs that arise as a result of a student’s
learning difficulties and how these recently technologies developed can
support inclusive education for students with learning difficulties. We have
searched a range of online digital libraries to locate journal articles and
conference papers relevant to our study. According to our examination of the
relevant literature, there has been little research on assistive technologies
related to XR, IoT, digital twins, and the metaverse. The use of AI and HCI as
assistive technologies is more prevalent than other technologies. It is also
understood that research on aiding students with learning difficulties in
primary education is more widespread than in higher education. The selection
of these technologies within this review is also supported by recent reports
identifying them as important developments in educational technology for
higher education in the near, mid and longer term future. Our review has also
highlighted projects related to assistive technology for the inclusive
education students with learning difficulties in higher education. We also
proposed strategies that may aid individuals with learning difficulties in
higher education. Moreover, we highlighted the challenges of using assistive
technology in providing inclusive education for students with learning
difficulties in higher education and providing potential solutions. We aim to
provide a road map to describe an accessible and inclusive higher education
system using important and highly promising technologies to support students
with learning difficulties.

## References

  * [1] _“World Report on Disability 2011”_ , [Accessed on 26.05.2022]. [Online]. Available: https://www.who.int/teams/noncommunicable-diseases/sensory-functions-disability-and-rehabilitation/world-report-on-disability
  * [2] _“Nearly 240 million children with disabilities around the world, UNICEF’s most comprehensive statistical analysis finds”_ , [Accessed on 26.05.2022]. [Online]. Available: https://www.unicef.org/press-releases/nearly-240-million-children-disabilities-around-world-unicefs-most-comprehensive#:~:text=NEW%20YORK%2C%2010%20November%202021,%2Dbeing%2C%20the%20report%20says.
  * [3] _“The State of Learning Disabilities: Understanding the 1 in 5 ”_ , [Accessed on 10.06.2022]. [Online]. Available: https://www.ncld.org/wp-content/uploads/2017/03/1-in-5-Snapshot.Fin_.03142017.pdf
  * [4] S. Schnepel and P. Aunio, “A systematic review of mathematics interventions for primary school students with intellectual disabilities,” _European Journal of Special Needs Education_ , vol. 37, no. 4, pp. 663–678, 2022.
  * [5] L. Mizen and S.-A. Cooper, “Learning disabilities,” _Medicine_ , vol. 40, no. 11, pp. 619–622, 2012.
  * [6] J. Khalil, S. Boutros, N. Kheir, M. Kassem, P. Salameh, H. Sacre, M. Akel, S. Obeid, and S. Hallit, “Eating disorders and their relationship with menopausal phases among a sample of middle-aged lebanese women,” _BMC Women’s Health_ , vol. 22, no. 1, pp. 1–10, 2022.
  * [7] M. G. Badilla-Quintana, E. Sepulveda-Valenzuela, and M. Salazar Arias, “Augmented reality as a sustainable technology to improve academic achievement in students with and without special educational needs,” _Sustainability_ , vol. 12, no. 19, p. 8116, 2020.
  * [8] _”The UNESCO Salamanca Statement”_ , [Accessed on 01.03.2022]. [Online]. Available: http://www.csie.org.uk/inclusion/unesco-salamanca.shtml#:~:text=In%20June%201994%20representatives%20of,inclusion%20to%20be%20the%20norm.
  * [9] S. G. Aly, H. Harmanani, R. K. Raj, and S. Sharafeddine, “Abet accreditation: A way forward for pdc education,” in _2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)_. IEEE, 2021, pp. 328–335.
  * [10] C. J. Meijer and A. Watkins, “Financing special needs and inclusive education–from salamanca to the present,” _International Journal of Inclusive Education_ , vol. 23, no. 7-8, pp. 705–721, 2019.
  * [11] A. M. Hayes and J. Bulat, “Disabilities inclusive education systems and policies guide for low-and middle-income countries. occasional paper. rti press publication op-0043-1707.” _RTI International_ , 2017.
  * [12] N. Martiniello, W. Eisenbarth, C. Lehane, A. Johnson, and W. Wittich, “Exploring the use of smartphones and tablets among people with visual impairments: Are mainstream devices replacing the use of traditional visual aids?” _Assistive Technology_ , vol. 34, no. 1, pp. 34–45, 2022.
  * [13] A. K. Soetan, A. O. Onojah, T. B. Alaka, and A. A. Onojah, “Attitude of hearing impaired students towards assistive technology utilization in oyo state adopting the survey method,” _Indonesian Journal of Community and Special Needs Education_ , vol. 1, no. 2, pp. 103–118, 2021.
  * [14] R. MacConville, _Looking at inclusion: Listening to the voices of young people_. SAGE, 2007.
  * [15] I. Kouatli, “The need for social and academic responsibility advisor (sara): a catalyst toward the sustainability of educational institutes,” _Social Responsibility Journal_ , vol. 16, no. 8, pp. 1275–1291, 2020.
  * [16] N. Zeeni, R. Doumit, J. Abi Kharma, and M.-J. Sanchez-Ruiz, “Media, technology use, and attitudes: Associations with physical and mental well-being in youth with implications for evidence-based practice,” _Worldviews on Evidence-Based Nursing_ , vol. 15, no. 4, pp. 304–312, 2018.
  * [17] A. Kohli, S. Sharma, and S. K. Padhy, “Specific learning disabilities: Issues that remain unanswered,” pp. 399–405, 2018.
  * [18] S. Field, M. D. Sarver, and S. F. Shaw, “Self-determination: A key to success in postsecondary education for students with learning disabilities,” _Remedial and special education_ , vol. 24, no. 6, pp. 339–349, 2003.
  * [19] J. Pilgrim, C. Bledsoe, and S. Reily, “New technologies in the classroom.” _Delta Kappa Gamma Bulletin_ , vol. 78, no. 4, 2012.
  * [20] H. P. Parette and J. B. Stoner, “Benefits of assistive technology user groups for early childhood education professionals,” _Early Childhood Education Journal_ , vol. 35, no. 4, pp. 313–319, 2008.
  * [21] R. A. Bryant, A. G. Harvey, S. T. Dang, T. Sackville, and C. Basten, “Treatment of acute stress disorder: a comparison of cognitive-behavioral therapy and supportive counseling.” _Journal of consulting and clinical psychology_ , vol. 66, no. 5, p. 862, 1998.
  * [22] E. C. Bouck, T. T. Doughty, S. M. Flanagan, K. Szwed, and L. Bassette, “Is the pen mightier? using pentop computers to improve secondary students’ writing,” _Journal of Special Education Technology_ , vol. 25, no. 4, pp. 33–47, 2010.
  * [23] I. Svensson, T. Nordström, E. Lindeblad, S. Gustafson, M. Björn, C. Sand, G. Almgren/Bäck, and S. Nilsson, “Effects of assistive technology for students with reading and writing disabilities,” _Disability and Rehabilitation: Assistive Technology_ , vol. 16, no. 2, pp. 196–208, 2021.
  * [24] P. Reddy, E. Reddy, V. Chand, S. Paea, and A. Prasad, “Assistive technologies: Saviour of mathematics in higher education,” _Frontiers in Applied Mathematics and Statistics_ , vol. 6, p. 619725, 2021.
  * [25] P. D. Barua, J. Vicnesh, R. Gururajan, S. L. Oh, E. Palmer, M. M. Azizan, N. A. Kadri, and U. R. Acharya, “Artificial intelligence enabled personalised assistive tools to enhance education of children with neurodevelopmental disorders—a review,” _International Journal of Environmental Research and Public Health_ , vol. 19, no. 3, p. 1192, 2022.
  * [26] W. Farhan and J. Razmak, “A comparative study of an assistive e-learning interface among students with and without visual and hearing impairments,” _Disability and Rehabilitation: Assistive Technology_ , vol. 17, no. 4, pp. 431–441, 2022.
  * [27] K. Dawson, P. Antonenko, H. Lane, and J. Zhu, “Assistive technologies to support students with dyslexia,” _Teaching exceptional children_ , vol. 51, no. 3, pp. 226–239, 2019.
  * [28] R. T. Kompen, P. Edirisingha, X. Canaleta, M. Alsina, and J. M. Monguet, “Personal learning environments based on web 2.0 services in higher education,” _Telematics and informatics_ , vol. 38, pp. 194–206, 2019.
  * [29] C. Bugaj, _The New Assistive Tech: Make Learning Awesome for All!_ International Society for Technology in Education, 2022.
  * [30] M. O. Ogundele and M. Morton, “Classification, prevalence and integrated care for neurodevelopmental and child mental health disorders: A brief overview for paediatricians,” _World Journal of Clinical Pediatrics_ , vol. 11, no. 2, p. 120, 2022.
  * [31] B. Kitchenham, O. P. Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, “Systematic literature reviews in software engineering–a systematic literature review,” _Information and software technology_ , vol. 51, no. 1, pp. 7–15, 2009.
  * [32] O. L. Usman, R. C. Muniyandi, K. Omar, and M. Mohamad, “Advance machine learning methods for dyslexia biomarker detection: A review of implementation details and challenges,” _IEEE Access_ , 2021.
  * [33] Á. O’Dea, S. Coote, and K. Robinson, “Children and young people’s experiences of living with developmental coordination disorder/dyspraxia: study protocol for a qualitative evidence synthesis,” _HRB open research_ , vol. 2, 2019.
  * [34] M. Cappelletti, R. Chamberlain, E. D. Freeman, R. Kanai, B. Butterworth, C. J. Price, and G. Rees, “Commonalities for numerical and continuous quantity skills at temporo-parietal junction,” _Journal of Cognitive Neuroscience_ , vol. 26, no. 5, pp. 986–999, 2014.
  * [35] G. Dimauro, V. Bevilacqua, L. Colizzi, and D. Di Pierro, “Testgraphia, a software system for the early diagnosis of dysgraphia,” _IEEE Access_ , vol. 8, pp. 19 564–19 575, 2020.
  * [36] D. R. Moore, “Auditory processing disorder (apd),” _Ear and Hearing_ , vol. 39, no. 4, p. 617, 2018.
  * [37] S. Janarthanan, “Visual processing disorder in children,” _Ophthalmology Open Journal_ , vol. 2, no. 2, pp. 45–47, 2017.
  * [38] S. M. Banker, B. Ramphal, D. Pagliaccio, L. Thomas, E. Rosen, A. N. Sigel, T. Zeffiro, R. Marsh, and A. E. Margolis, “Spatial network connectivity and spatial reasoning ability in children with nonverbal learning disability,” _Scientific reports_ , vol. 10, no. 1, pp. 1–10, 2020.
  * [39] H. Botha and K. A. Josephs, “Primary progressive aphasias and apraxia of speech,” _Continuum: Lifelong Learning in Neurology_ , vol. 25, no. 1, p. 101, 2019.
  * [40] _“Assistive technology”_ , [Accessed on 06.06.2022]. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/assistive-technology
  * [41] _“What is Assistive technology”_ , [Accessed on 06.06.2022]. [Online]. Available: https://www.atia.org/home/at-resources/what-is-at/
  * [42] _“SUBCHAPTER I—GENERAL PROVISIONS”_ , [Accessed on 06.06.2022]. [Online]. Available: https://www.govinfo.gov/content/pkg/USCODE-2011-title20/html/USCODE-2011-title20-chap33-subchapI.htm
  * [43] _“Assistive technology”_ , [Accessed on 06.06.2022]. [Online]. Available: http://uis.unesco.org/en/glossary-term/assistive-technologies
  * [44] _“Help Build the Future of Assistive Technology”_ , [Accessed on 06.06.2022]. [Online]. Available: https://spectrum.ieee.org/assistive-technology-degree
  * [45] _“WIPO Technology Trends 2021”_ , [Accessed on 06.06.2022]. [Online]. Available: https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055_2021.pdf
  * [46] _“Assistive products”_ , [Accessed on 06.06.2022]. [Online]. Available: https://www.iso.org/standard/72464.html
  * [47] A. Tarhini, J. Tarhini, and A. Tarhini, “Information technology adoption and implementation in higher education: Evidence from a case study in lebanon,” _International Journal of Educational Management_ , 2019.
  * [48] A. Tarhini, A. Harfouche, and M. De Marco, “Artificial intelligence-based digital transformation for sustainable societies: the prevailing effect of covid-19 crises,” _Pacific Asia Journal of the Association for Information Systems_ , vol. 14, no. 2, p. 1, 2022.
  * [49] A. Ullrich, G. Vladova, F. Eigelshoven, and A. Renz, “Data mining of scientific research on artificial intelligence in teaching and administration in higher education institutions: a bibliometrics analysis and recommendation for future research,” _Discover Artificial Intelligence_ , vol. 2, no. 1, pp. 1–18, 2022.
  * [50] R. BR, G. Yenduri, and S. Vyas, “Ontology alignment evaluation for online assessment of e-learners: a new e-learning management system,” _Kybernetes_ , vol. 51, no. 2, pp. 535–556, 2022.
  * [51] M. Ferenczy, L. Pottas, and M. Soer, “Speech perception in noise in children with learning difficulties: A scoping review,” _International Journal of Pediatric Otorhinolaryngology_ , p. 111101, 2022.
  * [52] A. Movaghar, D. Page, M. Brilliant, and M. Mailick, “Advancing artificial intelligence-assisted pre-screening for fragile x syndrome,” _BMC Medical Informatics and Decision Making_ , vol. 22, no. 1, pp. 1–8, 2022.
  * [53] A. Jothi Prabha and R. Bhargavi, “Prediction of dyslexia from eye movements using machine learning,” _IETE Journal of Research_ , vol. 68, no. 2, pp. 814–823, 2022.
  * [54] N. Ouherrou, O. Elhammoumi, F. Benmarrakchi, and J. El Kafi, “Comparative study on emotions analysis from facial expressions in children with and without learning disabilities in virtual learning environment,” _Education and Information Technologies_ , vol. 24, no. 2, pp. 1777–1792, 2019.
  * [55] P. J. Standen, D. J. Brown, G. M. Kwiatkowska, M. K. Belmonte, M. J. Galvez Trigo, H. Boulton, A. Burton, M. J. Hallewell, N. Shopland, M. A. Blanco Gonzalez _et al._ , “Teachers’ perspectives on the adoption of an adaptive learning system based on multimodal affect recognition for students with learning disabilities and autism,” in _International Conference on Human-Computer Interaction_. Springer, 2021, pp. 436–452.
  * [56] P. S. Mohammed and E. Nell’Watson, “Towards inclusive education in the age of artificial intelligence: Perspectives, challenges, and opportunities,” _Artificial intelligence and inclusive education_ , pp. 17–37, 2019.
  * [57] P. J. Standen, D. J. Brown, M. Taheri, M. J. Galvez Trigo, H. Boulton, A. Burton, M. J. Hallewell, J. G. Lathe, N. Shopland, M. A. Blanco Gonzalez _et al._ , “An evaluation of an adaptive learning system based on multimodal affect recognition for learners with intellectual disabilities,” _British Journal of Educational Technology_ , vol. 51, no. 5, pp. 1748–1765, 2020.
  * [58] T. Morimoto, T. Kobayashi, H. Hirata, K. Otani, M. Sugimoto, M. Tsukamoto, T. Yoshihara, M. Ueno, and M. Mawatari, “Xr (extended reality: virtual reality, augmented reality, mixed reality) technology in spine medicine: status quo and quo vadis,” _Journal of Clinical Medicine_ , vol. 11, no. 2, p. 470, 2022.
  * [59] K. H. McCord, S. K. Ayer, L. A. Perry, K. R. Patil, J. S. London, V. Khoury, and W. Wu, “Student approaches and performance in element sequencing tasks using 2d and augmented reality formats,” _Education Sciences_ , vol. 12, no. 4, p. 247, 2022.
  * [60] O. S. Itani and L. D. Hollebeek, “Light at the end of the tunnel: Visitors’ virtual reality (versus in-person) attraction site tour-related behavioral intentions during and post-covid-19,” _Tourism Management_ , vol. 84, p. 104290, 2021.
  * [61] E. Wagemaker, J. van Hoorn, and A. Bexkens, “Susceptibility to peer influence on prosocial behavior in adolescents with mild intellectual disability or borderline intellectual functioning,” _Research in Developmental Disabilities_ , vol. 120, p. 104143, 2022.
  * [62] L. E. Boyd, S. Gupta, S. B. Vikmani, C. M. Gutierrez, J. Yang, E. Linstead, and G. R. Hayes, “Vrsocial: Toward immersive therapeutic vr systems for children with autism,” in _Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems_ , 2018, pp. 1–12.
  * [63] P. J. Standen and D. J. Brown, “Virtual reality in the rehabilitation of people with intellectual disabilities,” _Cyberpsychology & behavior_, vol. 8, no. 3, pp. 272–282, 2005.
  * [64] A. Drigas, E. Mitsea, and C. Skianis, “Virtual reality and metacognition training techniques for learning disabilities,” _Sustainability_ , vol. 14, no. 16, p. 10170, 2022.
  * [65] S. Parsons and S. Cobb, “State-of-the-art of virtual reality technologies for children on the autism spectrum,” _European Journal of Special Needs Education_ , vol. 26, no. 3, pp. 355–366, 2011.
  * [66] S. Haddick, D. J. Brown, B. Connor, J. Lewis, M. Bates, and S. Schofield, “Metahumans: Using facial action coding in games to develop social and communication skills for people with autism,” in _International Conference on Human-Computer Interaction_. Springer, 2022, pp. 343–355.
  * [67] R. Karabulut, “Exceptional children,” in _Research Anthology on Inclusive Practices for Educators and Administrators in Special Education_. IGI Global, 2022, pp. 271–281.
  * [68] A. Kurniawati, A. Kusumaningsih, and I. Hasan, “Class vr: Learning class environment for special educational needs using virtual reality games,” in _2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)_. IEEE, 2019, pp. 1–5.
  * [69] M.-C. Lai, H.-Y. Lin, and S. H. Ameis, “Towards equitable diagnoses for autism and attention-deficit/hyperactivity disorder across sexes and genders,” _Current Opinion in Psychiatry_ , vol. 35, no. 2, pp. 90–100, 2022.
  * [70] Y. Tan, D. Zhu, H. Gao, T.-W. Lin, H.-K. Wu, S.-C. Yeh, and T.-Y. Hsu, “Virtual classroom: An adhd assessment and diagnosis system based on virtual reality,” in _2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS)_. IEEE, 2019, pp. 203–208.
  * [71] J. F. Herrero and G. Lorenzo, “An immersive virtual reality educational intervention on people with autism spectrum disorders (asd) for the development of communication skills and problem solving,” _Education and Information Technologies_ , vol. 25, no. 3, pp. 1689–1722, 2020.
  * [72] D. Sabie, C. Ekmekcioglu, and S. I. Ahmed, “A decade of international migration research in hci: Overview, challenges, ethics, impact, and future directions,” _ACM Transactions on Computer-Human Interaction (TOCHI)_ , vol. 29, no. 4, pp. 1–35, 2022.
  * [73] F. Moghaddam, A. Vaccari, T. Moniquet, C. Salzmann, Y. Piguet, S. Saab, R. Jaafar, A. A. Suratgar, Q. Shafiee, N. D. Lamso _et al._ , “Massive open online labs (mools): an innovative solution to achieving sdgs in the global south,” in _2019 5th Experiment International Conference (exp. at’19)_. IEEE, 2019, pp. 394–398.
  * [74] M. Jeanneret Medina, C. Baudet, and D. Lalanne, “Web user interface adaptation for low vision people: An exploratory study based on a grounded theory review method,” in _International Conference on Human-Computer Interaction_. Springer, 2022, pp. 3–21.
  * [75] Z. A. Wen, Y. Zhao, E. Silverstein, and S. Azenkot, “An intelligent math e-tutoring system for students with specific learning disabilities,” in _The 23rd International ACM SIGACCESS Conference on Computers and Accessibility_ , 2021, pp. 1–4.
  * [76] F. Putze, S. Putze, M. Sagehorn, C. Micek, and E. T. Solovey, “Understanding hci practices and challenges of experiment reporting with brain signals: Towards reproducibility and reuse,” _ACM Transactions on Computer-Human Interaction (TOCHI)_ , vol. 29, no. 4, pp. 1–43, 2022.
  * [77] A. K. Balan, A. R. Jacintos, and T. Montemayor, “The influence of online learning towards the attention span and motivation of college students,” 2020\.
  * [78] M. Serrhini and A. Dargham, “Toward incorporating bio-signals in online education case of assessing student attention with bci,” in _Europe and MENA cooperation advances in information and communication technologies_. Springer, 2017, pp. 135–146.
  * [79] Y. Choi, J. Kim, C. W. Park, J. Kim, J. H. Yi, and J.-H. Hong, “We play and learn rhythmically: Gesture-based rhythm game for children with intellectual developmental disabilities to learn manual sign,” in _CHI Conference on Human Factors in Computing Systems_ , 2022, pp. 1–13.
  * [80] R. A. Haraty and G. Bitar, “Associating learning technology to sustain the environment through green mobile applications,” _Heliyon_ , vol. 5, no. 1, p. e01141, 2019.
  * [81] A. A. Laghari, K. Wu, R. A. Laghari, M. Ali, and A. A. Khan, “A review and state of art of internet of things (iot),” _Archives of Computational Methods in Engineering_ , pp. 1–19, 2021.
  * [82] J. Wang and Z. Yu, “Smart educational learning strategy with the internet of things in higher education system,” _International Journal on Artificial Intelligence Tools_ , vol. 31, no. 05, p. 2140101, 2022.
  * [83] A. Verdes, C. Navarro, and P. Álvarez-Campos, “Mobile learning applications to improve invertebrate zoology online teaching,” _Invertebrate Biology_ , vol. 140, no. 1, p. e12321, 2021.
  * [84] H. E. Mrabet and A. A. Moussa, “Smart classroom environment via iot in basic and secondary education,” _Transactions on Machine Learning and Artificial Intelligence_ , vol. 5, no. 4, 2017.
  * [85] C. Raman _et al._ , “An iot-based system for supporting children with autism spectrum disorder,” in _2021 Innovations in Power and Advanced Computing Technologies (i-PACT)_. IEEE, 2021, pp. 1–5.
  * [86] A. Lekova, A. Andreeva, T. Tanev, M. Simonska, and S. Kostova, “A system for speech and language therapy with a potential to work in the iot,” in _Proceedings of the 23rd International Conference on Computer Systems and Technologies_ , 2022, pp. 119–124.
  * [87] R. Saade, K. Salhab, and Z. Nakad, “A voice-controlled mobile iot guider system for visually impaired students,” in _2018 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET)_. IEEE, 2018, pp. 1–6.
  * [88] H. Boyes and T. Watson, “Digital twins: An analysis framework and open issues,” _Computers in Industry_ , vol. 143, p. 103763, 2022.
  * [89] S. R. Newrzella, D. W. Franklin, and S. Haider, “Three-dimension digital twin reference architecture model for functionality, dependability, and life cycle development across industries,” _IEEE Access_ , 2022.
  * [90] ——, “Methodology for digital twin use cases: Definition, prioritization, and implementation,” _IEEE Access_ , vol. 10, pp. 75 444–75 457, 2022\.
  * [91] T. Huynh-The, T. R. Gadekallu, W. Wang, G. Yenduri, P. Ranaweera, Q.-V. Pham, D. B. da Costa, and M. Liyanage, “Blockchain for the metaverse: A review,” _Future Generation Computer Systems_ , 2023.
  * [92] C. Peukert, C. Weinhardt, O. Hinz, and W. M. van der Aalst, “Metaverse: How to approach its challenges from a bise perspective,” pp. 1–6, 2022.
  * [93] _“How Seniors Are Creating An Inclusive Metaverse”_ , [Accessed on 22.11.2022]. [Online]. Available: https://www.forbes.com/sites/forbestechcouncil/2022/08/25/how-seniors-are-creating-an-inclusive-metaverse/?sh=36148f1a1931
  * [94] R. Chengoden, N. Victor, T. Huynh-The, G. Yenduri, R. H. Jhaveri, M. Alazab, S. Bhattacharya, P. Hegde, P. K. R. Maddikunta, and T. R. Gadekallu, “Metaverse for healthcare: A survey on potential applications, challenges and future directions,” _arXiv preprint arXiv:2209.04160_ , 2022.
  * [95] D. J. Brown, P. J. Standen, T. Proctor, and D. Sterland, “Advanced design methodologies for the production of virtual learning environments for use by people with learning disabilities,” _Presence: Teleoperators & Virtual Environments_, vol. 10, no. 4, pp. 401–415, 2001.
  * [96] M. C. Harris, D. J. Brown, P. Vyas, and J. Lewis, “A methodology for the co-design of shared vr environments with people with intellectual disabilities: Insights from the preparation phase,” in _International Conference on Human-Computer Interaction_. Springer, 2022, pp. 217–230.
  * [97] R. Chengoden, N. Victor, T. Huynh-The, G. Yenduri, R. H. Jhaveri, M. Alazab, S. Bhattacharya, P. Hegde, P. K. R. Maddikunta, and T. R. Gadekallu, “Metaverse for healthcare: a survey on potential applications, challenges and future directions,” _IEEE Access_ , 2023.
  * [98] T. R. Gadekallu, T. Huynh-The, W. Wang, G. Yenduri, P. Ranaweera, Q.-V. Pham, D. B. da Costa, and M. Liyanage, “Blockchain for the metaverse: A review,” _arXiv preprint arXiv:2203.09738_ , 2022.
  * [99] _“Web-based literacy support, wherever learning happens”_ , [Accessed on 04.08.2022]. [Online]. Available: https://www.kurzweil3000.com/KLogin.php
  * [100] _“OUR GUIDE TO THE WORLD OF ROBOTICS”_ , [Accessed on 04.08.2022]. [Online]. Available: https://robots.ieee.org/robots/qtrobot/
  * [101] _“Math Workshops & Resources by ActiveMath”_, [Accessed on 04.08.2022]. [Online]. Available: https://activemath.com/
  * [102] _“MACHINE LEARNING MAKES THIS HEARING AID SMART”_ , [Accessed on 04.08.2022]. [Online]. Available: https://www.widexpro.com/en/products/evoke/programmable-hearing-aids/
  * [103] _“Immersive Reader ”_ , [Accessed on 04.08.2022]. [Online]. Available: https://docs.microsoft.com/en-us/learn/educator-center/product-guides/immersive-reader/
  * [104] _“Enable Grammarly wherever you type ”_ , [Accessed on 04.08.2022]. [Online]. Available: https://app.grammarly.com/
  * [105] _“INTRODUCING GLASS ENTERPRISE EDITION 2”_ , [Accessed on 04.08.2022]. [Online]. Available: https://www.google.com/glass/start/
  * [106] _“Science and Technology Expeditions”_ , [Accessed on 04.08.2022]. [Online]. Available: https://artsandculture.google.com/project/expeditions-science
  * [107] _“Hold Anything”_ , [Accessed on 04.08.2022]. [Online]. Available: https://mergeedu.com/cube
  * [108] _“Make AR & VR in the classroom”_, [Accessed on 04.08.2022]. [Online]. Available: https://cospaces.io/edu/
  * [109] _“What is Assemblr for Education?”_ , [Accessed on 04.08.2022]. [Online]. Available: https://edu.assemblrworld.com/how-it-works
  * [110] _“Handwriting app for kids”_ , [Accessed on 04.08.2022]. [Online]. Available: https://www.narratorar.com.au/s
  * [111] _“Boost Students’ Engagement and Close Learning Gaps”_ , [Accessed on 04.08.2022]. [Online]. Available: https://augmented-classroom.com/#solution
  * [112] _“Education”_ , [Accessed on 04.08.2022]. [Online]. Available: https://corp.roblox.com/education/
  * [113] _“welcome to minecraft”_ , [Accessed on 04.08.2022]. [Online]. Available: https://www.minecraft.net/
  * [114] _“Vision For every point of view.”_ , [Accessed on 04.08.2022]. [Online]. Available: https://www.apple.com/in/accessibility/vision/
  * [115] _“Voice Dream”_ , [Accessed on 04.08.2022]. [Online]. Available: https://www.voicedream.com/
  * [116] _“TouchCast Studio”_ , [Accessed on 04.08.2022]. [Online]. Available: https://apps.apple.com/us/app/touchcast-studio/id603258418/
  * [117] _“Love Learning”_ , [Accessed on 04.08.2022]. [Online]. Available: https://bookcreator.com/
  * [118] _“IncED”_ , [Accessed on 27.09.2022]. [Online]. Available: https://erasmus-plus.ec.europa.eu/projects/search/details/2021-1-EE01-KA220-YOU-000028805
  * [119] _“Fairness inclusive”_ , [Accessed on 27.09.2022]. [Online]. Available: https://erasmus-plus.ec.europa.eu/projects/search/details/2020-1-DE04-KA105-019388
  * [120] _“InClusion Team”_ , [Accessed on 27.09.2022]. [Online]. Available: https://erasmus-plus.ec.europa.eu/projects/search/details/2020-1-NO01-KA201-076430
  * [121] _“ELLeN Project: English Language Learning and Neurodiversity”_ , [Accessed on 27.09.2022]. [Online]. Available: https://erasmus-plus.ec.europa.eu/projects/search/details/2020-1-DE01-KA203-005696
  * [122] _“Accessible peer interaction with disabled youth”_ , [Accessed on 27.09.2022]. [Online]. Available: http://www.accessinteract.eu/project-overview/
  * [123] R. T. Stramandinoli-Zanicotti, P. F. Corso, and M. E. Kunkel, “Maxillofacial prostheses: Assistive technology in mutilated facial patients,” in _Personalized Orthopedics_. Springer, 2022, pp. 355–371.
  * [124] J. Salminen, L. Nielsen, M. Bahloul, R. G. Jørgensen, J. M. Santos, S.-G. Jung, and B. J. Jansen, “Persona preparedness: a survey instrument for measuring the organizational readiness for deploying personas,” _Information Technology and Management_ , pp. 1–26, 2022.
  * [125] L.-J. Elsaesser, N. Layton, M. Scherer, and S. Bauer, “Standard terminology is critical to advancing rehabilitation and assistive technology: a call to action,” _Disability and Rehabilitation: Assistive Technology_ , pp. 1–3, 2022.
  * [126] P. K. R. Maddikunta, Q.-V. Pham, B. Prabadevi, N. Deepa, K. Dev, T. R. Gadekallu, R. Ruby, and M. Liyanage, “Industry 5.0: A survey on enabling technologies and potential applications,” _Journal of Industrial Information Integration_ , vol. 26, p. 100257, 2022.
  * [127] M. E. Webb, A. Fluck, J. Magenheim, J. Malyn-Smith, J. Waters, M. Deschênes, and J. Zagami, “Machine learning for human learners: opportunities, issues, tensions and threats,” _Educational Technology Research and Development_ , vol. 69, no. 4, pp. 2109–2130, 2021.
  * [128] S. Banabilah, M. Aloqaily, E. Alsayed, N. Malik, and Y. Jararweh, “Federated learning review: Fundamentals, enabling technologies, and future applications,” _Information Processing & Management_, vol. 59, no. 6, p. 103061, 2022.
  * [129] R. Dwivedi, D. Dave, H. Naik, S. Singhal, O. Rana, P. Patel, B. Qian, Z. Wen, T. Shah, G. Morgan _et al._ , “Explainable ai (xai): Core ideas, techniques and solutions,” _ACM Computing Surveys (CSUR)_ , 2022.

|  Gokul Yenduri received his Master’s degree from Vellore Institute of
Technology in the year 2013. Currently, he is a senior research fellow at the
DIVERSASIA project, co-funded by the Erasmus+ programme of the European Union.
His areas of interest are Metaverse, Inclusive Education, Federated Learning,
Blockchain, and Explainable AI. He has worked as an assistant professor in the
past. He attended several national and international conferences, workshops,
and guest lectures and published papers in peer-reviewed international
journals. He is also acting as a reviewer for many prestigious peer-reviewed
international journals.
---|---
|  Dr. Rajesh Kaluri is currently working as an Associate Professor in the
School of Information Technology and Engineering, Vellore Institute of
Technology, Vellore, Tamil Nadu, India. He has more than ten years of teaching
experience. He was a visiting Professor in Guangdong University of Technology,
China in 2015 and 2016. He has completed a Ph.D. in Computer Vision at VIT
University, India. He obtained M. Tech in CSE from ANU, Guntur, India, and B.
Tech in CSE from JNTU, Hyderabad. His current research is in the areas of
Human Computer Interaction, Computer Vision, Big data Analytics, IoT. He is
partnering with the Nottingham Trent University in the United Kingdom on a
funded project worth 80 lakhs which is financed by the Erasmus + Programme of
the European Union. He is associated with several reputed journals as a guest
editor. He has published 40+ research papers in various reputed international
journals.
---|---
|  Dr. Dharmendra Singh Rajput working as an Associate Professor in the
Department of Software and Systems Engineering, SITE, VIT, Vellore since June
2014. He has completed a Ph.D. (2014) from NIT, Bhopal, India. His research
areas are data mining, machine learning, and big data. He has published 35+
reputed Journal Papers, 5 edited books published under reputed publishers, and
17 papers presented in the reputed international conference. He is also a
guest editor of various reputed journals. He has received various awards from
the Indian Government like DST-SERB, CSIR Travel Grant, and MPCST Young
Scientist Fellowship. He is doing the funded project of 80 lakhs which is
received from Erasmus + Programme of the European Union with the partner the
University of Nottingham UK. He has visited various countries UK, France,
Singapore, UAE, China, and Malaysia for academic purposes.
---|---
|  Dr. Kuruva Lakshmanna has received his B-Tech in Computer Science and
Engineering from Sri Venkateswara University College of Engineering
-Tirupathi, India in the year 2006, M-Tech in Computer Science and Engineering
(Information Security) from National Institute of Technology Calicut, Kerala,
India in the Year 2009, and Ph.D from VIT, India in the year of 2017. I am
working as an Associate professor in VIT, India. I was a visiting professor in
Guangdong University of Technology, China in 2018. My research interests are
Machine learning, Data Mining in DNA sequences, IoT, Block chain etc. I have
more than 12 years of experience in teaching. I have published around 25
papers in various reputed interantional journals. I am an editor in Hindawi
journals. He is doing the funded project of 80 lakhs which is received from
Erasmus + Programme of the European Union with the partner the University of
Nottingham UK.
---|---
|  Dr. Thippa Reddy Gadekallu is currently working as an Associate Professor
in the School of Information Technology and Engineering, Vellore Institute of
Technology, Vellore, Tamil Nadu, India. He obtained his Bachelors in Computer
Science and Engineering from Nagarjuna University, India, in the year 2003,
Masters in Computer Science and Engineering from Anna University, Chennai,
Tamil Nadu, India in the year 2011 and his Ph.D. in Vellore Institute of
Technology, Vellore, Tamil Nadu, India in the year 2017. He has more than 14
years of experience in teaching. He has more than 100 international/national
publications in reputed journals and conferences. Currently, his areas of
research include Machine Learning, Internet of Things, Deep Neural Networks,
Blockchain, Computer Vision. He is an editor in several publishers like
Springer, Hindawi, Plosone, Scientific Reports (Nature), Wiley. He also acted
as a guest editor in several reputed publishers like IEEE, Springer, Hindawi,
MDPI. He is recently recognized as one among the top 2% scientists in the
world as per the survey conducted by Elsevier in the year 2021.
---|---
|  Dr. Mufti Mahmud is an Associate Professor of Cognitive Computing at the
Computer Science Department of Nottingham Trent University (NTU), UK. He has
been listed among the Top 2% cited scientists in the world for 2020. He was
also the recipient of the NTU VC outstanding research award 2021 and the
Marie-Curie postdoctoral fellowship. Dr Mahmud is the coordinator of the
Computer Science and Informatics research excellence framework unit of
assessment at NTU and the deputy group leader of the Cognitive Computing &
Brain Informatics and the Interactive Systems research groups. His research
portfolio consists of GBP3.3 million grant capture with expertise that
includes brain informatics, computational intelligence, applied data analysis,
and big data technologies focusing on healthcare applications. He has over 15
years of academic experience and over 200 peer-reviewed publications. Dr
Mahmud is the General Chair of the Brain Informatics conference 2020, 2021,
and 2022; Applied Intelligence and Informatics conference 2021 and 2022; chair
of the IEEE CICARE symposium since 2017 and was the local organising chair of
the IEEE WCCI 2020. He is the Section Editor of the Cognitive Computation, the
Regional Editor (Europe) of the Brain informatics journal and an Associate
Editor of the Frontiers in Neuroscience. During the year 2021-2022, Dr Mahmud
has been serving as the Vice-Chair of the Intelligent System Application and
Brain Informatics Technical Committees of the IEEE Computational Intelligence
Society (CIS), a member of the IEEE CIS Task Force on Intelligence Systems for
Health, an advisor of the IEEE R8 Humanitarian Activities Subcommittee, the
Publications Chair of the IEEE UK and Ireland Industry Applications Chapter,
and the Project Liaison Officer of the IEEE UK and Ireland SIGHT Committee,
the Secretary of the IEEE UK and Ireland CIS Chapter, and the Social Media and
Communication Officer of the British Computer Society’s Nottingham and Derby
Chapter.
---|---
|  Dr. David J Brown is currently a professor of interactive systems for
social inclusion with Nottingham Trent University. He is the principal
investigator for two EU H2020 Grants (MaTHiSiS and No One Left Behind) to
investigate the use of sensor data to understand the emotional state of
learners to provide personalised learning experiences, and how game making can
enhance the engagement of students with learning disabilities and autism. He
is also co-investigator to EPSRC Internet of Soft Things, to investigate the
impact of networked smart textile objects on young people’s wellbeing.
---|---