Inclusive learning using industry 4.0 technologies: addressing student diversity in modern education

Abstract

This study explores the potential benefits of integrating Industry 4.0 technologies into educational settings, focusing on Artificial Intelligence/Machine Learning, Augmented Reality, Big Data, Blockchain, Cloud Computing, Internet of Things, Metaverse, Robotics, and Virtual Reality. The aim is to develop learning environments that are more responsive to the diverse needs of students and better equipped to meet those requirements. In today’s educational system, diversity manifests in various forms, including differences in learning styles, talents, interests, and cultural backgrounds. To effectively accommodate students with diverse profiles, educators must adapt and adjust their learning and teaching approaches accordingly. This study proposes that the incorporation of Industry 4.0 technologies into educational settings may offer adaptable solutions to meet the varying needs of students. It highlights the potential advantages of these technologies in fostering inclusive learning environments for modern education. By utilizing the power of technology, educators can create personalized learning experiences that cater to the unique needs of each student, thereby promoting inclusivity and equity in education. In conclusion, this study underscores the importance of embracing a more inclusive model of education that harnesses the potential of technology to enhance learning outcomes and support the diverse needs of students in today’s educational landscape.

Keywords:

• Industry 4.0
• technology in education
• student diversity
• artificial intelligence
• IoT
• augmented reality

REVIEWING EDITOR:

• Sirui Wang, UCLA: University of California Los Angeles, USA

SUBJECTS:

• Information & Communication Technology (ICT)
• Engineering Education
• Sustainability Education, Training & Leadership

1. Introduction

Since the dawn of time, humans have been motivated by an intrinsic need to understand their existence’s purpose and lead meaningful lives (Ohlsson, 2011). Although the exploratory goals have inevitably changed over time, it always contributed to learning in some way. The objectives that drive education at a given time take on new dimensions with each generation (Bruner, 2009). In ancient civilizations, education was mostly accomplished via apprenticeship, in which people learned skills and information from their elders (Collins & Halverson, 2009). As societies grew and became more complex, schools and universities emerged and became the main source of education. With the development of the printing press, knowledge and information were more readily communicated and propagated, resulting in the construction of libraries and a demand for knowledge (Gaines, 2013). The Industrial Revolution resulted in discoveries and breakthroughs, as well as a stronger focus on science and mathematics in education and the establishment of public education institutions (Bekar & Lipsey, 2004). The twentieth century witnessed a surge in mass education as technology advanced, with radio, television, and film (Kirby, 2016). The learners of the 21st century often referred to as the ‘Internet Generation’, are distinct from those of the past (Oblinger & Oblinger, 2005), and the Internet has given a new means for individuals to access knowledge and learn. The digital revolution has profoundly impacted how people live and work (Ghobakhloo, 2020). In today’s fast-changing world, the technologies of ‘Industry 4.0’ have had an effect and triggered a new wave of change across all sectors (Philbeck & Davis, 2019) which includes improved connection, intelligent data processing/analytics, and computing capacity (Lee et al., 2015). Future forecasts indicate that in coming years employment growth will be driven by technology, hence, it is becoming more necessary to train individuals accordingly (The Future of Jobs Report, 2020).

Along with the evolution of educational methodologies, the acknowledgment of student diversity has emerged as a fundamental component in modern education, recognizing the multifaceted nature of learners’ needs and backgrounds. Addressing this diversity is imperative to ensuring equitable academic outcomes for all learners. However, traditional pedagogical approaches often struggle to accommodate this heterogeneous student body (Padilla-Carmona et al., 2019). Student diversity brings a myriad of benefits to the educational experience. It fosters critical thinking, encourages collaboration, and promotes cross-cultural understanding (Smith & Schonfeld, 2000). Yet, despite the inherent value of student diversity, persistent inequities continue to hinder educational attainment for marginalized groups (Mahmud & Gagnon, 2023). Socioeconomic disparities, racial and ethnic biases, and inadequate support for students with disabilities are just a few of the barriers that impede equitable access to quality education (García-Barrera, 2022).

Building upon the foundational understanding of student diversity and the persistent challenges in traditional pedagogical approaches, Industry 4.0 technologies emerge as a beacon of hope, offering transformative solutions to address the varied needs of learners and foster inclusive educational environments. Industry 4.0 technologies which hold immense potential (Singh et al., 2022) is a collection of technologies viz. Artificial Intelligence/Machine Learning (AI/ML), Augmented Reality (AR), Big Data, Blockchain, Cloud Computing, Internet of Things (IoT), Metaverse, Robots, and Virtual Reality (VR), have the potential to improve the quality of education by providing personalized learning experiences that cater to individual student needs (Ahmad et al., 2022). These technologies can build upon earlier models of education (Soler Costa et al., 2021), and incorporate technological advancements and innovative teaching methods to enhance the learning experience and prepare students for the future workforce (Germain, 2020).

As we explore the potential of Industry 4.0 technologies to promote diversity, equity, and inclusion in education, it becomes evident that these innovative tools not only address the challenges posed by traditional pedagogical approaches but also pave the way for a more inclusive and equitable educational landscape. Technology may be a useful tool for continuing education even in challenging situations, it was seen during the COVID-19 pandemic, that students responded well to technology-assisted learning and were able to adjust to it (Ahmad et al., 2023a). Recent studies suggest that personalized learning is reaching a turning point in its evolution (Walkington & Bernacki, 2020). Technology can accommodate diverse learning styles and abilities, ensuring that all students receive personalized support (Xie et al., 2019). Augmented Reality and Virtual Reality can provide immersive educational experiences that cater to students with special education needs, offering new pathways to learning and engagement (Checa-Domene et al., 2023). Blockchain technology can enhance transparency and fairness in credentialing processes, mitigating biases, and promoting equitable access to opportunities (Rani et al., 2024). Recognizing the relationship between technology and accessibility requirements in education is essential, especially considering the increased emphasis on Diversity, Equity, and Inclusion (DEI) in the post-pandemic age (H. J. Kim et al., 2023). Technology can either narrow or widen the gap between educational opportunities and students, depending on its implementation and utilization. To create inclusive learning environments, it is vital to incorporate a detailed analysis of how technology affects educational results for different student groups.

This research explores the untapped potential of Industry 4.0 technologies in promoting diversity, equity, and inclusion in education. Specifically, it aims to:

• Provides an overview of Industry 4.0 technologies in education.

• Examine the benefits of integrating these technologies to address student diversity.

• Propose practical implications for educators, policymakers, and future research.

By bridging the gap between student diversity and educational practices, this study seeks to contribute to the ongoing discourse on inclusive learning in the digital age.

The following sections comprise the research: Section 2 comprises research methods that examine the bibliometrics of the literary works; Section 3 investigates the key disruptive forces of Industry 4.0 as emerging educational technologies; Section 4 comprises discussions, while Section 5 is recommendations for practice, policy, and future research, and the last section contains the study’s conclusion.

2. Research method

The research method adopted for this article included collecting articles only from Scopus-indexed journals. Scopus databases were chosen because they offer greater worldwide information than other databases. Table 1 lists the parameters for including and excluding literature.

Table 1. Parameters for including and excluding literature.

Parameters	Quest strategy
Electronic database	Scopus database
Search applied on	Article title, Abstract, Keywords
Search criteria	The search condition for the Boolean expression was ‘C1 AND C2’ C1: The word string ‘Education Technology’ C2: The keywords that are connected to Industry 4.0 technologies viz. ‘Internet of Things’ or ‘Cloud Computing’ or ‘Big Data’ or ‘Artificial Intelligence’ or ‘Robotics’ or ‘Blockchain Technology’ or ‘Metaverse’ or ‘Augmented Reality’ or ‘Virtual Reality’
Subject area	Computer Science
Document type	Article
Source type	Journal
Language	English
Period	2018 to 2022

The search was applied to the article title, abstract, and keywords. The two components of the search criteria utilized for this research are as follows:

• C1: The word string ‘Education Technology’.

• C2: The keywords that are connected to Industry 4.0 technologies viz. ‘Internet of Things’ or ‘Cloud Computing’ or ‘Big Data’ or ‘Artificial Intelligence’ or ‘Robotics’ or ‘Blockchain Technology’ or ‘Metaverse’ or ‘Augmented Reality’ or ‘Virtual Reality’.

The search conditions for the Boolean expression were ‘C1 AND C2.’ The articles that were originally obtained from the online Scopus database were examined and filtered for the subject area ‘Computer Science’ since the goal was to identify articles on Industry 4.0. Given that the most recent advancements in the subject have just been made during the past five years, the publication window chosen was from January 2018 to October 2022. Since ‘Educational Technology’ is a well-established research area, only research articles published in Scopus-indexed journals were considered, whereas conference papers, review papers, book chapters, and books were not considered. The articles were limited to those that could be read online in their entirety and were available in English.

According to the literature study, most of the work in education using Industry 4.0 technology was done using AI/ML (31%), which was followed by Gamification/VR/AR/Metaverse (22%), among others. Technology-wise breakdown of the literature cited in the research work is depicted in Figure 1.

Figure 1. Technology-wise distribution of Literature.

The pie-chart of the Technology-wise distribution of Literature included for this study, the plot shows highest percentage of literature is of AI/ML (31%) followed by Gamification/VR/AR/Metaverse (22%), IoT, Big Data and Cloud Computing each have 10% of literature and it is closely followed by Robotics (9%) and Blockchain Technology (8%).

3. Emerging educational technologies

Because of the evolving goals and demands in the education sector (Culp et al., 2005) and Industry 4.0's technological breakthroughs, it is critical to investigate teaching strategies for today’s digital age, as ideas in educational technology discipline have changed and are constantly improving (Januszewski & Molenda, 2013). Based on the literature from Scopus-indexed Journals, this section discusses the technologies that have affected learning in the 21st century during the year 2018-2022 and their possible use in education. Some of the popular technologies are illustrated in Figure 2.

Figure 2. Popular industry 4.0 technologies in education.

Authors imaginative technology icons (small figures/pictogram) of the Popular Industry 4.0 Technologies in Education viz. Blockchain, AI/ML, VR, AR, IoT, Cloud Computing, Big Data, and Metaverse.

3.1. Internet of things

The Internet of Things (IoT) is already being used in many different industries, as billions of gadgets are linked to the Internet. Educational establishments are now trying to integrate IoT into educational activities due to the pervasiveness of IoT devices (Kassab et al., 2020). IoT will improve fundamental teaching goals and their results in a variety of educational scenarios, in addition to helping to create novel user and environment interactions (Dai et al., 2021). In addition to the technology employed, smart learning environments must also consider the interactions they may develop, the improvements they may bring to education, and the profound transformation that results from group learning (Lorenzo et al., 2021). Recent research indicates that the Internet of Things (IoT) benefits learning in a variety of contexts, including interactive language teaching (Pu et al., 2021), secure learning applications for high school classrooms (Bondaryk et al., 2021), the Dynamic Monitoring System of Intelligent Digital Teaching (H. Zhang et al., 2022), etc. Some significant studies on IoT as an Educational Technology are summarized in Table 2.

Table 2. Significant studies on IoT as an educational technology.

Reference	Aim of the Study	Results
(X. Zhang et al., 2021)	To create an intelligent virtual learning environment for mobile based on IoT	The developed virtual classroom platform enables students to use more resources for learning, increases their motivation to study, and promotes a range of learning techniques
(Gao, 2022)	To create an interactive educational paradigm based on the early IoT findings	Correction of the learners’ speech and diction to improve their English. Results suggest that students assimilate knowledge more rapidly with the help of technology
(Carrasco-Navarro et al., 2022)	To integrate IoT in an educational toolkit developed for a Two-Dimensional Cartesian Coordinate System	The developed smart educational equipment may successfully convert a physical lab to a distant online lab
(Al-Masri et al., 2020)	Analytical Investigation of several courses that use or include hardware prototyping platforms	Results show that using hardware prototyping platforms like Arduino or Raspberry Pi as teaching and learning aids is a learning technology trend
(Huertas Celdrán et al., 2020)	Explore Multimodal Learning Analytics (MMLA) to analyze heterogeneous data to improve learning in smart classrooms.	The experiment shows that reasonable computing performance and efficiency may be achieved using standard classroom devices

3.2. Cloud computing

Cloud Computing creates a resource-sharing pond by combining several computer resources. Converting computer resources into public infrastructure improves data consumption and accessibility over the Internet (Hao, 2022). It also provides a new perspective on cost-benefit analysis as by utilizing these elements, the education sector may be freed from the complicated IT infrastructure and related maintenance costs (Nayar & Kumar, 2018). The adoption of cloud computing among students is directly and significantly correlated with perceived utility and convenience of use, which in turn predicts students’ academic success (Raza & Khan, 2022). It is one of the Industry 4.0 technologies that is increasingly being used in all verticals of the education system. Recent studies indicate that integrating cloud computing into interactive music education improves students’ understanding of the instructional material and helps them master the underlying concepts of the knowledge they have learned while also significantly addressing the shortcomings of the conventional music instructional mode (Tian, 2022). Although Cloud technology is trustworthy and secure (Kochyn & Zherelo, 2021) and the adoption of cloud-based services in educational institutions is a development for the better, it also poses risks to everyone in terms of safety, privacy, and secrecy (Amo et al., 2021). Adapt cloud computing’s centralized data center processing to the quickly growing data amount, nevertheless, is becoming increasingly challenging, Edge Computing is a cutting-edge approach for perceptual adaptive data processing that integrates well with the pervasive University Internet of Things data kinds, studies show it has an improvement over conventional cloud computing (Qin, 2022). Some significant studies on Cloud Computing as an Educational Technology are summarized in Table 3.

Table 3. Significant studies on cloud computing as an educational technology.

Reference	Aim of the study	Results
(Morales Chan et al., 2019)	Provide a structural equation modeling approach for analyzing how cloud-based tools (CBTs) are adopted and used in educational contexts	Peer-to-peer interaction and collaboration may serve as a powerful incentive for students to utilize cloud-based tools (CBTs) for learning activities
(Liang et al., 2021)	Implement a cloud computing-based intelligent model for interactive teaching of English and assess the hybrid mode	Most students had a positive learning experience in the cloud computing-based intelligent model employed in an interactive English teaching course.
(Wei & Mo, 2020)	Big data combined with cloud computing is used to create an open interactive teaching algorithm	This research seeks to increase resource sharing, scheduling, and recognition, as well as the quality of education and exchange of knowledge.
(Y. Kim et al., 2018)	Propose the idea of a smart classroom system that can offer real-time recommendations based on affect sensing, emotion identification powered by deep learning, as well as real-time mobile-cloud computing	The article describes the technical and practical elements of a future emotionally aware AI-smart classroom that provides automated real-time feedback to a presenter to enhance the presentation’s effectiveness
(Qasem et al., 2020)	Investigate the reasons behind the continued use of cloud computing in higher education institutions.	An instrument and a conceptual model were developed to measure the ongoing usage of cloud computing in higher education institutions. The findings of the pilot research, which involved surveying stakeholders indicate instrument is trustworthy and valid

3.3. Big data

The immense advantages of the World Wide Web and Big Data have transformed the education system (Liang-Feng & Yuan, 2022) and have led to the production of a significant amount of educational data (Die & Lianhong, 2022). Big Data technologies might help educators track student performance and improve the quality of their lessons. Studies confirm that Big Data helps analyze mouse movements on online learning websites thereby identifying problems in the textual content of study materials (Kirsh, 2022), increasing the range of resource sharing, boosting resource utilization effectiveness, and addressing issues with the unequal distribution of teaching resources (Fu & Mojtahe, 2022), design and building of Interactive Classrooms (Du, 2022; Shen, 2022), etc. Also, educational intelligence is becoming more prevalent in the forthcoming generation of analytical tools for research and education (Khan et al., 2019). Some significant studies on Big Data as an Educational Technology are summarized in Table 4.

Table 4. Significant studies on big data as an educational technology.

Reference	Aim of the study	Results
(Aguilar, 2018)	Analyze applications based on learning analytics that significantly and creatively customize the learning experience.	Personalized instruction is possible through educational technologies that are powered by learning analytics.
(Li & Zheng, 2021)	Develop an interactive classroom system for Tai Chi ideology using big data and graph neural networks.	The created system, which uses Spatio-temporal graph convolution and big data, strikes a better balance between effectiveness and performance.
(R. Zhang et al., 2021)	Analyses the use of the present scientific perspective in education and builds the paradigm for the evaluation of online education	Big data-driven education is anticipated to overtake computer-aided teaching and progressively replace it as the standard.
(Regan & Jesse, 2019)	Examine various ethical issues raised by applications of education technologies, big data, and customized learning, lumping these issues under the umbrella of ‘privacy’ misrepresents the issues and makes it too simple for proponents to ignore or reject them.	Information privacy, autonomy, surveillance, anonymity, ownership of information, and non-discrimination, are six key ethical issues that need to be addressed.
(Hoel & Chen, 2019)	Investigate the idea of data privacy in a cross-cultural context to provide a standard reference point for privacy engineering	The findings suggest that there are issues when adopting privacy notions from North American and European theories to guide privacy engineering for the global market in the age of Big Data.

3.4. Artificial intelligence/machine learning

A growing number of educational environments are using Artificial Intelligence (AI) and Machine Learning (ML) based strategies. (Hopcan et al., 2022). Significant improvements in the educational system have been made possible by AI, and this has caused it to surge swiftly to the top of the list of educational technology fields with the fastest rate of expansion (Dong et al., 2022). AI is used for everything from Smart classrooms (Cho et al., 2020), individualized learning environments (Renz & Vladova, 2021), Intelligent Tutoring Systems (De Benedictis et al., 2021; Haq et al., 2020; MacLellan & Koedinger, 2022; M. Xu et al., 2022), library services (Hussain, 2019), interactive systems for teaching and learning (Liu & Zou, 2022), visualization systems (Hong & Wang, 2022), to robots for educational purposes (Ceha et al., 2022).

A variety of facets of education have been the subject of AI research. From teacher-centric topics viz. intelligent test preparation software (İnce et al., 2020), programming question generation (Chung et al., 2022), automatic proctoring using AI in online exams (Tweissi et al., 2022), to student-centric subjects like AI- AI-enriched digital textbooks (Koć-Januchta et al., 2022), AI-generated courseware (Schroeder et al., 2022), student linguistic issues (Mnasri & Habbash, 2021), to providing language learning systems (P. Chen, 2022; McCarthy et al., 2020; Peng, 2022; Song & Wei, 2022; Żammit, 2022) for students who speak different languages at home.

The use cases for AI are endless - Multimodal learning analytics offers the ability to advance scientific knowledge of how students learn (Oviatt et al., 2018), an algorithm for recommending educational content (Baldominos & Quintana, 2019), algorithmic bias in education (Baker & Hawn, 2022; Gaskins, 2023), a recommendation system for protecting privacy (S. Xu & Yin, 2022), ethical challenges of AI (Kousa & Niemi, 2022). Studies suggest introducing an artificial companion in the form of a chatbot into education (Mageira et al., 2022), to make interactions more resourceful and humanized. Our social connections might also be revolutionized by AI, which could also lead to new approaches to teaching and learning (Kshirsagar et al., 2022). Some significant studies on AI/ML as an Educational Technology are summarized in Table 5.

Table 5. Significant studies on AI/ML as an educational technology.

Reference	Aim of the study	Results
(Renz & Hilbig, 2020)	Explore the existing trends and constraints affecting data-based teaching and learning approaches	AI-based learning systems are driven by the demand for flexibility and individualization, the creation and use of suitable solutions are simultaneously being hampered by the problem of data sovereignty, ambiguity, and the problem of comprehension of data.
(M.-H. Chen et al., 2018)	Employ sentiment analysis for language learning by developing a context-aware emotion synonyms recommendation system.	The findings suggest the participants made a significant improvement in their usage of emotional words. Less skilled participants showed significant gains.
(Yannier et al., 2020)	Experiment with an Intelligent Science Station device that aids students in learning science by using a customized AI computer vision algorithm	The investigation conclusively shows that students who study in a mixed-reality environment using AI adaptive inquiry assistance have a better knowledge of scientific ideas.
(Schiff, 2021)	Examines the state of Artificial Intelligence in education.	AI applications in education promise to invade instructors’ roles and eliminate two of the biggest obstacles preventing distant digital education from attaining its high objectives: social-emotional engagement and differentiation.
(Santos, 2019)	Describes the modeling of human motion obtained with sensors to provide students with individualized help when learning complicated psychomotor skills.	Literature suggests that the motion modeling of complex human activities detected using inertial sensors is still in its early stages.

3.5. Robotics

Educational Robots are now able to interpret information and learn thanks to AI/ML (Ceha et al., 2022) and IoT. The research establishment has begun to focus its work on more specialized applications of computers in education, including robotics or augmented reality (Moreno-Guerrero et al., 2022), although developed educational robots still need a lot more development before they can be as effective as human teaching aids (Chew et al., 2021). Literature suggests robot technology should be employed to provide help in subjects like mathematics over a longer period (Casler-Failing, 2021), humanoid robots being co-teachers were generally well received by the students, however, the teachers expressed some reservations and indicated a wish to improve the efficiency of the robot (Alhashmi et al., 2021). Table 6 summarizes some studies on Robotics as Educational Technology.

Table 6. Significant studies on robotics as an educational technology.

Reference	Aim of the study	Results
(Egido-García et al., 2020)	Examine the necessity for a social robot in a program for children’s education and therapy	Results indicate using robotics for speech therapy sessions could be beneficial in a variety of logopedic aspects and serve as a motivator.
(Orlando et al., 2020)	Support teachers in tracking students’ learning progress in a classroom using robotics-based activities	Instructors provided useful data for a greater understanding of the student’s learning processes without requiring extensive data analysis knowledge.
(Casey et al., 2021)	Assessment of how preservice teachers use floor robots to work through issues related to content learning	The floor robot was considered a helpful tool and rather simple to operate by preservice teachers.
(Angeli, 2022)	Check viability for teaching computational thinking through computer programming with robotics	Results demonstrated statistically significant learning benefits in pre-service instructors’ computational thinking and debugging abilities referring to robotic programming exercises
(Marín-Marín et al., 2020)	Examine whether the MakeyMakey robot affects several psycho-social and pedagogical aspects of physical education research.	The teaching method has the most impact on students’ knowledge, teamwork, instructor rating, and motivation.

3.6. Blockchain technology

Professionals and academics alike are becoming very interested in blockchain technology. Data integrity, decentralization, reliability, and security are some of their unique qualities. Despite this increased enthusiasm, when it comes to using blockchain technology in education, little is understood about the degree of expertise and usage (Alammary et al., 2019). The majority of attention was put on documenting and confirming academic transcripts and diplomas (Ocheja et al., 2022). It establishes an infrastructure for managing credentials and giving learners a permanent record of their accomplishments (Jirgensons & Kapenieks, 2018). Blockchain technology has the potential to fundamentally alter how health professionals are educated in the future, as well as how patients, experts, instructors, and students collaborate to share reliable information (Funk et al., 2018). Some significant studies on Blockchain as an Educational Technology are summarized in Table 7.

Table 7. Significant studies on blockchain technology as an educational technology.

Reference	Aim of the study	Results
(Cheriguene et al., 2022)	Proposes an innovative online teaching and evaluation method for critical situations that use Blockchain.	Preliminary findings from the Coronavirus timeframe indicate a quite high satisfaction rate of more than 90%.
(Xiao et al., 2021)	Advocates blockchain architecture with cross-chain functionality and integration to offer liable data-sharing infrastructure for open learning.	The outcome demonstrates that the blockchain implementation can operate in a real-world setting and outperforms earlier similar attempts.
(Y. Chen, 2022)	Investigate chemistry teaching in the Intelligent classroom using blockchain and artificial intelligence	Learning improved by incorporating blockchain technology and intelligence into education.
((Popa) Chivu et al., 2022)	Investigate teachers’ attitudes regarding using new technologies in the university setting, as well as student’s perspectives on the adoption of blockchain technology	Both university students and academics are interested in using blockchain solutions in the academic process
(Savelyeva & Park, 2022)	Identify a method and approach for employing education blockchain as a potential tool for advancing the UN Sustainable Development Goals (SDGs)	conceptualization of knowledge transformation towards sustainable education via modeling blockchain technology educational commons and educating educators and technology innovation decision-makers.

3.7. Virtual reality/augmented reality/metaverse

Children find the game to be intrinsically satisfying because they evoke positive feelings like excitement, delight, and curiosity, among others, which lift players’ spirits and encourage further participation (López-Faican & Jaen, 2020). As learning that is not enjoyable is ineffective, education should be exciting in its own right (Langer, 1993). If properly structured, interactions may play a significant role in increasing learner engagement (Christopoulos et al., 2018). Over the past few decades, we have witnessed significant growth and increase in the popularity of interactive video games, Virtual Reality (VR), Augmented Reality (AR), metaverses, etc (Petrović & Kovačević, 2022). These technological advancements might be an effective tool for inspiring students’ positive feelings (Fidan & Tuncel, 2019). Literature suggests that children with autism spectrum conditions responded better to collaborative virtual environments (Roper et al., 2019).

VR is a rapidly developing technology with rapidly changing features, uses, and educational benefits (Lege & Bonner, 2020). Studies indicate that student motivation and academic achievement improve when virtual reality is used to teach history (Villena Taranilla et al., 2022), renewable energy (Gonzalez Lopez et al., 2019), healthcare education (Ulrich et al., 2021), a system for learning sign language (Rho et al., 2020), etc. AR technology has recently gained prominence in disciplines like science and mathematics due to its ability to increase student engagement and transport them into a personalized learning environment (Arici et al., 2019). Literature suggests its applicability in developing skills of fashion design (Elfeky & Elbyaly, 2021), support geometry learning (Rossano et al., 2020), structural systems (Hu et al., 2021), library services (Lestariningsih et al., 2021), etc. It can be difficult to adopt AR technology in several areas, such as student acceptability, teacher competence, technological barriers, and institution policy (Abdul Hamid et al., 2022). However, studies demonstrate that applications for augmented reality can be helpful as teaching aids in courses that include reading (Bursali & Yilmaz, 2019), but for that to happen, teachers must receive the technical training necessary to develop and implement augmented reality practices in the classroom (Sáez-López et al., 2020).

Future educational technologies such as the Metaverse are significantly aided by AI (Hwang & Chien, 2022). There are currently relatively few references in the literature describing the usage of the metaverse in education because it is a new developing educational technology trend. However, as it is believed that the metaverse will become more well-known in due course, starting to think about future learning and curriculum designs for the metaverse is crucial (Southgate et al., 2019). Even while most individuals still find the price of the necessary equipment for VR, AR, and Metaverse viz. headmount, etc. to be expensive, various business organizations are working to create affordable options (Hwang & Chien, 2022). Some significant studies on Metaverse/AR/VR as an Educational Technology are summarized in Table 8.

Table 8. Significant studies on metaverse/AR/VR as an educational technology.

Reference	Aim of the study	Results
(Chang & Hwang, 2018)	Assess the efficacy of Augmented Reality (AR)-based educational method in a flipped learning system	The student performed better on projects as well and their learning motivation, critical thinking, and group self-efficacy were enhanced
(Cheng & Tsai, 2019)	Investigate the immersive virtual field excursions in the classroom using head-mounted displays (HMD)-based VR technology.	Task-based virtual field excursions improved the degree of teacher-student interactions. students felt motivated.
(Jiang, 2021)	Create a virtual reality-based interactive educational platform and assess its advantages and disadvantages	Successfully developed a low-cost, basic interactive education environment using entry-level VR glasses, smartphones, and Bluetooth wireless handles
(Lestariningsih et al., 2021)	Mixing real and virtual things to present interesting library information	Use of QR codes for augmented reality in libraries to provide services like book summaries and information about library architecture and shelving
(Suh & Ahn, 2022)	Analyze the metaverse usage by students in elementary school to determine whether it was suitable for learner-centered progressive teaching	Findings show that nearly all respondents (97.9%) had already used the metaverse; consequently, many students may benefit from using the metaverse in education.

4. Discussions

Technology offers a pathway to enhance learning experiences for students with diverse needs, ultimately improving student outcomes and efficiency in education. The advent of Industry 4.0 technologies signals a forthcoming transformation in education, where personalized, engaging, and effective learning experiences are anticipated to become commonplace within a few years.

The key futuristic components of education for a better engaging, personalized, and effective learning experience, as determined by the study are depicted in Figure 3 and are deliberated below.

Figure 3. Key futuristic components of education.

Authors imagination of Key Futuristic Components of Education which consists of six components, Personalized learning (comprising of technologies viz. IoT, Cloud Computing, Big Data, and AI/ML); Secured and Reliable System (comprising of technology viz. Blockchain); Educational Robot (comprising of technologies viz. IoT, and AI/ML); Creative content (comprising of technologies viz. Mataverse, AR, VR, and Gamification); e-Resources (comprising of technologies viz. Big Data and Cloud Computing); and the last component is Feedback System which acts as interface between learner and the different components.

4.1. Personalized learning

Industry 4.0 technologies use computational approaches, algorithms, analytics, and automation, to enable the extraction of valuable insights from the abundance of data available in the digital age. These insights, encapsulated as learning analytics, hold the potential to revolutionize the teaching-learning process. Future students are poised to benefit from personalized learning experiences tailored to their individual needs and preferences.

4.2. Secured and reliable system

The integration of blockchain technology promises unparalleled accuracy and speed in storing student records and authentication processes. While still in its nascent stages within the education sector, blockchain has already demonstrated its potential through identifiable use cases. As one of the emerging areas of future research, blockchain holds promise in ensuring the integrity and security of educational data.

4.3. Educational robot

The future landscape of education envisions students interacting with humanoid robots, serving as wise companions or co-teachers capable of delivering personalized lessons and facilitating diverse learning exercises (Casey et al., 2021; Casler-Failing, 2021). The emergence of a new age of flying robots known as drones and the growth of their use from a niche market to broad use in civilian applications (Floreano & Wood, 2015), might soon result in the creation of an educational drone. There is a good chance that in the future, robots will be able to help students with impairments.

4.4. Creating creative educational content

Incorporating Gamification, Virtual Reality (VR), Augmented Reality (AR), and the Metaverse into educational practices enriches the learning experience by blending virtual and real-world interactions. Dynamic and personalized study materials, along with educational games, accelerate learning and engagement, catering to diverse learning styles and preferences.

4.5. e-Resources

The COVID-19 pandemic has underscored the importance of e-resources in education, with Blended learning (Garrison & Kanuka, 2004), flipped classrooms (Strayer, 2012), and adding MOOCs to higher education (Ahmad et al., 2018) are getting more and more mainstream. Libraries, as pivotal enablers of hybrid online education and research models (Maidment-Otlet, 2021), play an important part in accomplishing sustainable development objectives (Msauki, 2021) and must adapt to disruptive changes while ensuring inclusivity for special needs users. Technical innovations (Salleh et al., 2020) are key to advancing sustainable digital library solutions that accommodate diverse accessibility needs (Iroeze & Owate, 2021).

4.6. Feedback system

Effective feedback mechanisms are integral to evaluating student and educator performance (Y. Kim et al., 2018), guiding the learning process, and facilitating continuous improvement. In addition to determining the acceptability and quality of course contents, feedback may be utilized to develop courses, suggest policies, and assist in decision-making (Ahmad et al., 2023b). Industry 4.0 technologies enhance the efficacy of feedback systems, empowering parents to actively engage in monitoring their child’s educational journey at various levels.

5. Recommendations for practice, policy, and future research

Expanding on the implications for practice, policy, and future research necessitates a comprehensive approach to supporting students as they navigate the integration of Industry 4.0 technologies into education. In addition to ensuring seamless access to technical support for students engaging with advanced technologies, educators and policymakers must prioritize the development of training programs and resources to empower both students and educators in using these tools effectively. Furthermore, ongoing professional development initiatives should be implemented to equip educators with the skills and knowledge needed to facilitate meaningful learning experiences in technology-enhanced environments.

5.1. Technical support

Ensuring seamless access to technical support for students engaging with advanced technologies is paramount in fostering equitable learning opportunities. This includes providing timely assistance for troubleshooting technical issues, accessing digital learning materials, and utilizing educational platforms. Collaborative efforts between educational institutions, technology providers, and support services can enhance the accessibility and effectiveness of technical support mechanisms, ensuring that all students can fully participate in digital learning environments.

5.2. Content provision

Clarifying responsibilities for delivering inclusive educational content through technology platforms is essential for promoting equitable access to quality education. Educators, curriculum developers, and content creators must collaborate to develop diverse and culturally responsive learning materials that cater to the unique needs and backgrounds of all students. Additionally, policies should be established to ensure that educational content aligns with inclusivity and diversity objectives, promoting representation and inclusion across various subject areas and learning contexts.

5.3. Data security

Establishing robust measures to safeguard students’ data in an era reliant on technology-driven education is imperative to maintain trust and integrity within educational systems. Educational institutions and policymakers must prioritize data privacy and security protocols to protect sensitive student information from unauthorized access, misuse, or exploitation. This involves implementing stringent data protection policies, encryption techniques, and access controls to safeguard student data throughout its lifecycle. Moreover, ongoing monitoring and auditing of data practices are necessary to identify and mitigate potential security risks, ensuring compliance with relevant regulations and standards.

6. Conclusion

Industry 4.0 technologies offer great potential for creating more inclusive learning environments that are responsive to the diverse needs of students in contemporary education. This article found numerous examples of how Industry 4.0 technologies have been used in educational settings to address key challenges such as personalized learning, student engagement, and assessment. By utilizing the power of AI/ML, AR, Big Data, Blockchain, Cloud Computing, Gamification, IoT, Metaverse, Robotics, and VR, educators can create personalized and immersive learning experiences that meet the diverse needs of the students.

Incorporating Industry 4.0 technologies into education is a ray of hope for both the learners and the educators. The potential benefits of using these technologies for creating inclusive learning environments in contemporary education are vast and far-reaching. However, it is important to acknowledge a limitation of this review: the scope of this study primarily focused on exploring the potential advantages of Industry 4.0 technologies in education, and as such, did not extensively examine potential challenges or barriers to their implementation. Future research should delve deeper into these areas to provide a more comprehensive understanding of the implications and limitations of integrating Industry 4.0 technologies into educational settings. Nonetheless, it is incumbent upon all stakeholders to engage in ongoing dialogue and collaboration to ensure that these technologies are used in ways that support positive educational outcomes for all students.

Disclosure statement

We have taken the references of other publishers and drafted this article. We have not submitted the manuscript, figures, and tables presented in this article to any other journal for consideration. No conflict of interest.

Additional information

Funding

This research received no external funding. The APC will be funded by Digital Transformation Portfolio, Tshwane University of Technology, Staatsartillerie Rd, Pretoria West, Pretoria 0183, South Africa.

Notes on contributors

Ishteyaaq Ahmad

Ishteyaaq Ahamd is a dedicated researcher with a focus on exploring the intersection of technology and education. With expertise in Industry 4.0 technologies such as Artificial Intelligence, Machine Learning, and Big Data, etc., the author’s research aims to revolutionize learning environments and promote inclusivity in education. This paper contributes to the author’s broader research agenda, which seeks to understand the potential of emerging technologies to address challenges in education and promote equitable access to learning opportunities. By examining the implications of Industry 4.0 technologies on student diversity, equity, and inclusion, the author’s work contributes to ongoing efforts to shape the future of education and ensure that all students have the tools they need to succeed in the digital age.

Sonal Sharma

Sonal Sharma is working as the Dean of the Uttaranchal School of Computing Sciences, Uttaranchal University with a total experience of 23 years. Her research interest is in Data Warehousing, Data Mining, Machine Learning, Big Data Analytics, and Data Sciences. She has presented/published 73 research papers in national and international conferences/seminars/journals.

Rajesh Singh

Rajesh Singh, a Professor at Uttaranchal University, boasts 19+ years in academics, awarded for his M.Tech and B.E. achievements. With 400+ research papers, 500+ patents, and mentoring accolades, he excels in embedded systems, robotics, and IoT. Recognized by prestigious institutions, he's authored 48 books and led Springer's special issue.

Anita Gehlot

Anita Gehlot, an Associate Professor at Uttaranchal University, holds a decade-long academic tenure. With 300 patents and 300 publications, she specialized in embedded systems and IoT. Recognized for her exemplary contributions, she's authored 40 books and edited a special issue for Springer. She organized workshops and lectures for students' enrichment.

Lovi Raj Gupta

Lovi Raj Gupta is the Pro Vice Chancellor, Lovely Professional University. He is a leading light in the field of Technical and Higher education in the country. He holds a Ph.D. in Bioinformatics. He did his M.Tech in Computer-Aided Design & Interactive Graphics from IIT, Kanpur and B. E (Hons) Mechanical Engineering from MITS, Gwalior.

Amit Kumar Thakur

Amit Kumar Thakur is currently a Professor& HOD (Aerospace Engineering) (since July 2019) in the School of Mechanical Engineering (SME) at Lovely Professional University, Phagwara, Punjab, with more than Twenty years of experience in academics and research. His area of research is Renewable Energy Technologies, Biofuels, Thermal Engineering Aerodynamics, Propulsion and IoT (Internet of Things). He has published over Sixty Eight (68) Journal papers all Scopus Indexed (27 in SCI-indexed journals), in the peer-reviewed international journals, conference proceedings, and book chapters. He guided 11 M.Tech Thesis, and is guiding 4 Ph.D Thesis is currently. His h-index of 11, i-10 index of 15, and total citations of 530 strongly endorse his high research productivity. He has published 3 books with Elsevier, Nova Science Publisher, and CRC Press - Taylor & Francis Group and 10 edited books are in progress. He is also acting as an Editorial board member and reviewer of 5 international peer-reviewed Journals. He has Over 190+ Patents Published with 22 Innovative Australian Patent, 7 Indian patent, 2 South African Granted. He completed a three-week summer school program on Environment at Tsinghua University, Beijing, China. Topper of NPTEL online course. He was the University topper in M.Tech, RGPV, Bhopal. He was the Faculty Advisor of the SAE collegiate club, organizing head of IC engine and robotics workshop. He has organized and conducted many workshops and national-level working model competitions. He is a Member of the Society of Automobile Engineers (SAE India), a life member of the Indian Society for Technical Education (ISTE), and a life member of The Aeronautical Society of India (AMeASI). He was the Research sub-coordinator for ADRDE, DRDO in the project of design and development of the deceleration system for the Space capsule recovery experiment (SRE).

Neeraj Priyadarshi

Neeraj Priyadarshi, a Senior IEEE Member, holds an M.Tech from VIT and a Ph.D. from Udaipur's Government College of Technology and Engineering. Currently at Aarhus University's CTiF Global Capsule, his expertise spans power electronics, control systems, and solar power. With 60+ publications and 10 patents, he's a distinguished researcher and organizer of international workshops.

Bhekisipho Twala

Bhekisipho Twala is the Deputy Vice-Chancellor for Digital Transformation and Professor in Artificial Intelligence and Data Science at the Tshwane University of Technology in South Africa. His research includes promoting Artificial Intelligence within the Big Data Analytics field and developing novel solutions to crucial research problems in this area.