Intelligent technologies in smart education: a comprehensive review of transformative pillars and their impact on teaching and learning methods

Distribution of studies

Distribution by database

Table 4 shows the distribution of the publication years of the 55 selected articles over the past 10 years. Among the selected publications in this category, Springer Link is the most frequently represented source, accounting for 29.09% (n = 16) of the publications. Thirteen publications (23.64%) are attributed to Google Scholar, followed by 12 publications (21.82%) from Science Direct, and nine publications (16.36%) from Web of Science.

Distribution by publication year

The growing focus on intelligent technology in support of teaching and learning in smart education has been notable. While the overall trend is upward, a slight decrease is observed in 2023. It is noteworthy that in 2020, there was a significant increase in the number of articles, totaling 16 (29.10%), that focus on the technological teaching trends in smart education.

Distribution by country

The number of publications by country is counted as 56, as one study (Kausar et al. 2020) has a first author affiliated with institutions in both China and Pakistan. As shown in Fig. 2, research on teaching in the context of smart education is being conducted globally. China (n = 17) has the highest productivity, accounting for 30.36% of the studies. Following closely is Europe (n = 11), accounting for 19.64%, with Russia (n = 2), Bulgaria (n = 2), and Spain (n = 2) being the main contributors. The United States (n = 5), South Korea (n = 4), and India (n = 4) contribute 8.93%, 7.14%, and 7.14% respectively.

Note: Countries are based on the organization of the first author, because there are two organizations for the first author of one article, so there are 56 in total.

Distribution by smart education systems, platforms, or models

In the current educational landscape, smart education tools and innovative learning management systems have gained significant attention. These tools can be seen as advanced mechanistic learning models that incorporate smart and virtual tools to enhance the learning process. Table 5 presents a comprehensive list of systems, platforms, or models supported by intelligent technologies such as information and communication technology, artificial intelligence, and virtual simulation.

Key elements of smart education (RQ1)

The term “smart education” is interpreted by researchers in various ways, resulting in a lack of consensus. Nonetheless, several aspects are generally agreed upon by researchers, including intelligent technology (n = 12), smart learning environments (n = 9), smart pedagogy (n = 8), smart learning (n = 7), learner (n = 8) and teacher (n = 2). Table 6 presents a summary of these key elements of smart education.

Given the diverse definitions and perspectives on smart education, this article adopts the term “smart education” to encompass the various dimensions and components.

Intelligent technologies for smart education (RQ2)

Figure 3 illustrates that ICT (n = 28) plays a crucial role in smart education, encompassing a significant portion of the technology used in teaching and learning. This highlights the importance of ICT as a key technology in the transition from digital education to smart education. Furthermore, emerging technologies such as artificial intelligence (AI) (n = 16), virtual simulation technology (VST) (n = 9), cloud computing (CC) (n = 4) and the Internet of Things (IoT) (n = 4) have also become indispensable in supporting teaching and learning within the framework of smart education. Less frequently mentioned technologies included big data (BD) (n = 2), wearable technology (WT) (n = 1), and unspecified smart technology (ST) (n = 2). These intelligent technologies offer new opportunities and capabilities for enhancing educational experiences and outcomes.

The number of intelligent technologies used in the review studies.

In the sample articles, intelligent technology is categorized into various areas, but often lacks clear definitions and explanations. Presently, intelligent technology refers to a diverse range of devices, systems and applications that utilize artificial intelligence, internet connectivity and other advanced technologies (Alahi et al. 2023). Educational intelligent technology is defined as the use of tools, such as artificial intelligence, big data, and cloud computing, to advance the development of educational digitalization and establish a novel educational system that supports both intelligent learning and interactive learning (Barakina et al. 2021).

In recent years, the impact of intelligent technology on education, particularly in the context of smart education, has become increasingly significant due to advancements in technology and communication. According to Gros (2016), smart learning relies on two types of technology: intelligent devices and technologies. The education sector and stakeholders, including schools, teachers, students, and parents, have shown great optimism towards integrating technology in teaching and learning. Student acceptance of technology is particularly crucial to ensure its effective contribution to improved learning outcomes (Raes and Depaepe, 2020). Additionally, teachers’ beliefs play a major role in effectively utilizing new technology in teaching and learning (Leem and Sung, 2019).

Intelligent technology plays a fundamental supportive role in teaching and learning. It directly or indirectly assists in teaching and enhances performance. It can also improve student performance by enhancing the smart learning environment, such as integrating technology into pedagogy in smart classrooms (Yang et al. 2018).

It is worth noting that mobile devices, especially smartphones, have been mentioned several times in the article as a carrier of basic mobile technology that has an impact on smart education. Therefore, it would be appropriate to include mobile technology as one of the intelligent technologies in the statistics. The following will elaborate on the application of intelligent technology in teaching and learning.

Artificial intelligence

According to the analysis of the selected sample articles, the use of AI in smart education appears 35 times in the literature. AI, as a branch of computer science, focuses on simulating human thinking processes and intelligent behavior in computers. In the context of smart education, AI finds its main application in intelligent tutor systems (ITS) or adaptive teaching systems (ATS) (Jo et al. 2014; Mousavinasab et al. 2021; Tang et al. 2020). These systems leverage AI algorithms to provide personalized and adaptive learning experiences to students. AI can also be used for data mining and to provide support to learners through predictions related to sustainability or discontinuation (Lee and Lee, 2021; Wang, 2019).

The interaction between physical and virtual learning is enhanced through a variety of AI-powered teaching tools, allowing the next generation of digital citizens to learn more effectively. Additionally, AI is integrated into dynamic adaptive hypermedia systems to enhance learners’ perception through its high adaptability and reliability (El Janati et al. 2018).

Furthermore, the term “artificial neural network” (ANN) appears 90 times in the literature. ANN is a mathematical or computational model that imitates the structure and function of biological neural networks. It has various applications in smart education, such as developing auxiliary learning systems, enriching educational resources, and improving the monitoring of the education and teaching cycle. ANN can also be used in image processing for facial analysis, enhancing teaching methods, strategies, and learning strategies (Dimililer, 2018; Ever and Dimililer, 2018; Kong et al. 2020).

The transformative impact of AI in smart education emerges vividly through empirical insights. Adaptive learning systems, such as Lexue 100, harness smart pedagogy to elevate higher-order thinking skills and mathematics proficiency among junior high school students, as evidenced by significant cognitive advancements (Meng et al. 2020). In parallel, a sentinel-based mechanism outperforms baseline algorithms in refining peer assessment accuracy, thereby enhancing collaborative learning dynamics (Wang et al. 2020). Together, these developments underscore AI’s capacity to integrate precision and adaptability into educational frameworks, fostering sophisticated skill acquisition and interactive engagement, and cementing its pivotal contribution to the evolution of smart education.

Big data

The use of big data in smart education has been mentioned 50 times in the literature. Big data in education (BDE) refers to the behavioral data derived from daily educational activities (Chaurasia et al. 2018). BDE has hierarchical, sequential, and situational features and is used to support data analysis and mining for effective application in educational data mining and performance improvement (Lytras et al. 2018). BDE combines the advantages of AI and IoT to provide a comprehensive service system for smart education.

The application of BDE in smart education includes the construction of teaching quality monitoring platforms and the use of intelligent technologies such as neural networks for data collection and improved data operation efficiency (Fu et al. 2021). Big data, with its volume, velocity, variety, value, and veracity characteristics, combines with education to form BDE, enhancing the quality of teaching and learning (Mayer-Schönberger and Cukier, 2013).

Cloud computing

The use of cloud technology in smart education has been mentioned 333 times in the literature. Cloud computing is one of the representative technologies in cloud technology and a fundamental technology in smart education. Cloud computing enables ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (Jang, 2014; Qasem et al. 2021). It is based on three fundamental models: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS); Mohammed and Zebaree, 2021; Vaquero, 2011). Cloud computing is used to create education and teaching systems and platforms, providing the necessary computing resources for seamless and efficient access to educational content and services. Cloud computing enhances the accessibility and scalability of educational resources, facilitating personalized and adaptive learning experiences for students.

The use of cloud computing in smart education provides powerful data storage, computing infrastructure, and internet services for teachers and students. With cloud computing, business software and user data are stored on remote servers, allowing access through web browsers or lightweight desktop or mobile apps (Al Tayeb et al. 2013). Researchers have applied cloud computing to smart education systems, establishing cloud platforms that are compatible with various file formats. Teachers can create media content on these platforms to support personalized learning for students. The cloud system in the smart education application framework also utilizes cloud computing technology in conjunction with augmented reality scenarios to provide economical, safe, and reliable educational services and multi-modal learning resources, enabling learners to engage in immersive learning.

Overall, the use of big data and cloud technology in smart education contributes to the improvement of teaching quality, personalized learning experiences, and efficient access to educational resources.

Virtual/augmented reality

The use of VR/AR in smart education has been extensively studied, with 175 mentions in the literature. Among these, VR was mentioned 41 times, while AR was mentioned 134 times.

VR is a computer simulation system that creates a virtual world for users to experience. On the one hand, it uses computer-generated environments to immerse users in a simulated educational environment, essentially replacing the real world with a digitally recreated one (Kiryakova et al. 2018). AR, on the other hand, enhances the real-world educational environment by overlaying virtual objects and sensory enhancements in real time. It combines real and virtual objects, provides interactivity in real time, and displays virtual teaching and learning objects in the physical world (Azuma, 1997; Siriwardhana et al. 2021).

Intelligent systems in smart education leverage image recognition analysis and screen splicing playback technology to create virtual reality environments. Additionally, educational game systems using VR/AR have been developed, providing learners with immersive gaming learning experiences and personalized scaffolding. Overall, VR/AR offer learners various forms of enhanced educational content in a three-dimensional (3D) world, providing immersive and interactive learning experiences.

Evidence from the literature substantiates the efficacy of these technologies in metaverse environments. A simulation platform for smart learning is proposed. The proposed 3D M&S education platform can improve creative thinking, application of convergence of knowledge, and various problem-solving skills (Hong and Hwang, 2013). Complementing this, a study employs the evidence-centered design (ECD) framework to evaluate the effectiveness of serious game-based evacuation training in virtual reality settings (Al-Smadi et al. 2018). Findings reveal that such guided, exploratory game-based emergency training scenarios significantly augment learning outcomes, highlighting the potential of immersive technologies to elevate educational impact.

Internet of Things

According to the analysis of the selected sample articles, the use of the Internet of Things (IoT) in smart education has been mentioned 121 times in the literature. The IoT refers to the network of physical objects or “things” embedded with sensors, software, and other technologies that enable them to connect and exchange data with other devices and systems over the Internet (Perwej et al. 2019).

The Internet of Things (IoT) has far-reaching implications for education. Its integration into the educational environment has the potential to significantly enhance the quality of the educational process. Specifically, IoT can improve the reliability, availability, flexibility, energy efficiency, and low latency of educational systems, particularly with the advancements in technologies such as 5 G. These developments underscore the importance of considering IoT’s impact on education in the context of broader technological advancements.

However, academic institutions have yet to fully leverage the benefits of IoT integration in education. Currently, IoT has been applied in smart education systems, such as decision support systems and context-aware ubiquitous learning systems based on IoT (Abdel-Basset et al. 2019). These applications involve the use of IoT to perceive, collect, store, and analyze data, enabling informed decision-making in education (Shapsough and Zualkernan, 2020).

In summary, based on the content analysis of the selected articles, the main purpose of IoT in smart education is to serve as a supporting technology for decision support and intelligent perception systems. IoT, along with other intelligent technologies (e.g., mobile devices and big data), plays a crucial role in shaping and creating a dynamic education system with the concept of intelligent collection and analysis of educational data.

Mobile communication device

The use of mobile technology in smart education has been mentioned 269 times in the literature. Mobile devices, such as smartphones and tablet computers, have become pervasive in children’s school life and are popular tools in primary schools and universities (Ojino and Mich, 2018; Zaranis, 2016; Zaranis et al. 2013). With the current demand for digital literacy, these devices are regularly used for teaching. Mobile technology and devices in school education can yield better results for teaching and learning (Hamhuis et al. 2020). The portability of mobile devices allows them to be integrated into learners’ daily lives, providing personalized learning materials that adapt to their previous learning level and style (Burbaite et al. 2014; Mulatu et al. 2018).

Researchers have conducted studies to demonstrate the impact of mobile technology on teaching. The integration of mobile technology into devices creates smart virtual spaces that encourage online participation and the exchange of ideas. The beliefs and acceptance of smart mobile devices, such as tablets, by teachers can either facilitate or hinder their use in the classroom (Leem and Sung, 2019). Numerous studies have shown that mobile devices play a significant role in promoting teaching, and both teachers and students are willing to integrate technology into subject teaching (Nikolopoulou, 2020).

A study applied smart technologies in the process of English teaching, such as different language learning platforms (Edmodo, TEDed), social networks (VKontakte, Facebook), and a new online language development course. The results indicate that the integration of smart technologies allows learners to boost foreign language skills and encourage motivation (Elsakova et al. 2019).

5 G

The development of 5 G communication technology, the advancement of the fourth industrial revolution, and the emergence of technologies such as cloud computing, big data, and artificial intelligence have propelled smart education forward. 5 G enhances the sense of touch and improves machine-to-machine interaction through its three application modules: enhanced mobile broadband, large-scale machine-type communications, and ultra-reliable and low-latency communications (Dake and Ofosu, 2019). The disruption of the ICT ecosystem brings forth new opportunities for smart education, facilitating changes in learning and teaching models, such as individualized learning, collaborative learning in groups, and group teaching (Demirbilek, 2010; Gloria and Oluwadara, 2016; Yang et al. 2022).

Teaching and learning supported by intelligent technology (RQ3)

Teaching methods supported by intelligent technology

According to the analysis of the sample articles, this study presents a comprehensive overview of smart education (Shown in Fig. 4), focusing on the common teaching methods involved. The findings reveal the frequency and proportion of each teaching method. The results indicate that the most frequently employed methods include differentiated teaching methods (23.63%), flipped classroom pedagogy (18.14%), online teaching (15.19%), personalized and collaborative pedagogy (8.86%), individualization of instruction (8.02%), bring-your-own-device (BYOD) pedagogy (7.59%), human-computer interactive teaching (7.17%), and smart technology integration instruction (6.75%). Additionally, blended instruction shows a relatively lower frequency (4.64%).

Distribution by teaching methods.

Intelligent technology-based instruction is a major theme in smart education, taking various forms. With the assistance of intelligent technology (e.g., human–computer interactive, mobile devices), teaching becomes more convenient and tailored to individual students’ needs and abilities. We elaborated on the most frequent differentiated instruction, flipped classroom pedagogy, and online teaching for smart education.

Differentiated teaching methods

The use of differentiated teaching methods in smart education has been mentioned 56 times in 28 sample articles. Differentiated teaching involves recognizing the varying background knowledge, readiness, language, preferences in learning, and interests of students, and responsively adapting teaching methods to meet their individual needs. It is a process that aims to approach teaching and learning for students of differing abilities in the same class. The focus of differentiated teaching is on individual characteristics, with the intention of maximizing each student’s growth and success by supporting their learning process (Harris et al. 2022).

In a smart learning environment, dynamic grouping and intervention can be carried out to implement differentiated teaching. The rule-based and data-driven nature of differentiated teaching methods, combined with adaptive personal development and the support of big data, has become an important component of smart pedagogies.

Flipped classroom pedagogy

The use of flipped pedagogy in smart education has been mentioned 43 times in 21 sample articles, with flipped learning specifically mentioned twice. Flipped pedagogy in smart education is a dynamic and student-centered approach that enhances the quality of learning within a given class (Iqbal et al. 2020). In a flipped classroom, the dissemination of learning content is moved outside of the classroom, freeing up more time for active learning inside the classroom (Leo and Puzio, 2016). Flipped classroom pedagogy promotes active learning through various activities such as discussions (Critz and Knight, 2013; Talley and Scherer, 2013; Wagner and Urhahne, 2021), individual or small group projects (Al-Zoubi and Suleiman, 2021; Yoon et al. 2021), and investigations (Naibert et al. 2020).

Online teaching

The use of online teaching in smart education is found in 36 instances across 17 sampled articles. Considering the impact of COVID-19, online teaching has become a global emergency measure for schools and educational institutions (Chehri et al. 2021; Omonayajo et al. 2022). It enables remote/online learning and supports the synchronous delivery of learning content for both local and remote students through communication between teachers and students (Shoikova et al. 2017). Research on smart learning takes into account students who face difficulties in offline learning and provides them with the opportunity to review video lectures. For challenging exercises, learners can repeatedly watch explanations, seek support from intelligent tutoring systems, or engage in online learning forums (Zhang and Chang, 2016).

In the context of foreign language learning, online teaching (e.g., MOOC and SPOC) is no longer able to meet the demands of personalized learning. Smart education highlights its advantages by establishing open online learning platforms. These platforms allow teachers and students to collaboratively generate digital learning resources, enabling the development of online language courses designed by teachers from diverse cultural backgrounds. Learners can engage in interactive language learning environments with authentic content from around the world and communicate with native speakers (Elsakova et al. 2019).

Learning methods supported by intelligent technology

In addition to teaching methods, the utilization of intelligent technology has diversified learning approaches, as shown in Fig. 5. Among them, electronic learning (e-learning) stands out as the most prevalent, accounting for 25.51% of the literature. Personalized learning follows closely at 24.37%, indicating its significance in the field. Collaborative learning (19.29%), ubiquitous learning (9.39%), mobile learning (6.73%), game-based learning (4.95%), and exploratory learning (1.65%) are also recognized as important methods of future development in smart education. Considering the similarity between e-learning and online teaching in terms of research, we instead elaborate on the following learning methods, as suggested by Li and Wong (2022), that hold great importance in smart education.

Distribution by learning methods.

Personalized learning

The concept of personalized learning is mentioned a total of 192 times across the 26 sampled articles. Personalized learning, driven by technology, becomes a crucial aspect of smart education development. The learning environment created by smart education offers learners personalized learning services (e.g., Zhang et al. 2015). Personalized learning holds significance in modern education for two reasons. Firstly, it involves accurately and concisely extracting relevant resources for learners, avoiding overwhelming them with excessive information. Secondly, personalized learning provides learners with the opportunity to progress using diverse learning resources that cater to their individual needs.

Mobile technology and AI technology play a crucial role in creating the physical and social environment necessary for personalized learning. With the rapid development of technology and the abundance of information resources, learners can continue their learning outside the traditional classroom setting. Machine learning technology allows for the analysis of learner-generated data, enabling the adaptation of teaching processes to match their individual learning performance. It also facilitates the delivery of different learning content based on specific learning situations (Zhou et al. 2018). Additionally, machine learning technologies, such as deep knowledge tracking, perform pre-learning diagnosis and collect data during the learning process. Based on system feedback, customized feedback can be provided to learners, enhancing their learning experience.

Collaborative learning

The concept of collaborative learning is mentioned a total of 152 times in the 16 sampled articles, emphasizing its significance in leveraging technology to transform learning in smart education. Collaborative learning is a teaching method that involves two or more learners working together to solve problems, complete tasks, or create products. It emphasizes active interaction between students, encouraging them to ask questions, provide detailed explanations, exchange arguments, and generate new ideas and solutions (Asterhan and Schwarz, 2016; Laal and Ghodsi, 2012; van Leeuwen and Janssen, 2019).

For instance, some articles describe how collaborative learning enables intelligent grouping, allowing both teachers and students to engage in collaborative learning and reflective activities, construct knowledge, and foster higher-order thinking skills. Collaborative learning strategies rely on emerging technologies (e.g., big data, AI) to enhance and innovate learning and teaching methods, transforming the traditional classroom into a learner-centered environment.

Ubiquitous learning and mobile learning

The concept of u-learning is mentioned in the literature a total of 74 times in the 35 sampled articles. U-learning refers to anytime and anywhere learning that aims to improve students’ academic performance. It allows students to access learning content from any location and at any time, regardless of whether wireless communications or mobile devices are used (Cárdenas-Robledo and Peña-Ayala, 2018). U-learning leverages ubiquitous technology to create a smart learning environment, breaking the limitations of traditional classrooms by presenting learning content and activities in the real world (Vallejo-Correa et al. 2021).

The pedagogical landscape is shifting from teacher-centered teaching to student-centered teaching, driven by the development of humanism in education. Smart education is student-centered and focused on developing 21st-century skills for learners (Khlaif and Farid, 2018). The integration of mobile learning (m-learning) and ubiquitous learning (u-learning) facilitated by emerging intelligent technologies has given rise to smart learning. This approach aims to equip learners with the skills necessary to adapt to the future of education and the future society.

The concept of m-learning is mentioned in the literature a total of 53 times. M-learning refers to the use of mobile devices as an educational platform and is seen as a solution to educational challenges by utilizing the various resources and tools available. M-learning enables students to collaborate, solve problems, work on projects, share opinions, and access content anywhere and anytime (Akour et al. 2020). Smart education integrates formal and informal learning supported by intelligent technology to create an adaptive smart learning environment where learners can have real-time and seamless experiences in mobile learning and ubiquitous learning (Chen et al. 2016).

Game-based learning

The concept of game-based learning (GBL) is mentioned in the literature a total of 39 times in the 14 sampled articles. GBL is considered a smart pedagogy within the context of SE, as it provides a pleasant learning environment for self-motivated learners and transformed their role from passive recipients to active producers of information and knowledge.

GBL involves the use of game mechanisms, automated recommendations, personalized data analysis and support for educational purposes (All et al. 2016), allowing students to learn and achieve educational goals in a stress-free and engaging manner. Smart education integrates innovative concepts and creative activities into an engaging, immersive, and effective educational gaming environment. Additionally, GBL incorporates interactive exercises and assessments that enable students to collaborate and experience effective learning methods. This aligns with the goals of smart education, as GBL fosters creativity, innovation, and customization in the learning process (Kanimozhi and Jayakumar, 2015).

Exploratory learning

The concept of exploratory learning in smart education is mentioned 13 times. Exploratory learning refers to a learning approach where students select a problem as a starting point, either from the subject field or a real-life situation, and engage in questioning, investigation, analysis, and discussion to solve the problem. It involves inquiry-based learning activities such as expression and communication, through which students acquire knowledge and master methods.

Exploratory learning is often described as learning through the exploration of environments. Smart learning environments can be real, virtual, or a combination of both, and may provide peer or tutorial support (De Freitas, 2006). Learning through exploration provides opportunities for multimodal learning, where learners engage with multiple representations of meaning through different media and multimedia forms. It also facilitates the transfer of learning patterns and behaviors from one context to another. For instance, game- and simulation-based learning operate in this manner, as demonstrated in smart education (Mohammad et al. 2018). Guided exploratory learning using serious games and 3D immersive environments can enhance fire evacuation training in school education.