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To understand the development of students’ higher-order thinking skills (HOTS) in the smart classroom environment, a structural equation modeling analysis was used to examine the relationships between key factors that influence students’ learning and their HOTS within a smart classroom environment. A sample of 217 first-year Chinese college students, who studied in a smart classroom environment for one semester, completed a survey that measures their smart classroom preferences, learning motivation, learning strategy, peer interaction, and HOTS. The results indicated that peer interaction and learning motivation had a direct impact on students’ HOTS. Furthermore, indirect effects were found between students’ learning strategy and HOTS through the mediator peer interaction, and between smart classroom preferences and HOTS through the following: learning motivation, the combination of learning strategy and peer interaction, and the combination of learning motivation, learning strategy and peer interaction. Based on these findings, this study recommends that instructors teaching in a smart learning environment should focus on improving peer interaction and learning motivation, as well as smart classroom preferences and learning strategy, to hone students’ HOTS.

Ambient conditions influence human health, emotions, and mental power. Therefore, numerous studies have been conducted in different disciplines on the measurement and control of ambient conditions in classrooms. Moreover, a number of studies identify some physical and mental performances of students simultaneously or for informative purposes by utilizing various hardware and software. However, these studies did not consider automatic control processes and individuality in fulfilling classroom ambient conditions, which influence students’ behavior. This study aims to reduce the adverse impacts of environmental factors on learning and control more necessary physical parameters with higher accuracy using the latest technology and methods. Thus, a new smart classroom incubator (SCI) algorithm, including hardware, software, and experimental studies, in which individual differences could be considered even in the same classroom environment, and its implementation were presented. The system enables access and monitoring of data wherever there is Internet connection. Moreover, it was designed based on IoT because it allows for data transfer over web services or data-dependent operations. All the necessary equipment was placed in the classroom without affecting the learning environment and distracting the class. Cronbach’s α coefficient, which indicates the reliability of the implemented model, was 0.891.

In view of the main challenges faced by the realization of smart classroom environment in classroom teaching, such as inconvenient use of equipment, inability to comprehensively obtain multi-source heterogeneous data in real time, and lack of effective support for smart teaching, an IoT system archi-tecture based on cloud-edge computing technology is proposed. The system architecture consists of four parts: sensor layer, edge computing layer, core network layer and platform application layer, which are respectively used to obtain multi-source heterogeneous data, configure edge nodes on demand, transfer information and provide various educational services. A question-naire survey was adopted to investigate the teachers and students in the smart classrooms of Central China Normal University where the proposed system was implemented. The results showed that the overall satisfaction of students and teachers is high. Among them, 100.00% of teachers and 97.14% of students agreed that the system can effectively collect educational data and give students feed-back; 96.00% of teachers and 91.43% of students believed that it can pro-mote classroom teaching and bring pleasant experience to teachers and stu-dents. In brief, the proposed solution is completely practicable and can pro-vide a reference for the research, construction and application of smart classrooms.

Purpose The outbreak of COVID-19 has projected prominent threats to the learning and teaching environment. The context of pandemic has delivered numerous advices, which are relevant in dealing with the pandemic situation, to the educational institution administrators, educators and other officials. Design/methodology/approach The response of an educational body addresses the needs as well as the concerns of learners and their parents. Educational body incorporates asynchronous learning methodologies that work pre-eminent in digital media, to enhance their ability to teach distantly. To make remote teaching and learning efficient, artificial intelligence (AI) approaches are incorporated into the traditional system of education. Findings Educational body have to encompass a diversified tools and system that places COVID-19 in a worldwide, in addition to the general disciplines of classroom. AI and other technological advancement has introduced numerous tools and applications for handling the pandemic situation. Originality/value This research discussed the impact of COVID and influence of AI on education and also the significance and applications of AI in education system in Saudi Arabia. In addition, this study examined the experience of Saudi’s students Universities with AI applications, (316) form the sample of this study to response it’s the Likert scale tool. The results of the study indicated that in the midst of the COVID-19 outbreak, the Government switched to online education, and positive responses were found from learners with taking all benefits of AI application. However, the lack of experience played a critical role in preventing full utilization of AI applications, which will motivate the decision maker to train the learner and the teacher to take advantage of these applications to face any pandemic in future.

The term \"Smart Classroom\" has evolved over time and nowadays reflects the technological advancements incorporated in educational spaces. The rapid advances in technology, and the need to create more efficient and creative classes that support both in-class and remote activities, have led to the integration of Artificial Intelligence and smart technologies in smart classes. In this paper we discuss the concept of Artificial Intelligence in Education and present a literature review related to smart classroom technology, with an emphasis on emerging technologies such as AI-related technologies. As part of this survey key technologies related to smart classes used for effective class management that enhance the convenience of classroom environments, the use of different types of smart teaching aids during the educational process and the use of automated performance assessment technologies are presented. Apart from discussing a variety of technological accomplishments in each of the aforementioned areas, the role of AI is discussed, allowing the readers to comprehend the importance of AI in key technologies related to smart classes. Furthermore, through a SWOT analysis, the Strengths, Weaknesses, Opportunities, and Threats of adopting AI in smart classes are presented, while the future perspectives and challenges in utilizing AI-based techniques in smart classes are discussed. This survey targets educators and AI professionals so that the former get informed about the potential, and limitations of AI in education, while the latter can get inspiration from the challenges and peculiarities of educational AI-based systems.

The rapid development of educational informatisation has made smart classrooms a key platform for advancing innovative teaching methods. However, traditional evaluation systems face challenges related to data security, fairness, and result traceability. This paper introduces a blockchain‐based teaching evaluation system for smart classrooms to address these issues and improve teaching quality and management efficiency. The system employs a cloud‐network‐edge‐device architecture, integrating cloud computing, network communication, and edge devices for real‐time data collection, secure transmission, and intuitive visualisation. Blockchain technology ensures data integrity and transparency, while the practical Byzantine fault tolerance consensus algorithm maintains system reliability and prevents data manipulation. Experiments conducted at Dalian Jiaotong University demonstrate that the smart classroom improves teaching quality by 20% compared to traditional classrooms. The system is particularly effective in enhancing teaching resources and real‐time communication, though improvements in student engagement are still needed. System performance tests indicate that the platform maintains low response times and stability under varying levels of concurrent requests, demonstrating its capability to support high‐demand teaching scenarios and ensure data consistency and transparency. Smart classrooms, based on a blockchain framework, enhance teaching quality and management efficiency. Experiments at Dalian Jiaotong University show advantages in teaching resources and communication but need improvement in student engagement. The system offers a reliable evaluation method.

Educational institutions are envisioned as principal agents for addressing the current sustainability challenge that society is facing. Education for Sustainable Development (ESD) is transformational and concerns learning content and outcomes, pedagogy and the learning environment in itself. ESD entails rethinking the learning environment (physical and virtual) in line with sustainable development, which implies classrooms’ transformation towards learner engagement, formative assessments and active methodologies. This paper responds to this need through exploring the relationship between Smart Classrooms and four widely used ESD methodologies (project or problem-based learning, case study, simulation and cooperative inquiry), identifying how the dimensions and categories of the characteristics of Smart Classrooms can contribute and lead to the implementation of ESD methodologies in real teaching practice in an effective way. The method used in this study consisted of a literature review of both theoretical frameworks separately, ESD and Smart Classrooms, and a subsequent expert analysis to identify the interrelation between both. The Smart Classroom shows a high level of adequacy for using problem and project-based learning, case study and cooperative inquiry methods because of its characteristics in terms of technology developments, environmental conditions and processes. Simulation is the ESD methodology with the lowest level of adequacy in a Smart Classroom, because it is primarily held online rather than through face-to-face teaching. Smart Education facilitates the putting in practice of ESD processes as it enables the creation of intelligent, sustainable, resource-efficient, personalised and adaptive learning environments. Further empirical research is needed to explore the influence that the Smart Classroom has in enabling ESD processes and practices, and to identify students’ and teachers’ needs at different education levels. Additionally, teacher training programmes focused on the correct use of Smart Classrooms and on the digital competence of teachers are critical to its successful implementation.

Electricity-saving can be achieved through the efficient use of energy such as turning off lights and electrical appliances when not in use. Therefore this work proposed the smart classroom for electricity-saving with an integrated IoT System to prevent wasting electricity in the classroom. Smart Classroom means that it will detect and count the number of students entering and exiting the classroom by using a sensor system automatically. The main objective of this work is to control the lighting systems and fans by using the IoT application and sensor system. This means that when the sensor is triggered the sensor will send data to the Blynk application software using IoT to display the status of the classroom. This proposed work is also able to detect whether a classroom is available to use or not based on the presence of people. If the classroom is being used the Blynk application software will show the lamp and fan are ON. Otherwise the lamps and fans are OFF if there are no people in the classroom. The result successfully shows that if the first student entering the classroom all the lamps and fans are ON. While if the last student exiting the classroom all the lamps and fans are OFF. This result also indicates that electricity can be saved if all appliances in the classroom are switch OFF at the right time.

This study investigated the effect of classroom settings on teacher-student interaction in higher education by comparing the behavioural sequences in smart classrooms (SCs) and traditional multimedia classrooms (TMCs). Twenty in-classroom teaching sessions were randomly selected from six universities in South China, involving 1,043 students and 23 teachers. Half of the sessions were taken in SCs as the experimental group, and half were in TMCs as the control group. A teacher-student interaction behaviour coding schema was developed, and a total of 17,805 observable behaviours were collected and coded sequentially via a review of classroom videos. Then, the behavior pattern diagram was set up to visualise a lag sequential analysis results by four themes, namely teacher-talk, teacher-action, student-talk and student-action. Results show that compared to TMCs, the SCs triggered significantly more self-initiated student actions and student-driven teacher talk, while teacher-initiated talk decreased significantly, indicating that students’ autonomy was strengthened in the SC. Furthermore, teachers’ workload was somewhat reduced, and they obtained more support with trying new pedagogies with mobile terminals in the data-rich environment. These findings provide evidence to validate the effect of SCs on increasing teacher-student interaction and strengthening the students’ dominant position.

Interaction in the classroom plays the key role for cultivating students’ 21st century skills. Insufficient breadth of interaction, uneven interaction opportunities, and chaotic interaction existed in many classrooms. With the integration of technology into education, many smart classrooms were built, with one of the aims being to promote interaction. However, the differences of interaction behaviors and engagement in a smart class versus a traditional class could rarely be found in literature, especially with the same teacher lecturing in both classes. In this study, a quasi-experiment was conducted by one experienced English teacher lecturing in a smart classroom with students and a traditional classroom with students for one semester. Research data were obtained by coding the 8 class videos with the proposed “Classroom Interaction Analysis Framework” and the adapted engagement questionnaire, and the data were analyzed using SPSS 24. Results showed that there were no significant differences in either interpersonal interaction or human–technology interaction; however students experienced significantly more engagement in the smart classroom. The reasons were analyzed and interaction patterns in smart classroom were discussed. Finally, a smart classroom interaction model was proposed to promote classroom interaction by considering the interplay of pedagogy, space, and technology.