Smart Learning and Smart Schools

In the history of supporting learning with computers (“e-Learning”) curricula individualisation may be done according to several paradigms: Computer-Assisted Instruction (CAI), Intelligent Tutoring Systems (ITSs), and Computer-Supported Collaborative Learning (CSCL). In CAI learners get electronic versions of the course pages and they can read them sequentially. For passing from a part of the course to the following one they usually should complete some tests. Curriculum, in this case may be individualised, but only before learning starts. It cannot be easily adapted dynamically, depending, for example, on learner’s learning pace, or errors detected during verification tests. The elimination of this limitation was one of the main goals of ITSs, which build and maintain a model of learner’s knowledge and even of his cognitive, conative, and emotional features. ITSs are using symbolic, knowledge-based artificial intelligence techniques that allow reasoning and dinamically individualised curriculum generation and update. The third paradigm, CSCL is taking advantage of the facilities of WWW 2.0 (the so-called Social Web), such as instant messengers (chat), forums of discussions, wikis, blogs, and social networks. CSCL sees learning as social knowledge construction, in contrast to the individualised perspective of ITSs. From the above discussion, it seems that ITSs are the best solution for the individualization of curricula. However, ITSs faces some some extremely difficult problems: they need comprehensive formal knowledge bases (e.g. ontologies), small learning objects associated with their concepts, and student models. Building and updating all these modules are extremely difficult. A solution is to use the so-called recommender systems, which can provide relevant documents for an individual learner, starting from his/her particularities and taking into account the activity of learners with similar interests, therefore combining ITSs with CSCL. Medicine education and, in particular, paediatric education, has some obvious particularities, because practice is a very important part of curricula. Due to the globalisation, to the rapid spread of information appearance, and, meanwhile, geographic, cultural and other context features, generation of individualised curricula, tailored to particular features is expected to have a future, by the integration of features from ITSs and CSCL, as above mentioned. The presentation will consider as exemplification the particular case of education for early nutrition.

Online learning with Intelligent Tutoring System (ITS) is becoming very popular where the system models the student's learning behavior and presents to the student the learning material (content, questions‐answers, assignments) accordingly. In today's distributed computing environment, the tutoring system can take advantage of networking to utilize the model for a student for students from other similar groups. In the present paper we present a methodology where using Case Based Reasoning (CBR), ITS provides student modeling for online learning in a distributed environment with the help of agents.Student modeling is a key component in any Intelligent Tutoring System (ITS). In today's distributed computing environment, the tutoring system can take advantage of networking to utilize the model for a student for students from other similar group. In the present paper we present a methodology where using Case Based Reasoning (CBR), the ITS provides student modeling for online learning in a distributed environment with the help of agents.The Chapter describes the approach, the architecture, and the agent characteristics for the student modeling in the ITS. This concept can be deployed to develop ITS where the tutor can author and the students can learn locally whereas the ITS can model the students' learning globally in a distributed environment. The advantage of such an approach is that both the learning material (domain knowledge) and students' model can be globally distributed thus enhancing the efficiency of ITS with reducing the bandwidth requirement and complexity of the system.

Self-regulated learning (SRL) can predict academic performance. Yet, it is difficult for learners. The ability to self-regulate learning becomes even more important in emerging online learning settings. To support learners in developing their SRL, learning analytics (LA), which can improve learning practice by transforming the ways we support learning, is critical. This scoping review is based on the analysis of 54 papers on LA empirical research for SRL in online learning contexts published between 2011 and 2019. The research question is: What is the current state of the applications of learning analytics to measure and support students' SRL in online learning environments? The focus is on SRL phases, methods, forms of SRL support, evidence for LA and types of online learning settings. Zimmerman's model (2002) was used to examine SRL phases. The evidence about LA was examined in relation to four propositions: whether LA i) improve learning outcomes, ii) improve learning support and teaching, iii) are deployed widely, and iv) used ethically. Results showed most studies focused on SRL parts from the forethought and performance phase but much less focus on reflection. We found little evidence for LA that showed i) improvements in learning outcomes (20%), ii) improvements in learning support and teaching (22%). LA was also found iii) not used widely and iv) few studies (15%) approached research ethically. Overall, the findings show LA research was conducted mainly to measure rather than to support SRL. Thus, there is a critical need to exploit the LA support mechanisms further in order to ultimately use them to foster student SRL in online learning environments.

The construction of a rural teaching workforce is a key factor in promoting educational equity and the development of high-quality education. We employ the Leslie model and the age-specific grade progression rate method to estimate a population forecasting model. Using data from the seventh national census and regional statistical yearbooks and accounting for educational centralization and population trends, we forecast rural preschool, primary, and junior high teacher demand in 12 western Chinese provinces (2024–2040) on the basis of school-age population changes. The results show that (1) population changes have led to a decline in the allocation of rural education resources in western China; (2) the decrease in the school-age population has led to a continuous contraction of the teacher team structure, with a more pronounced decline in the preschool education stage; for example, rural student numbers in Gansu are projected to decline by 35% between 2024 and 2040, contributing to a 28% drop in primary teacher demand under constant policy conditions; and (3) the decline in teacher demand at all educational stages has exacerbated the imbalance in the allocation of educational resources. To address shrinking school-age populations and rural teacher imbalances, we recommend adjusting staffing standards on the basis of demographic forecasts, strengthening cross-stage professional development with clear pathways and incentives, and modernizing training through blended mentoring and data-driven coaching to ensure sustainable education quality.

In this study, we review 1830 research articles on artificial intelligence in education (AIED), with the aim of providing a holistic picture of the knowledge evolution in this interdisciplinary research field from 2010 to 2019. A novel three-step approach in the analysis of the keyword co-occurrence networks (KCN) is proposed to identify the knowledge structure, knowledge clusters and trending keywords within AIED over time. The results reveal considerable research diversity in the AIED field, centering around two sustained themes: intelligent tutoring systems (2010-19) and massive open online courses (since 2014). The focal educational concerns reflected in AIED research are: (1) online learning; (2) game-based learning; (3) collaborative learning; (4) assessment; (5) affect; (6) engagement; and (7) learning design. The highly connected keywords relevant to analytic techniques within this field include natural language processing, educational data mining, learning analytics and machine learning. Neural network, deep learning, eye tracking, and personalized learning are trending keywords in this field as they have emerged with key structural roles in the latest two-year period analyzed. This is the first article providing a systematic review of a large body of literature on artificial intelligence in education, and in it we uncover the underlying patterns of knowledge connectivity within the field, as well as provide insight into its future development. The three-step multi-scale (macro, meso, micro) framework proposed in this study can also be applied to map the knowledge development in other scientific research areas.

Application and theory gaps during the rise of Artificial Intelligence in Education

The rapid advancement of artificial intelligence (AI) has profoundly reshaped global educational paradigms. By leveraging its well-established education system, Finland has actively integrated AI into physical education (PE), driving significant advancements in school-based PE. Meanwhile, China’s PE reform is at a critical juncture, facing challenges in resource allocation, pedagogical innovation, and technological integration. This study systematically examines AI applications in Finnish PE, evaluates their pedagogical impact, and explores their applicability in the Chinese context to inform AI-driven innovations in China’s PE reform. A systematic literature review focused on AI applications in PE within Finland and China. Data were retrieved from Web of Science, Scopus, Google Scholar, and CNKI, covering English and Chinese publications from 2014 to 2024. The study adhered to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, with thematic content analysis identifying key areas of AI implementation. The findings reveal that Finland has successfully integrated AI into PE, primarily in the following areas: (1) Personalized and Adaptive Learning – AI platforms dynamically adjust training regimens based on students’ physical conditions and learning progress. AI-powered interactive games further enhance student engagement. (2) Performance Analytics and Feedback – The Finnish National Agency for Education collaborates with AI enterprises, utilizing AI-driven data analytics to optimize sports performance and injury prevention. (3) Teacher Training in AI Pedagogy – Finnish educators undergo systematic training in AI-assisted PE instruction, with institutions such as the University of Jyväskylä prioritizing AI competency in teacher preparation. China has initiated pilot projects in AI-powered sports analytics and digital fitness education, reflecting a growing emphasis on AI-driven PE. However, challenges persist, including disparities in technological resources, insufficient AI literacy among educators, and outdated assessment frameworks. AI holds transformative potential for PE, and Finland’s experience offers valuable insights for China’s PE reform. Key recommendations include: (1) Enhancing AI Integration in Teacher Training – Strengthening AI-focused professional development to equip educators with necessary competencies. (2) Advancing AI-Driven Performance Assessment – Utilizing AI technologies to enable objective, data-informed evaluations. (3) Fostering Policy Support for AI Adoption – Promoting AI-integrated pedagogical models to drive innovation in PE instruction. (4) Bridging the Digital Divide – Addressing infrastructural disparities to ensure equitable access to AI-enhanced PE education. Future research should empirically assess the efficacy of AI applications in Chinese PE and their impact on student learning outcomes.

In order to narrow the gap in compulsory education resource distribution among districts and counties, and promote equity in education among regions. This article analyzes the current situation and causes of the unbalanced allocation of compulsory education resources in Chongqing and proposes the existing problems. Through the use of statistical description and spatial autocorrelation methods to analyze the internal relations of related factors and variables. And on the basis of proving the existence of proximity, it analyzes the type and influence mechanism of regional aggregation in the process of unbalanced allocation of compulsory education resources. This article focuses on constructing the theory of non-equilibrium allocation of compulsory education resources in Chongqing, proposing the basic assumptions of compulsory education resource allocation research, analyzing the problems of spillover effect, convergence effect, demonstration effect, etc. The theoretical hypothesis of the unbalanced allocation of educational resources is empirically tested.

Reasonable allocation of art teaching resources can improve the management efficiency of art teaching resources. There is a large delay in the allocation of art teaching resources, which leads to the long occupation time of network resource allocation channel. The traditional method of network experiment resource allocation is to assign resource tasks for different channels to complete the resource allocation. When the network resource allocation channel occupies a long time, the allocation efficiency is reduced. This paper proposes an optimal allocation method of art teaching resources based on multi rate cognition. From the point of view that there are a pair of primary users and a pair of secondary users in the network, this method constructs a resource allocation delay model, obtains the resource allocation delay under different modes, and dynamically adjusts the transmission rate on the allocation resource block. The art teaching resource allocation scheduling problem is modeled as a nonlinear optimization problem, and the constraints of the optimization problem are given, which are integrated into greedy computing. The global optimal solution of the problem is carried out by using the method, and the allocation of art teaching resources is completed. Simulation results show that the proposed algorithm greatly improves the efficiency and effect of teaching network resource allocation.

Teaching resources in higher education are an important part of the talent cultivation system of higher education institutions. Dynamic management and optimal allocation of teaching resources are the focus of the construction of each school, which reflects the teaching, research, and management level of the school to a certain extent. In this paper, after combining the current situation and management problems of teaching resources in six schools in City A and putting forward constructive opinions, we further use data envelopment analysis to study the efficiency of teaching resource allocation in School H in City A and put forward the method of resource optimization.The input redundancy rate of School X1 in School H during the three years of 2021-2023 is 30.25%, 44.15%, and 55.26%, respectively. The data shows that the school has an excess of teacher inputs and there is a shortage of resources for teachers. For the current dynamic management and resource allocation problems of teaching resources in A city school, this paper argues that the management of school teaching resources is a long-term and complex organic whole, and needs to be reformed in terms of structure, resource integration and management, and to construct a high-quality and efficient management model. In terms of resource optimization and allocation, schools can optimize the talent resource allocation mechanism and other aspects to promote the full allocation of teaching resources in higher education.

This study aims to develop and implement a tool called intelligent tutoring system in an online course to help a formative evaluation in order to improve student learning. According to Bloom et al. (1971,117) formative evaluation is a systematic evaluation to improve the process of teaching and learning. The intelligent tutoring system may provides a timely and high quality feedback that not only inform the correctness of the solution to the problem, but also informs the students about the accuracy of the response relative to their current knowledge about the solution. Constructive and supportive feedback should be given to the students to reveal the right and wrong answers immediately after taking the test. A feedback about the right answers is a form to reinforce positive behaviors. Identifying possible errors and relating them to the instruction material may help student to strengthen the content under consideration. The remedial suggestion should be given for each answer with detailed prescription with regards the materials and instructional procedures before taking next step. The main idea is to inform the students what he has learned and what he still has to learn. The open-source LMS called MoodleTM was extended to accomplish the formative evaluation, high-quality feedback, and communal knowledge presented here with a short online financial math course that is being offered at a large University in Brazil. The preliminary results shows that the intelligent tutoring system using high quality feedback helped the students to improve their knowledge about the solution to the problems based on the errors of their past cohorts. The results and suggestion for future work are presented and discussed.

The high-quality and balanced development of compulsory education is affected by the differences in the allocation of educational resources. This paper takes primary education in Pudong New District as the research object, and uses TOPSIS Model of entropy weight method to evaluate and analyze the allocation of educational resources in each school district. The research shows that the level of teaching conditions in each school district of Pudong is equal, but there are obvious differences in the level of teachers and balanced development. The overall difference in the allocation of high-quality resources is large. Relatively high-quality school districts are mainly concentrated in the middle and West, while the north, East and South are relatively weak. Improving the allocation of educational resources can expand the radiation of high-quality resources to the surrounding weak areas; In weak areas, high-quality schools should be added to improve the construction of teachers.

The digital revolution and skilled workforce define the advances and evolution of any nation’s population. To stay competitive, an education system must cater to the demands of the technological age. The latest technological advancements have improved the quality of modern education. New advances in ICT, the transformation of learning, and a constantly competitive environment are nearly reimagining the academic spectacle. In the last decade, there has been a gap in research on online teaching and learning and very few systematic reviews has been done of this area. This systematic review of literature aims to address this gap by examining 55 research articles on online teaching and learning published in eleven journals in the last two decades. These studies were examined for publication trends and patterns, research themes, research methods, and research settings. While there has been a slight decrease in the number of studies on online learning in 2014 to 2016, it has then continued to increase in 2018, especially after the pandemic. The majority of these studies were quantitative in nature and examined higher education learners. Online learning was categorized into four themes and a framework across Technology, Human, Design and also Course and Evaluation was developed through this study. Several studies have examined online learner’s characteristics and learner’s engagement, as well as organization-level topics such as technology, content, and management. Research on online learning engagement is mixed, with some showing higher student engagement compared to traditional classrooms, while others note lower engagement due to lack of contact and feedback. Effective online education requires secure technology, experiential learning, teacher preparedness, learner motivation, and robust feedback mechanisms. Future learners, particularly self-directed ones, may prefer online learning. Interactive educational settings, collaborative platforms, and supplemental online programs enhance accessibility but require constructive instruction. Institutions must ensure equal success in online courses and traditional classrooms to meet diverse learner needs and sustain enrollment. By employing rigorous inclusion criteria and a systematic approach, this study offers insights into the various paradigms of online learning. However, there is still a need for additional research on these topics.

Artificial Intelligence (AI) is reshaping primary education across literacy, numeracy, inclusion, and classroom orchestration. This systematic review synthesizes empirical research from 2020 to 2025 to clarify how AI enhances learning and teaching in primary education. Drawing on 94 studies identified through a PRISMA-guided process, the evidence shows that AI adds the greatest value when it (a) personalizes feedback and practice, (b) scaffolds inquiry and computational thinking, and (c) augments teacher decision-making through learning analytics. Reported gains include reading fluency, problem-solving, motivation, and participation among diverse learners. Yet progress remains constrained by uneven teacher AI-TPACK and assessment literacy, infrastructural inequities, and ethical concerns regarding transparency, bias, and data governance. Across studies, the most sustainable outcomes emerged from human-in-the-loop approaches where teachers interpret and moderate AI insights. The review argues that adequate and equitable AI integration depends less on technical sophistication than on pedagogically grounded design, robust professional development, and policy frameworks ensuring accountability and equity by design. These findings inform future directions for educational policy, teacher preparation, and the ethical governance of AI-supported learning ecosystems.

Due to the rapid growth of the use of computers and increasing use of the Internet in education a large number of Web-based educational applications have been developed and implemented. However, very few of them are pedagogically intelligent and interactive for learning purposes. The Web-based intelligent learning has become more effective in the past decade due to increasing use of the Internet in education. A literature search indicates that there is a lack of relevant comprehensive research concerning the efficiency of computer-assisted instructions used in engineering education. The main focus of the research described in this chapter is on the comprehensive review of design and development of the Web-based authoring tool for an Intelligent Tutoring System in engineering education. The chapter outlines and discusses important issues of the development of Intelligent Tutoring System (ITS) in engineering education with an example of the development of a Web-Based Computer-Assisted Tutorials and Laboratory Procedures (WCALP).KeywordsEducational SoftwareExpert ModelInstructional SoftwareEducational Technology PublicationNatural Language DialogueThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.