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Affective intelligent tutoring systems (ATSs) are gaining recognition for their role in personalized learning through adaptive automated education based on students' affective states. This scoping review evaluates recent advancements and the educational impact of ATSs, following PRISMA guidelines for article selection and analysis. A structured search of the Web of Science (WoS) and Scopus databases resulted in 30 studies covering 27 distinct ATSs. These studies assess the effectiveness of ATSs in meeting learners' emotional and cognitive needs. This review examines the technical and pedagogical aspects of ATSs, focusing on how emotional recognition technologies are used to customize educational content and feedback, enhancing learning experiences. The primary characteristics of the selected studies are described, emphasizing key technical features and their implications for educational outcomes. The discussion highlights the importance of emotional intelligence in educational environments and the potential of ATSs to improve learning processes. This review identifies gaps in the current research and suggests future directions, including broader implementation across diverse educational settings and deeper integration of affective data to refine system responsiveness. Future research should investigate the integration of advanced natural dialogue modules and generative AI to create more sophisticated interfaces, underscoring the role of affective adaptation in educational technology. [ABSTRACT FROM AUTHOR]

The use of Artificial Intelligence (AI) in healthcare, particularly in real-time health monitoring and predictive interventions for chronic diseases, has many benefits but also many drawbacks. Existing health risk prediction algorithms face accuracy issues and, due to the wide variety of health profiles, general algorithm applicability is problematic. The proposed model solves this issue by using an advanced AI framework to improve the accuracy of the prediction of Chronic Kidney Disease (CKD) and eliminate false positives. Our hybrid Deep Learning (DL) method blends a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN), thus including real-time feedback to help the system learn from its predictions and results. The proposed system solves a major problem in this area and sets a new benchmark for AI applications in healthcare by directly addressing prediction accuracy. It offers more tailored, accurate, and responsive chronic disease management, improving patient outcomes and healthcare resource efficiency. [ABSTRACT FROM AUTHOR]

The field of Human-Computer Interaction (HCI) is progressing quickly with the incorporation of gesture recognition, which requires advanced systems capable of comprehending intricate human movements. This study introduces a new Dynamic Adaptation Convolutional Neural Network (DACNN) that can adjust to different human hand shapes, orientations, and sizes. This allows for more accurate identification of hand gestures over a wide range of variations. The proposed model includes a thorough process of collecting and preparing data from the Sign Language MNIST dataset. This is followed by a strong data augmentation procedure that provides a wide variety of realistic variations. The architecture utilizes sophisticated convolutional layers to leverage the capabilities of deep learning to extract and synthesize essential gesture features. A rigorous training procedure, supplemented with a ReduceLROnPlateau callback, was used to assure the model's generalization and efficiency. The experimental findings provide remarkable results, showing a substantial accuracy of 99% in categorizing a wide range of hand movements. This study makes a significant contribution to the field of hand gesture recognition by introducing morphological operations, thus enriching input data quality and expanding the model's applicability in diverse HCI environments. [ABSTRACT FROM AUTHOR]

The paper discusses the integration of artificial intelligence (AI) in education, particularly focusing on digital tourism. It highlights the use of AI-assisted learning to personalize educational content and enhance learning experiences for students. Specific examples include AI-powered platforms for designing cultural travel blogs and personalized learning paths for cultural tourism education. The paper also emphasizes the role of AI in supporting self-learning, adaptive learning systems, intelligent tutoring systems, and natural language processing. It showcases case studies from the University of Rome "Tor Vergata" and explores AI's potential to improve accessibility and provide tailored educational experiences for learners with disabilities. [ABSTRACT FROM AUTHOR]

Разработана интегративная схема использования цифровых технологий с целью индивидуализации обучения иностранным языкам. Обзор предыдущих исследований показывает, что адаптивные обучающие системы и интеллектуальные системы способны повысить эффективность обучения. Однако в современных реализациях отсутствуют надежное моделирование индивидуальной траектории обучаемого, валидные механизмы оценки и интеграция с педагогическими принципами. Предлагается оригинальная концептуальная модель, объединяющая адаптивные платформы, многомерную оценку, адаптацию преподавателя и теорию обучения иностранному языку в целостную систему, ориентированную на обучаемого. Эта модель включает в себя циклическую оценку, последовательность изучения материала на основе данных, персонализированную обратную связь и гибкое взаимодействие человека и искусственного интеллекта. Она представляет собой дорожную карту для индивидуальной поддержки учащихся разного возраста и уровня подготовки. Модель способствует более эффективной и увлекательной языковой подготовке, доступной всем учащимся. Даны рекомендации по проведению междисциплинарных исследований для дальнейшего совершенствования этого подхода путем проведения эмпирических исследований. Данная концепция обеспечивает теоретическую базу для создания следующего поколения персонализированных технологий обучения иностранному языку.

This study introduces a novel approach named the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), aimed at personalizing educational pathways through machine learning technology. Our findings reveal that this framework significantly enhances student engagement and learning effectiveness by providing real-time feedback and personalized instructional content tailored to individual learning needs. This research demonstrates the potential of leveraging advanced technology to create more effective and individualized learning environments, offering educators a new tool to support each student's learning journey. The study thus contributes to the field by showcasing how personalized education can be optimized using modern technological advancements. [ABSTRACT FROM AUTHOR]

Affective intelligent tutoring systems (ATSs) are gaining recognition for their role in personalized learning through adaptive automated education based on students’ affective states. This scoping review evaluates recent advancements and the educational impact of ATSs, following PRISMA guidelines for article selection and analysis. A structured search of the Web of Science (WoS) and Scopus databases resulted in 30 studies covering 27 distinct ATSs. These studies assess the effectiveness of ATSs in meeting learners’ emotional and cognitive needs. This review examines the technical and pedagogical aspects of ATSs, focusing on how emotional recognition technologies are used to customize educational content and feedback, enhancing learning experiences. The primary characteristics of the selected studies are described, emphasizing key technical features and their implications for educational outcomes. The discussion highlights the importance of emotional intelligence in educational environments and the potential of ATSs to improve learning processes. This review identifies gaps in the current research and suggests future directions, including broader implementation across diverse educational settings and deeper integration of affective data to refine system responsiveness. Future research should investigate the integration of advanced natural dialogue modules and generative AI to create more sophisticated interfaces, underscoring the role of affective adaptation in educational technology.

This research aims to explore the effectiveness of adaptive learning systems in dynamically modifying content to align with the abilities and knowledge levels of individual learners. By employing data analytics and machine learning algorithms, the study examines how content difficulty adjustment, pacing, content selection, and adaptive feedback contribute to a personalized learning experience. This study embarked on an exploration of the efficacy and implications of adaptive learning systems across diverse educational settings: K-12 classrooms, higher educational institutions, and corporate training environments. Through a multi-modal approach, incorporating both quantitative and qualitative analyses, the study evaluated the potential benefits and transformative impact of these personalized learning tools. Quantitatively, results indicated marked improvements post-intervention: notably, a rise in completion rates, significant enhancement in test scores, and increased engagement durations. Machine learning analyses further revealed patterns among learners, signifying segments that benefited immensely from the intervention. Qualitative feedback, obtained through semi-structured interviews, painted a compelling narrative of learner experiences. Common themes emphasized the system's adeptness at adjusting difficulty, facilitating personalized pacing, and providing nuanced, constructive feedback. Adaptive learning systems emerge as a potent tool in modern educational strategies, blending technology and pedagogy to deliver a tailored, responsive learning experience. However, while the immediate implications are promising, the broader applicability and long-term outcomes warrant further research. This study serves as a foundational exploration, signaling the transformative potential of adaptive learning in reshaping educational landscapes. [ABSTRACT FROM AUTHOR]

Abstract Background The growing necessity of providing better education, notably through the development of Adaptive Learning Systems (ALSs), leveraged the study of several psychological constructs to accurately characterize learners. A concept extensively studied in education is engagement, a multidimensional construct encompassing behavioral expression and motivational backgrounds. This metric can be used to not only guide certain pedagogic methodologies, but also to endow systems with the right tutoring techniques. As such, this article aims to inspire improved teaching styles and automatic learning systems, by experimentally verifying the influence of in-class behaviors in students’ engagement. Results Over 16 math lessons, the occurrence of students’ and instructors’ behaviors, alongside students’ engagement estimates, were recorded using the COPUS observation protocol. After behavior-profiling the classes deploying such lessons, significant linear models were computed to relate the frequency of the students’ or instructors’ behaviors with the students’ engagement at different in-class periods. The models revealed a positive relation of students’ initial individual thinking and later group activity participation with their collective engagement, as well as a positive engagement relation with the later application of instructor’s strategies such as giving feedback and moving through class, guiding on-going work. Conclusions The results suggest the benefit of applying a workshop-like learning process, providing more individual explanations and feedback at the beginning of an interaction, leaving collective feedback and students’ guidance of on-going work for later on. Based on the relations suggested by our models, several guidelines for developing ALSs are proposed, and a practical illustrative example is formulated.

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E-Learning Ecosystems (ELE) offer excellent opportunities to manage teaching activities by incorporating state-of-the-art technologies, practices, and professional support, as well as learning and assessment resources that can be adaptive. Therefore, it can help people with disabilities or conditions such as Autism Spectrum Disorder (ASD) to develop skills. However, some technological factors prevent this population’s implementation of support scenarios and hinder the proper learning process. This paper systematically reviews relevant studies on E-Learning Ecosystems for people with ASD, identifying the influence of Information and Communication Technologies (ICT) on forming ELE and the technological barriers that affect their development and appropriate use on people with ASD. This work conducted a systematic review using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, including a search of five scientific literature databases from 2017 to 2022. The main aspects identified were 1) a shortage in design guides for the implementation of e-learning ecosystems adapted for people with ASD, 2) technological barriers that prevent the development of ELE, and 3) recommendations that help to mitigate the limitations of this field. In addition, the authors identified that the skills with the most significant focus of interest were social, communicative, and cognitive. The most implemented technologies include virtual and augmented reality or mobile applications. Most studies involved children with ASD between 8 and 15 years, followed by works with children between 5 to 8 years. Very few researches linked adults with ASD. Very few studies mention the ASD level of the participants, but most highlight the positive results of implementing ICT in training processes.

Background: The growing necessity of providing better education, notably through the development of Adaptive Learning Systems (ALSs), leveraged the study of several psychological constructs to accurately characterize learners. A concept extensively studied in education is engagement, a multidimensional construct encompassing behavioral expression and motivational backgrounds. This metric can be used to not only guide certain pedagogic methodologies, but also to endow systems with the right tutoring techniques. As such, this article aims to inspire improved teaching styles and automatic learning systems, by experimentally verifying the influence of in-class behaviors in students' engagement. Results: Over 16 math lessons, the occurrence of students' and instructors' behaviors, alongside students' engagement estimates, were recorded using the COPUS observation protocol. After behavior-profiling the classes deploying such lessons, significant linear models were computed to relate the frequency of the students' or instructors' behaviors with the students' engagement at different in-class periods. The models revealed a positive relation of students' initial individual thinking and later group activity participation with their collective engagement, as well as a positive engagement relation with the later application of instructor's strategies such as giving feedback and moving through class, guiding on-going work. Conclusions: The results suggest the benefit of applying a workshop-like learning process, providing more individual explanations and feedback at the beginning of an interaction, leaving collective feedback and students' guidance of on-going work for later on. Based on the relations suggested by our models, several guidelines for developing ALSs are proposed, and a practical illustrative example is formulated. [ABSTRACT FROM AUTHOR]

This study introduces a novel approach named the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), aimed at personalizing educational pathways through machine learning technology. Our findings reveal that this framework significantly enhances student engagement and learning effectiveness by providing real-time feedback and personalized instructional content tailored to individual learning needs. This research demonstrates the potential of leveraging advanced technology to create more effective and individualized learning environments, offering educators a new tool to support each student’s learning journey. The study thus contributes to the field by showcasing how personalized education can be optimized using modern technological advancements.

Since Personalized Instruction increases the motivation, interest, performance and attitude of the student, it is aimed to develop an instructional management system that can be adapted to the individual, taking into account the prior knowledge level of the person who provides the personalization of all instructional materials. The project is basically an interdisciplinary system and product development study covering the fields of educational technology and computer engineering. The research, which was planned as a research and development study, was generally planned in accordance with action research methodologies. Considered within the scope of "Information Access and Management Systems", a learning management system that can be adapted to the individual has been developed with this project. In the system, a database has been created in which students are better introduced. An adaptation of the mathematics course was made as an example given by Arizona State University.

Experiential learning (ExL) is the process of learning through experience or more specifically "learning through reflection on doing". In this paper, we propose a simulation of these experiences, in Augmented Reality (AR), addressing the problem of language learning. Such systems provide an excellent setting to support "adaptive guidance", in a digital form, within a real environment. Adaptive guidance allows the instructions and learning content to be customised for the individual learner, thus creating a unique learning experience. We developed an adaptive guidance AR system for language learning, we call Arigatō (Augmented Reality Instructional Guidance & Tailored Omniverse), which offers immediate assistance, resources specific to the learner's needs, manipulation of these resources, and relevant feedback. Considering guidance, we employ this prototype to investigate the effect of the amount of guidance (fixed vs. adaptive-amount) and the type of guidance (fixed vs. adaptive-associations) on the engagement and consequently the learning outcomes of language learning in an AR environment. The results for the amount of guidance show that compared to the adaptive-amount, the fixed-amount of guidance group scored better in the immediate and delayed (after 7 days) recall tests. However, this group also invested a significantly higher mental effort to complete the task. The results for the type of guidance show that the adaptive-associations group outperforms the fixed-associations group in the immediate, delayed (after 7 days) recall tests, and learning efficiency. The adaptive-associations group also showed significantly lower mental effort and spent less time to complete the task. [ABSTRACT FROM AUTHOR]

Adaptive learning and assessment systems support learners in acquiring knowledge and skills in a particular domain. The learners' progress is monitored through them solving items matching their level and aiming at specific learning goals. Scaffolding and providing learners with hints are powerful tools in helping the learning process. One way of introducing hints is to make hint use the choice of the student. When the learner is certain of their response, they answer without hints, but if the learner is not certain or does not know how to approach the item they can request a hint. We develop measurement models for applications where such on-demand hints are available. Such models take into account that hint use may be informative of ability, but at the same time may be influenced by other individual characteristics. Two modeling strategies are considered: (1) The measurement model is based on a scoring rule for ability which includes both response accuracy and hint use. (2) The choice to use hints and response accuracy conditional on this choice are modeled jointly using Item Response Tree models. The properties of different models and their implications are discussed. An application to data from Duolingo, an adaptive language learning system, is presented. Here, the best model is the scoring-rule-based model with full credit for correct responses without hints, partial credit for correct responses with hints, and no credit for all incorrect responses. The second dimension in the model accounts for the individual differences in the tendency to use hints. [ABSTRACT FROM AUTHOR]

Recent studies are increasingly using brain-imaging techniques in the technology-enhanced learning (TEL) context to understand students' cognitive processes during technology-assisted learning, with the ultimate goal to improve students' outcomes in these environments. Given the importance of the promising impact of brain-imaging techniques in the technology-enhanced learning context, it is of utmost importance to analyze and investigate studies published in the intersection of these research areas. However, despite the growing interest in this promising research field, there is a lack of systematic literature review investigating how brain-imaging techniques have been applied in technology-enhanced learning environments. Therefore, this article presents a systematic literature review (SLR) using a well-accepted guideline to perform a rigorous study of the current literature to investigate, analyze, and understand how brain-imaging techniques have been applied in technology-enhanced learning contexts. This SLR considered studies published in nine academic databases that resulted in a total of 3910 studies that, after the selection process, were reduced to 37 studies (published during 2001–2019) for the final analysis. The studies' content analysis was used to classify the studies according to their objectives, brain-imaging techniques and devices used, educational levels, study domains, and studies outcomes. The main results indicate that i) most studies aim to use brain-imaging methods to measure/detect students' psychological processes while using educational technology to provide a personalized experience, ii) the most used brain-imaging technique in the studies is electroencephalogram, and iii) the most investigated study domain was Mathematics and Biology. Moreover, this article highlights some gaps found in the state-of-the-art and provides insights that can be used for future research. [ABSTRACT FROM AUTHOR]

As remote learning technologies play an increasingly larger role in education, clear evidence of effectiveness is needed for widely used online learning technologies, such as Assessment and LEarning in Knowledge Spaces (ALEKS). By adapting to individual students' knowledge states and personalizing interactive practice and feedback, ALEKS may potentially support learning in mathematics, which is foundational for success in science, technology, engineering, and mathematics fields. Given the mixed findings about the effects of ALEKS in previous research, we conducted a meta-analysis to examine if ALEKS was as effective as traditional instruction in mathematics education. Our analysis included 56 independent effect sizes obtained from 9,238 students in K-12 schools and institutions of higher education participating in 33 research studies between 2000 and August 2020. Results indicated that learning performance with ALEKS was comparable to that with traditional instruction (Hedge's g = 0.05, 95% CI [−0.01, 0.20]) and ALEKS was especially effective when used to supplement traditional instruction (g = 0.43, 95% CI [0.02, 0.83]). These findings have important implications for using ALEKS in mathematics learning across different levels of education. [ABSTRACT FROM AUTHOR]

The use of information technology and, in particular, learning management systems, increases the ability of both the teacher and the learner to achieve their goals in the educational process. Such systems provide educational content, help organize and monitor training, collect progress statistics, and can also take into account the individual characteristics of each user of the system. The purpose of this study is to determine the direction of development of modern learning systems and technologies for their implementation. The evolution of learning management systems, the transition to intelligent learning systems, the main stages of such systems were reviewed, the types of learning sequences were analyzed, the transformationinto adaptive learning systems was identified, and the scheme of the system and its mathematical model were presented. Expertise systems, the theory of fuzzy sets and fuzzy logic, cluster analysis, as well as genetic algorithms and artificial neural networks are defined as the mechanisms for implementing the learning systems. An artificial neural network in an adaptive learning system will allow you to create a unique training program that will build on existing knowledge and the level of perception of educational material by students. By formalizing the intellectual processes that both the teacher and the student carry out, it is possible to automate a certain part of the teacher’s functions, reduce the cost of manual labor, which will make it easier to monitor the learning process and also make the learning process more efficient.

The processes of training air traffic controllers require the development of a quality management system for their training, which should allow to evaluate the results of their training. The main task of assessing the quality of training is to obtain the information necessary for proper conclusion on the success of their training course and assess the expected quality of their work. The article establishes that when training controllers, whose erroneous actions influence the performance of air traffic control tasks, “indirect indicators” are also used along with direct indicators – indicators of the body’s physiological “price” for the results achieved. The choice of indirect indicators is due to the nature of the activity of the controllers. As a rule, indirect indicators are aimed at assessing the intensity of the neuro-emotional stress of the air traffic controller in the process of training. Compliance with the conditions created in the interest of controlling the controller's algorithm of operation is provided by using simulation equipment with a high value of the indicators of completeness and quality of reproduction of the information model, using natural resources to create an environment corresponding to the actual conditions of air traffic control. The article developed a method for evaluating the results of training air traffic controllers, based on the formalization of assessment using the theory of fuzzy sets. An apparatus has been developed to formalize the rules for obtaining estimates of individual and group activities of dispatchers. This approach allowed us to bring estimates of different nature into a single formal system and take them into account using common formal rules.

Mobile internet, cloud computing, big data technologies, and significant breakthroughs in Artificial Intelligence (AI) have all transformed education. In recent years, there has been an emergence of more advanced AI-enabled learning systems, which are gaining traction due to their ability to deliver learning content and adapt to the individual needs of students. Yet, even though these contemporary learning systems are useful educational platforms that meet students’ needs, there is still a low number of implemented systems designed to address the concerns and problems faced by many students. Based on this perspective, a systematic mapping of the literature on AI-enabled adaptive learning systems was performed in this work. A total of 147 studies published between 2014 and 2020 were analysed. The major findings and contributions of this paper include the identification of the types of AI-enabled learning interventions used, a visualisation of the co-occurrences of authors associated with major research themes in AI-enabled learning systems and a review of common analytical methods and related techniques utilised in such learning systems. This mapping can serve as a guide for future studies on how to better design AI-enabled learning systems to solve specific learning problems and improve users’ learning experiences.

Valid measures of student motivation can inform the design of learning environments to engage students and maximize learning gains. This study validates a measure of student motivation, the Reduced Instructional Materials Motivation Survey (RIMMS), with a sample of Chinese middle school students using an adaptive learning system in math. Participants were 429 students from 21 provinces in China. Their ages ranged from 14 to 17 years old, and most were in 9th grade. A confirmatory factor analysis (CFA) validated the RIMMS in this context by demonstrating that RIMMS responses retained the intended four-factor structure: attention, relevance, confidence, and satisfaction. To illustrate the utility of measuring student motivation, this study identifies factors of motivation that are strongest for specific student subgroups. Students who expected to attend elite high schools rated the adaptive learning system higher on all four RIMMS motivation factors compared to students who did not expect to attend elite high schools. Lower parental education levels were associated with higher ratings on three RIMMS factors. This study contributes to the field’s understanding of student motivation in adaptive learning settings.

Valid measures of student motivation can inform the design of learning environments to engage students and maximize learning gains. This study validates a measure of student motivation, the Reduced Instructional Materials Motivation Survey (RIMMS), with a sample of Chinese middle school students using an adaptive learning system in math. Participants were 429 students from 21 provinces in China. Their ages ranged from 14 to 17 years old, and most were in 9th grade. A confirmatory factor analysis (CFA) validated the RIMMS in this context by demonstrating that RIMMS responses retained the intended four-factor structure: attention, relevance, confidence, and satisfaction. To illustrate the utility of measuring student motivation, this study identifies factors of motivation that are strongest for specific student subgroups. Students who expected to attend elite high schools rated the adaptive learning system higher on all four RIMMS motivation factors compared to students who did not expect to attend elite high schools. Lower parental education levels were associated with higher ratings on three RIMMS factors. This study contributes to the field's understanding of student motivation in adaptive learning settings. [ABSTRACT FROM AUTHOR]

In this synthesis, we systematically review research on educational games with adaptivity. Although an adaptive gaming experience provides personalization to learning, the complexity of design makes it difficult to evaluate its effectiveness. In this systematic review, we adopt three analytic approaches: (1) bibliometric analysis, (2) qualitative thematic analysis, and (3) meta-analysis. We identified 62 relevant publications and used bibliometric analysis to visualize the hidden conceptual structure among the articles. We then used thematic analysis to investigate the emerging themes in the research and inform the coding for meta-analysis. Twelve articles that used experimental designs were further screened to model the effect of adding adaptivity to educational games. We found that an adaptive learning condition does not produce a substantial overall effect compared to a non-adaptive condition (g =.11, p =.26). Furthermore, moderator analysis reveals that the design variability of adaptivity does not contribute as much to heterogeneity as hypothesized. However, the effect size is positive when the target outcome focuses on learning (g =.39, p <.001) and engagement (g =.41, p =.13). The effect size is negative when the target outcome focuses on game performance (g = −.27, p =.04). In addition, we find evidence for a potential publication bias based on the distribution of effect sizes. [ABSTRACT FROM AUTHOR]

The goal of adaptive learning systems is to help the learner achieve their goals and guide their learning. These systems make it possible to adapt the presentation of learning resources according to learners' needs, characteristics and learning styles, by offering them personalized courses. We propose an approach to an adaptive learning system that takes into account the initial learning profile based on Felder Silverman's learning style model in order to propose an initial learning path and the dynamic change of his behavior during the learning process using the Incremental Dynamic Case Based Reasoning approach to monitor and control its behavior in real time, based on the successful experiences of other learners, to personalize the learning. These learner experiences are grouped into homogeneous classes at the behavioral level, using the Fuzzy C-Means unsupervised machine learning method to facilitate the search for learners with similar behaviors using the supervised machine learning method K- Nearest Neighbors. [ABSTRACT FROM AUTHOR]