Adaptive Learning Environment Research Articles

Digital Literacy Dan Cyber Socialization In The Context Of 21st Century Education: A Systematic Review

This research provides an in-depth review of the integration of digital literacy and cyber socialization in 21st-century education, positing the importance of these two aspects in preparing students for an ever-evolving world. Using PRISMA's framework for systematic analysis, the study evaluated articles from reputable journals between 2014 and 2024, presenting results that underscore a significant shift in the educational paradigm from traditional to digital. Digital literacy, defined as the ability to use technology efficiently, search and evaluate information, and communicate effectively on digital platforms, has become an essential requirement in modern education. The study shows that digital literacy not only improves technical skills but also supports critical and creative learning among students. In addition, digital literacy education includes teaching ethics and online safety and encouraging responsible internet use. Cyber socialization, with its focus on interaction through social media and digital platforms, has enriched the learning experience through wider collaboration and exchange of resources. However, it also poses challenges such as cyberbullying and security issues, which demand a structured educational response to address related social and psychological issues. The Industrial Revolution 4.0 and the COVID-19 pandemic have accelerated the acceptance of technology in education, emphasizing the importance of character education and the adaptation of innovative teaching methods. Education that is responsive to technological change must integrate digital literacy into all aspects of learning, leading to a more holistic and inclusive approach. Overall, the study underscores the importance of digital literacy and cyber socialization in shaping adaptive and supportive learning environments. 21st-century education must recognize and utilize technology not only as an auxiliary tool but as an integral part of the curriculum, to equip students with the skills needed to succeed in the digital age. By ensuring that digital literacy and cyber socialization are at the heart of education, we can anticipate future needs and challenges, shaping a generation that can not only survive but thrive in an ever-changing digital landscape.

AI and VR Integration in E-Learning: Designing meaningful learning environments

The main idea of the educational process based on artificial intelligence and virtual reality forms a new, effective methodology of training. This article examines how AI facilitates VR environments and enriches them with immediate feedback, learner-generated learning paths and big data, resulting in personalizing the education requirements for a spectrum of learners. The literature review addresses the subject through different areas where AI and VR have been applied in a timely manner to learning and comprehension, as well in fields such as engineering, healthcare, and humanities. Also, the advantages of this integration, including enhanced activity, access to the material, and potential to get practical training, are outlined. But, it is imperative to also consider the implementation costs, technology enforcement issues, as well as the probable data privacy problems. Direction and opportunity are explored, along with such areas as further development of adaptive learning environments, more realistic VR settings, and challenges of data utilization in learning. Finally, this article underlines the valuable possibility of the AI and VR applying in the educational field and the necessity of overcoming the barriers to the educational standards.

To be with artificial intelligence in oral test or not to be: a probe into the traces of success in speaking skill, psychological well-being, autonomy, and academic buoyancy

There has been a surge in employing artificial intelligence (AI) in all areas of language pedagogy, not the least among them language testing and assessment. This study investigated the effects of AI-powered tools on English as a Foreign Language (EFL) learners’ speaking skills, psychological well-being, autonomy, and academic buoyancy. Using a concurrent mixed-methods design, the study included 28 upper-intermediate EFL students from an Ethiopian university. We gave the Michigan Language Proficiency Test to evaluate degrees of proficiency before the TOEFL iBT speaking section, which used ChatGPT for scoring and feedback. Speaking abilities were assessed using pretests, immediate posttests, and delayed posttests. Furthermore, we evaluated the impacts on psychological well-being, autonomy, and academic buoyancy using narrative frames. We used one-way repeated measurements to examine the quantitative data and thematically evaluated the qualitative data. According to the results, speaking abilities, psychological well-being, learner autonomy, and academic buoyancy showed notable increases. The results suggest that by improving skill development, offering individualized feedback, and meeting students' emotional and psychological needs, AI systems like ChatGPT have the capacity to transform language assessment and pedagogy. Encouraging the incorporation of AI technologies to enhance educational outcomes and provide a more flexible and adaptable learning environment, the study presents important implications for various stakeholders.

Advanced functional connectivity analysis with integrated EEG signal enhancement and GTW-EEG-GAN for emotion detection

Emotion recognition is crucial in human-computer interaction and psychological research, utilizing modalities such as facial expressions, voice intonations, and EEG signals. This research investigates AI-driven techniques by employing Graph Neural Networks (GNNs) on functional connectivity matrices derived from EEG data to advance emotion recognition. Our approach integrates sophisticated preprocessing methods, including Integrated EEG Signal Enhancement (IESE) and novel augmentation techniques such as Gaussian Time Warping Generative Adversarial Network (GTW-EEG-GAN). This integration aims at enhancing data quality and diversity. Signal decomposition using EMD-Wavelet Hybrid Decomposition further refines feature extraction, enabling robust analysis of EEG signals. Functional connectivity metrics capture critical neural interactions intended for precise emotion characterization. The GNN architecture effectively processes these features, achieving significant accuracy improvements. Evaluations on the DEAP dataset demonstrate promising results, attaining 94.5% accuracy before hyperparameter tuning and further improving to 98.6% after tuning, highlighting the system's efficacy. The methodology showcases the integration of advanced signal processing techniques with AI, offering a comprehensive framework for future studies. The findings advocate potential applications in areas such as personalized mental health monitoring, adaptive learning environments, and responsive gaming experiences, demonstrating the broad impact and versatility of AI-driven EEG-based emotion recognition.

Generative AI Applications in Architecture, Engineering, and Construction: Trends, Implications for Practice, Education & Imperatives for Upskilling—A Review

This study investigates the current landscape of generative AI and LLM applications in architecture, engineering, and construction (AEC), focusing on trends, practical implications, educational strategies, and imperatives for upskilling. Employing a six-stage systematic review sourced from Google Scholar, Scopus and Web of Science, 120 papers were analyzed to provide a comprehensive understanding of the role of these technologies in shaping the future of the AEC industry. By addressing these objectives, the research contributes to enhancing knowledge about the potential impacts of generative AI and LLMs on the AEC industry and provides insights into strategies for leveraging these technologies effectively. This study underscores the transformative impact of AI and advanced technologies on the AEC sector and education. By enhancing learning experiences and optimizing construction processes, AI fosters personalized education and efficient project management. The study’s significance lies in its identification of necessary skills and competencies for professionals, ensuring effective AI integration. Implications include the need for continuous professional development, formal education, and practical training to leverage AI’s potential fully. This paves the way for sustainable, intelligent infrastructure and accessible, adaptive learning environments, driving innovation and efficiency in both fields.

A Model-Driven Approach to Developing Scalable Educational Software for Adaptive Learning Environments

This research presents a model-driven approach to the development of scalable educational software tailored to adaptive learning environments. With the increasing demand for personalized education, adaptive learning systems play a crucial role in meeting diverse student needs by adjusting instructional content dynamically. This paper proposes a software engineering framework that integrates model-driven development (MDD) techniques with scalability principles, allowing for the efficient design and implementation of educational applications that can handle varying workloads and user demands. The framework emphasizes modular architecture, reusability, and flexibility to ensure that software can evolve with emerging educational requirements. Key components include the design of a learning content management system (LCMS) and the application of adaptive algorithms to personalize learning pathways. Additionally, this study explores the integration of cloud technologies to enhance the scalability and performance of educational platforms. A prototype system was developed and tested in a controlled environment, showing significant improvements in scalability, system performance, and student engagement compared to traditional static e-learning platforms. The results indicate that the model-driven approach not only improves software development efficiency but also offers a robust solution for creating adaptive educational systems that can scale to meet the growing needs of learners and institutions. This research contributes to the field of educational software development by providing a systematic methodology for building scalable and adaptive learning environments using advanced software engineering techniques.

Enhancing Student Engagement with AI-Driven Personalized Learning Systems

This paper explores the impact of AI-driven personalized learning systems on enhancing student engagement in educational settings. With the increasing integration of artificial intelligence (AI) in various sectors, education is also experiencing a shift towards more adaptive and personalized learning environments. The study investigates how personalized learning paths, powered by AI algorithms, can address diverse learning needs and promote greater involvement from students. Through a comprehensive analysis of engagement metrics, pre-and post-implementation comparisons, and surveys from both students and educators, this research identifies key factors that contribute to improved student motivation, interaction, and academic performance. The findings suggest that AI-driven systems not only provide tailored learning experiences but also foster a deeper connection between students and their learning content. The paper concludes with recommendations for future research and practical applications in educational institutions to further optimize the use of AI for enhancing student engagement.

Managing Personalization and Collaboration in Education: A Systematic Review of Educational Practices

This systematic literature review analyzes 20 carefully selected documents from the Scopus database. This study explores ways that align with Islamic educational management's principles to balance personalized learning outcomes and community empowerment in academic settings. The selection of documents is based on relevance to the focus and recent developments in Islamic education. The qualitative analysis method uses thematic coding and VOS Viewer. The analysis results indicate that Islamic approaches like collaboration, reflection, and feedback can achieve balanced learning outcomes. Collaborative filtering helps select learning materials according to student's needs, while reflection and feedback strengthen positive interactions and ukhuwah within the learning community. In addition, computer-based learning and e-learning allow students to learn at their own pace. A flexible curriculum allows for adjustments to students' interests by the principle of taysir (making it easy). These findings recommend that educational institutions integrate a multifaceted approach to create an inclusive, adaptive, and Islamic-oriented learning environment.

Analyzing demand for virtual reality based adaptive learning design in engineering faculty of universitas negeri Makassar Indonesia

This research aims to analyze the needs of students regarding the application of Virtual Reality (VR) technology in learning at the Faculty of Engineering. The main focus is to understand how VR can support adaptive learning and improve students' practical ability according to individual ability. This research uses a needs analysis method involving a survey of engineering students. Data collection was conducted through questionnaires and observations to identify students' constraints, needs, and interests in VR-based learning. The collected data were then analyzed quantitatively and qualitatively. The results show that 85% of students struggle to understand complex engineering concepts, and 90% show high interest in using VR for skills practice in a safe environment. In addition, students expected a more interactive and personalized learning approach. Important features they expected included automatic adjustment of difficulty levels and immediate feedback during practice. The findings of this study confirm the importance of integrating VR into engineering education, especially in creating an adaptive learning environment that meets individual needs. VR has the potential to improve students' understanding of concepts and practical skills, as well as to support the development of curriculum that are more relevant to the needs of modern industry.

Theoretical Perspectives on Blended Learning Integrating Traditional and Digital Pedagogies

Blended learning, which combines traditional face-to-face instruction with digital pedagogies, has gained increasing attention in educational research and practice. This paper explores various theoretical perspectives on blended learning, examining its effectiveness, challenges, and future directions. By integrating traditional and digital pedagogical approaches, blended learning offers a flexible and adaptive learning environment catering to diverse student needs. Theoretical frameworks such as constructivism, connectivism, and the Community of Inquiry (CoI) model are discussed to provide a comprehensive understanding of how blended learning can enhance educational outcomes. This study also highlights the importance of teacher training, curriculum design, and technological infrastructure in the successful implementation of blended learning

Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis

This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive Assessment (AA). This review follows the PRISMA systematic review methodology and synthesizes the results of 85 studies that were selected from an initial pool of 818 records across several databases. The results indicate that AI can improve students’ performance, engagement, and motivation; at the same time, some challenges like bias and discrimination should be noted. The review covers the historic development of AI in education, its theoretical grounding, and its practical applications within PL and AA with high promise and ethical issues of AI-powered educational systems. Future directions are empirical validation of effectiveness and equity, development of algorithms that reduce bias, and exploration of ethical implications regarding data privacy. The review identifies the transformative potential of AI in developing personalized and adaptive learning (AL) environments, thus, it advocates continued development and exploration as a means to improve educational outcomes.

Updating Calculus Teaching with AI: A Classroom Experience

In the context of mathematics education, the integration of artificial intelligence (AI) in teaching calculus is revolutionizing instructional methodologies and enhancing learning experiences both inside and outside the classroom. This study explores the use of specific AI tools, including ChatGPT, MathGPT, Gemini, and Wolfram Alpha, to deepen students’ understanding of key mathematical concepts such as derivatives and rates of change through continuous interaction with a virtual tutor. By employing well-designed prompts, these tools facilitated problem-solving exercises that were verified and refined by AI, fostering both precision in calculations and conceptual clarity. Observations from the classroom implementation reveal that students not only improved their accuracy in performing derivative calculations but also developed a clear understanding of the distinctions between average and instantaneous rates of change. The AI tools created a dynamic, adaptive learning environment, providing immediate feedback and simulations that significantly boosted student engagement and motivation. These findings underscore the potential of AI to transform mathematics education by making learning more personalized and accessible, ultimately enhancing educational outcomes and preparing students for future academic and professional challenges. Furthermore, this study introduces an innovative approach to refining AI prompts and interactions, highlighting the importance of iterative improvement to enhance the quality of AI feedback. This approach is crucial for developing better problem-solving skills and ensuring a comprehensive understanding of mathematical concepts.

Transition-aware human activity recognition using an ensemble deep learning framework

Understanding human activities in daily life is of utmost importance, especially in the context of personalized and adaptive ubiquitous learning. Although existing HAR systems perform well-identifying activities based on their inter-spatial and temporal relationships, they lack in identifying the importance of accurately detecting postural transitions that not only enhance the activity recognition rate and reduced the error rate but also provides added motivation to explore and develop hybrid models. It's in this context we propose an ensemble approach of 1D-CNN and LSTM for the task of postural transition recognition, facilitated by wireless computing and wearable sensors. The proliferation of achieving ubiquitous learning will ultimately lead to the creation of adaptive devices enabled by various data analysis and relation learning techniques. Our approach is one of the methods that can be incorporated to enable seamless learning and acquire correlations with adaptive learning techniques. The experimental results on testing datasets including newly produced HAPT (Human Activities and Postural Transitions) show better classification accuracy than existing state-of-the-art HAR approaches (97.84% for transitional activities and 99.04% for dynamic human activities) indicating the capability of the model in ubiquitous learning scenarios and personalized and adaptive human learning environments.

Navigating AI-Powered Personalized Learning in Special Education: A Guide for Preservice Teacher Faculty

Integrating Artificial Intelligence-Powered Personalized Learning (AI-PPL) in special education teacher preparation represents a shift toward tailoring educational experiences to meet the unique needs of preservice teachers and students with disabilities. This article explores the implementation of AI-PPL tools in teacher preparation programs, highlighting their potential to customize learning experiences, provide adaptive feedback, and enhance engagement through interactive content. This review of current AI-PPL functionalities, such as adaptive learning environments and customized feedback mechanisms, demonstrates how AI-PPL can impact teaching practices and student learning outcomes. The article introduces critical attributes for successful AI-PPL integration, such as ensuring accessibility and inclusivity. It calls for further professional development to enhance educator competency and skills. By presenting real-world examples and guiding questions for special education faculty, the authors offer practical insights for educators and faculty members to effectively navigate the complexities of adopting AI technologies in teacher preparation programs.

Utilizing the eXtreme Gradient Boosting Algorithm for Artificial Intelligence-supported Learning Analytics Application

Recent technological advancements, including internet-based distance education and artificial intelligence-supported learning analytics, have significantly impacted the field of education. These advancements not only enhance the efficiency of education but also broaden access to learning while mitigating barriers to implementation. AI-supported learning analytics emerges as a pivotal tool for interpreting data gleaned from educational processes and stakeholders, thereby enhancing educational processes and outcomes. This tool streamlines the measurement, analysis, and evaluation of learning processes, encompassing a wide array of factors and parameters. Moreover, it contributes to the development of personalized and adaptive learning environments. In this study, a predictive model utilizing the XGBoost algorithm has been developed to analyze students' academic achievements. The model forecasts final exam grades based on various student characteristics, including age, participation rate, and exam scores. Evaluating the performance of the AI model involves metrics such as Mean Squared Error, Mean Absolute Error, and R² score. In findings indicate a strong prediction performance, with an R² score of 0.819. As a result of underscore the potential of AI-supported learning analytics as an effective tool for predicting and enhancing student academic performance.

ROLE OF ARTIFICIAL INTELLIGENCE FOR ADAPTIVE LEARNING ENVIRONMENTS IN HIGHER EDUCATION BY 2030

This study focuses on the ability of Artificial Intelligence (AI) to redesign learning experience of higher education by making learning adaptable by the year 2030. Machine learning and natural language processing afford the possibility of developing adaptive learning environment for students. The research also focuses on AI's present and future uses in the learning & issues of realizing those uses. The purposive sampling technique selected 5 faculty members from different universities. A semi-structured interview guide was developed to get data from the participants. Data was analyzed thematically by facilitation of NVivo 14. The potential of AI for enhancing personalized tasks, automated tasks related to administration, and creating interactive learning experiences. The concerns of data confidentiality and ethical considerations were also addressed. By analyzing the improvement of adaptive learning technologies, the study presents views of how AI can improve educational outcomes. Therefore, the findings also emphasize the diverse implications of equalizing technological innovation with keeping important human fundamentals in education while highlighting the justice and inclusivity.

Exploration of Personalized Learning for University Students in the Era of AI

This paper delves into the pivotal role of Artificial Intelligence (AI) in enhancing personalized learning for university students. It begins by highlighting the challenges faced by students in resource acquisition, learning method adaptability, and motivation, and then discusses how AI can address these issues through customized resources and adaptive learning environments. The paper further explores the application of AI in course design, intelligent tutoring, learning analytics, and collaborative learning platforms, showcasing the technology s potential to improve learning efficiency and outcomes. It also examines the future trends and challenges in the popularization of personalized learning, emphasizing the need for ethical considerations and data security. The paper concludes by suggesting that future research should focus on the long-term impact of AI in education and the importance of ensuring equitable access to its benefits for all students.

Exploring the integration of IoT and Generative AI in English language education: Smart tools for personalized learning experiences

English language education is undergoing a transformative shift, propelled by advancements in technology. This research explores the integration of the Internet of Things (IoT) and Generative Artificial Intelligence (Generative AI) in the context of English language education, with a focus on developing a personalized oral assessment method. The proposed method leverages real-time data collection from IoT devices and Generative AI's language generation capabilities to create a dynamic and adaptive learning environment. The study addresses historical challenges in traditional teaching methodologies, emphasizing the need for AI approaches. The research objectives encompass a comprehensive exploration of the historical context, challenges, and existing technological interventions in English language education. A novel, technology-driven oral assessment method is designed, implemented, and rigorously evaluated using datasets such as Librispeech and L2Arctic. The ablation study investigates the impact of training dataset proportions and model learning rates on the method's performance. Results from the study highlight the importance of maintaining a balance in dataset proportions, selecting an optimal learning rate, and considering model depth in achieving optimal performance.

The Influence of Artificial Intelligence (AI) and Mobile Learning on Learning Outcomes in Higher Education: Did the Mediation of Self-Competence Matter?

Islamic Religious Education (PAI) has a significant impact on the development of students' character, morality, and overall learning outcomes. This study aims to investigate the effects of artificial intelligence (AI) and mobile learning on student learning outcomes, with a specific focus on the role of students' self-competence as a mediating factor. Employing a quantitative survey approach, the research included 208 students from the PAI Study Program at IAIN Ponorogo, using probability sampling techniques. Data was collected through Likert-scale questionnaires, and the research data was analyzed using PLS-SEM analysis. The results indicate a positive influence of AI and mobile learning on student learning outcomes, with self-competence playing a crucial role as a mediating factor. These findings highlight the importance of educators promoting self-regulation, self-efficacy, and motivation skills within online learning environments. The study emphasizes the potential of integrating AI and mobile learning to enhance the quality of education and recommends that educators continuously update their knowledge of technological advancements through training and collaboration. Strengthening these competencies can lead to a more interactive, personalized, and adaptive learning environment for students.

Playing single-player tasks together: Dyads’ collaborative activities across two games in Math Garden

Much research on collaborative learning focuses on working together on tasks that are designed with collaboration in mind. However, teachers may ask children to collaborate on single player tasks as well, given a scarcity of available computers in the classroom or assuming that children can learn from each other and motivate each other. In the current study, we studied the conversations of primary school children (N = 64; 39 girls, 25 boys) working together in dyads in an adaptive single-player math learning environment (Math Garden). We aimed to investigate (1) how dyads collaborate across two math tasks aimed at practicing different math skills (arithmetic vs. reasoning skills) embedded within Math Garden and (2) whether collaborative activities differed across the two math tasks. We studied both individual utterances as well as episodes of social metacognitive interactions between the dyad members. We found that children engaged in activities important for successful collaboration, such as relational and metacognitive activities. Moreover, we identified social metacognitive episodes, meaning that children regulated their groups’ learning and that the regulatory processes which took part within the collaborative learning session cannot be reduced to individual activities. We conclude that children engage in collaborative activities when working together on single player tasks. And, in line with previous research, these collaborative activities differed across games, highlighting that collaborative learning is shaped by the task at hand.