Digital dynamics: Exploring the intersection of AI, animation, and personalized learning

This study investigates the potential of artificial intelligence (AI) in personalizing learning for students in remote Chinese regions, a context characterized by significant educational disparities and resource scarcity. Using a quantitative quasi-experimental design with a pre-test/post-test control group, the research evaluated the impact of an AI-powered personalized learning platform over a six-month period. The sample consisted of middle school students and teachers in an underserved rural area. Key metrics included student academic performance, engagement levels, and teacher-reported workload and technology confidence. The findings reveal a statistically significant positive effect of the AI intervention. Students in the experimental group showed a remarkable 26.2% average score improvement, nearly double that of the control group. This was accompanied by a significant increase in student engagement, as evidenced by a higher post-test score on a motivational scale. Concurrently, teachers in the AI group reported a substantial reduction in time spent on grading and a marked increase in their confidence in using educational technology. The results suggest that AI offers a viable and effective strategy for bridging educational resource gaps, enhancing student outcomes, and empowering educators in geographically isolated and under-resourced environments. The study provides a strong empirical foundation for the strategic integration of AI into educational policy aimed at promoting equity.

Currently, the online learners are increasingly demanding more personalized learning since the web technology, and the learners have individual features of characteristics such as learning goals, experiences, interests, personality traits, learning styles, learning activities, and prior knowledge. A personalized learning process requires an adaptive learning system (ALS). In order to adapt, a learner model is required. Thus, modelling the learner model in an adaptive system environment is a key point to success in recommending the learner. The ontology-based approach was used to model the adaptive learning model in this research. Ontology is a graph structure that consists of a collection of contexts, relationships, and models which related to contexts. The ontology of the learner model enables to produce a description of learner’s properties which contains important information about domain knowledge, learning performance, interests, preference, goal, tasks, and personal traits. Keywords - Personalized Learning, Adaptive Learning System, Ontology, Learner Model

The informatisation of society and the rapid development of technology significantly outpaces its implementation, including in education. The use of the latest technologies reduces the dependence of teaching and learning on students’ physical location, enhances the structure and content of the educational process, and improves its efficiency, personalisation and student engagement. Artificial intelligence tools facilitate more effective information presentation and assimilation. This research aimed to examine how technologies can be implemented in education to enhance efficiency and student-centred learning. This article presented experiences in using technologies for teaching English for Specific Purposes, with a particular focus on the application of artificial intelligence and the Internet of Things. Their role in increasing student motivation and ensuring that the educational process remains aligned with current trends was explored. Theoretical research methods, including analysis, synthesis, comparison, generalisation, and concretisation, alongside scientific methods such as chronological analysis and cause and-effect examination, enabled an investigation of the challenges associated with integrating emerging technologies into education. The current state of the problem, along with the benefits and challenges of using innovations in education, was analysed through content analysis. This article compared the definitions of artificial intelligence and highlighted its transformative potential in personalised learning. Key applications of AI in education, including natural language processing (NLP) and chatbots, which enhance learning experiences, were examined. Ethical considerations regarding AI usage in education were also discussed. The advantages of using AI-powered learning platforms for both students and teachers were explored. Particular attention was to AI’s ability to analyse students’ strengths, weaknesses, and preferences, ensuring that content is relevant and appropriately challenging for each individual. Continuous evaluation adjusts question difficulty based on student performance, ensuring they are challenged without being overwhelmed. Instant feedback enables students to identify mistakes immediately, which fosters rapid learning and improvement. Solutions to identified challenges were suggested

Preface S. L. Christenson, A. L. Reschly, and C. Wylie.- SECTION I: WHAT IS STUDENT ENGAGEMENT?.- Jingle, Jangle, and Conceptual Haziness: Evolution and Future Directions of the Engagement Construct A. L. Reschly and S. L. Christenson.- Developmental Dynamics of Student Engagement, Coping, and Everyday Resilience E. A. Skinner and J. R. Pitzer.- Engagement across Developmental Periods D. Mahatmya, B. J. Lohman, J. L. Matjasko, and A. Feldman Farb.- Ethnicity and Student Engagement G. E. Bingham and L. Okagaki.- Student Engagement: What is it? Why does it matter? J. D. Finn and K. Zimmer.- Section I Commentary: So What is Student Engagement Anyway: Commentary on Section I J. Eccles and M. Wang.- SECTION II: ENGAGEMENT AS LINKED TO MOTIVATIONAL VARIABLES.- A Self-Determination Theory Perspective on Student Engagement J. Reeve.- Achievement Goal Theory, Conceptualization of ability/Intelligence, and Classroom Climate E. M. Anderman and H. Patrick.- School Identification K. E. Voelkl.- Self-Efficacy as an Engaged Learner D. H. Schunk and C. A. Mullen.- A Cyclical Self-Regulatory Account of Student Engagement: Theoretical Foundations and Applications T. J. Cleary and B. J. Zimmerman.- Academic Emotions and Student Engagement R. Pekrun and L. Linnenbrink-Garcia.- Students' Interest and Engagement in Classroom Activities M. Ainley.- Section II Commentary: Motivation and Engagement: Conceptual, Operational, and Empirical Clarity A. Martin.- SECTION III: ENGAGEMENT AND CONTEXTUAL INFLUENCES.- Parental Influences on Achievement Motivation and Student Engagement J. Bempechat and D. J. Shernoff.- Families as Facilitators of Student Engagement: Toward a Home-School Partnership Model J. N. Raftery, W. S. Grolnick, and E. S. Flamm.- Teacher-Student Relationships and Engagement: Conceptualizing, Measuring, and Improving the Capacity of Classroom Interactions R. C. Pianta, B. K. Hamre, and J. P. Allen.- The Role of Peer Relationships in Student Academic and Extracurricular Engagement J. Juvonen, G. Espinoza, and C. Knifsend.- Understanding Student Engagement with a Contextual Model S. Lam, B. P. H. Wong, H. Yang, Y. Liu.- Allowing Choice and Nurturing an Inner Compass: Educational Practices Supporting Students' Need for Autonomy A. Assor.- The Engaging Nature of Teaching for Competency Development R. Hipkins.- Assessment as a Context for Student Engagement S. Nichols and H. Dawson.- Section III Commentary: Socio-Cultural Contexts, Social Competence, and Engagement at School K. Wentzel.- SECTION IV: ENGAGEMENT AND STUDENT OUTCOMES.- The Relationship between Engagement and High School Dropout R. W. Rumberger and S. Rotermund.- High School Reform and Student Engagement M. H. Davis and J. M. McPartland.- The Power of Mindsets: Nurturing Engagement, Motivation, and Resilience in Students R. Brooks, S. Brooks, and S. Goldstein.- The Relations of Adolescent Student Engagement with Troubling and High-Risk Behaviors A. Griffiths, E. Lilles, M. Furlong, and J. Sidwha.- Trajectories and Patterns of Student Engagement: Evidence from a Longitudinal Study C. Wylie and E. Hodgen.- Instructional Contexts for Engagement and Achievement in Reading J. T. Guthrie, A. Wigfield, and W. You.- A Self-regulated Learning Perspective on Student Engagement C. A. Wolters and D. J. Taylor.- Classroom Strategies to Enhance Academic Engaged Time M. Gettinger and M. J. Walter.- Deep Engagement as a Complex System: Identity, Learning Power and Authentic Enquiry R. Deakin Crick.- Section IV Commentary: Outcomes of Engagement and Engagement as an Outcome: Some consensus, divergences and unanswered questions M. Janosz.- SECTION V: MEASUREMENT ISSUES, INSTRUMENTS, AND APPROACHES.- Measuring Student Engagement: The Development of a Scale for Formative Use C. W. Darr.- Systems Consultation: Developing the Assessment-to-Intervention Link with the Student Engagement Instrument J. J. Appleton.- Finding the Humanity in the Data: Understanding, Measuring & Strengthening Student Engagement E. Yazzie-Mintz and K. McCormick.- The Measurement of Student Engagement: A Comparative Analysis of Various Methods and Student Self-Report Instruments J. A. Fredricks and W. McColskey.- Issues and Methods in the Measurement of Student Engagement: Advancing the Construct through Statistical Modeling J. Betts.- Section V Commentary: Possible New Directions in the Measurement of Student Engagement K. Samuelsen.- Epilogue S. L. Christenson, A. L. Reschly and C. Wylie.

This approach is grounded in the recognition that each learner has unique strengths, preferences, and challenges, which necessitate a shift from traditional, one-size-fits-all teaching methods. Central to the success of personalized learning is the concept of student engagement and motivation, which are critical drivers of academic achievement and long-term educational outcomes. This can be achieved through a variety of strategies, such as allowing students to choose topics of interest, setting personalized learning goals, and offering a range of resources and tools that cater to different learning styles. Moreover, personalized learning environments often incorporate adaptive technologies that respond to individual learning needs. These technologies use data to provide real-time feedback and adjust instructional content accordingly, ensuring that students are neither overwhelmed nor under-challenged. The use of such technologies can help maintain an optimal level of difficulty and support, which is crucial for sustaining student motivation and engagement.

This research explores students' perceptions regarding the impact of flexible working arrangements (FWA) approach on their academic performance, with a focus on the moderating role of blended learning (BL) as an educational setting. A survey was conducted with students across foundation, undergraduate, and postgraduate levels in a Malaysian university to gather insights on how FWA and BL intersect to influence their performance. The findings indicate that FWA positively and significantly influences student performance. Additionally, the integration of BL was found to enhance the relationship between FWA and student performance. This suggests that the flexibility afforded by FWA, when combined with the benefits of BL such as increased engagement and personalised learning, can lead to improved academic outcomes from the students' perspective. The study provides practical implications for educators and institutions seeking to leverage technology and flexible arrangements to create more effective and adaptive learning environments. The theoretical contributions lie in enriching existing frameworks on student engagement, educational technology integration, and the dynamics of BL settings. Overall, this research offers valuable insights into how students perceive the combined effects of FWA and BL on their academic performance. Keywords: Student perceptions, flexible working arrangements, blended learning and teaching, students' performances, university students

Background: Artificial intelligence (AI) technology is transforming education through personalised learning and creates dynamic, adaptive learning environments that cater to each student’s unique strengths and challenges. Developed countries have largely integrated AI technologies into their learning institutions, while the discipline is in its infancy in developing countries such as South Africa (SA). Objectives: This study aims to contextualise and recommend the strategy that institutions of higher learning in SA can adopt to integrate AI into their institutions. Method: A systematic literature review (SLR) method was followed. Publications published between 2018 and 2024 in the Multidisciplinary Digital Publishing Institute (MDPI) and Taylor & Francis Online databases using the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Following an initial search, 114 documents were retrieved, and, using inclusive criteria, 29 papers were chosen for analysis. The databases were selected because of their unique benefits in terms of accessibility, material breadth, and researcher-specific functions, unlike other sources. Results: Results show that to integrate AI, the following should be considered: planning, collaborations, training, and ethical standards to guarantee responsible use and productivity. This will enhance teaching and learning, well preparing students for a future whereby AI is widely used in the workplace. Conclusion: To integrate AI into learning institutions, a tailored approach needs to ensure that the AI technology improves teaching, enhances administrative procedures, and adheres to the institution’s rules and regulations. Contribution: This article forms a theoretical and methodological contribution to advancing knowledge that may inform policy and practice makers.

Integrating Artificial Intelligence (AI) in education has significantly transformed teaching methodologies, assessment systems, and student engagement. This study evaluates the impact of AIdriven personalized learning and compares AI-assisted grading systems with traditional methods. The research examines the relationship between AI adoption and student performance using a dataset of 115 respondents and applying statistical techniques such as correlation analysis, chi-square tests, and parabolic distribution modelling. Findings reveal that while AI contributes to improved learning outcomes, its influence on teaching preference remains statistically weak (correlation coefficient r = 0.089). Furthermore, chi-square analysis (χ² = 9.57, p = 0.144) suggests no significant association between AI performance perception and preference for AI-assisted teaching. The parabolic representation of mean (2.43), median (3.0), and mode (3.0) highlight a concentration of responses favoring AI-driven improvements, though variations exist across demographics. These results indicate that while AI enhances learning effectiveness, external factors such as subject complexity, institutional policies, and individual teaching styles may influence AI acceptance. The study contributes to future AI research by identifying key adoption trends, recommending data-driven strategies for AI integration in education, and addressing potential challenges such as algorithmic bias and accessibility gaps. The findings serve as a foundation for policymakers, educators, and researchers to refine AI-driven academic frameworks, ensuring equitable and efficient learning environments. Keywords: Artificial Intelligence, Personalized Learning, AI Grading, Statistical Analysis, Education Technology, Data-Driven Decision Making

The adoption of AI-driven chatbots has substantially influenced language teaching pedagogies and transformed the educational landscape in terms of providing affective support to EFL students to a great extent. Accordingly, research on emotional AI in EFL education is characterized by the deployment of AI applications to provide emotional support for learners and evaluate the emotional impacts of AI through their affective consequences. This study explores the studies conducted about the role of AI-enhanced social-emotional learning frameworks in promoting engagement and emotional well-being in EFL contexts. In other words, the principal objective of this research is to analyze the effects of integrating Artificial Intelligence (AI) tools into English language pedagogy in terms of facilitating student engagement and fostering their emotional well-being. The scholarly studies underscore the efficacy of AI-enhanced social-emotional learning in heightening student engagement and emotional well-being by tailoring learning to learners’ own profiles (Zong& Yang, 2025). A key function of these AI systems is the cultivation of measurable improvements in emotional regulation, cognitive engagement, and higher academic performance. In addition, AI-powered corrective and motivational elements significantly strengthened EFL learners' self-reflective practices, with increased confidence in expressing original ideas, and enjoyment in productive language skills (He, Abbasi & He, 2025). In this regard, emotional AI applications in EFL settings have been utilized in EFL contexts, where they have shown considerable efficacy on the behavioral, cognitive, and affective dimensions of student engagement. The most important and functional affordances of AI tools in English language pedagogy include the delivery of instructional guidance, creating customized content, and offering the capacity for emotion analysis (Liu et al., 2024). This paper will conclude by analyzing the potential factors that shape the adoption and effectiveness of emotional AI technologies in EFL educational contexts.

This study explores the role of Artificial Intelligence (AI) in facilitating personalised learning among public sector university teachers in South Punjab, Pakistan. The objective of the study are examine how university teachers perceive AI’s potential in tailoring learning experiences to individual student needs. To compare differences in perceptions between teachers with varying levels of AI usage. To analyze disciplinary variations in the adoption and impact of AI-based personalized learning practices. To investigate teachers’ confidence in the ethical and privacy dimensions of using AI in educational contexts. Using a structured questionnaire with a 5-point Likert scale, data were collected from 400 teachers selected through multistage random sampling. Descriptive and inferential analyses, including mean, standard deviation, t-tests, and ANOVA, were performed using SPSS. Findings reveal that teachers generally perceive AI positively, acknowledging its potential to improve learning outcomes, provide personalised learning pathways, and enhance student engagement. Significant differences were observed across gender and departmental affiliations. Teachers also emphasized the need for professional development and ethical guidelines in AI implementation. The study concludes that AI is a valuable tool for personalised learning but requires proper training, ethical oversight, and balanced integration with human-led instruction.

Introduction: The integration of artificial intelligence (AI) into educational technology has transformed assessment practices by enabling more personalized and adaptive learning experiences. This study examined the development and effectiveness of an AI-powered adaptive quizzing system designed to adjust question difficulty in real time based on student performance and to deliver immediate feedback. Methods: The system employed learning analytics and machine learning algorithms to identify individual learning patterns and recommend targeted exercises aligned with students’ weaknesses. A mixed-methods design was implemented involving 200 high school students divided into an experimental group—using the AI-based adaptive quiz platform—and a control group relying on conventional assessments. Data were collected through pre- and post-tests, student surveys, and instructor interviews. Results: Findings showed that students using the AI-powered system demonstrated significantly higher engagement, improved knowledge retention, and greater motivation compared to those completing static quizzes. The adaptive mechanism facilitated more efficient learning by aligning question difficulty with students’ proficiency levels while providing timely corrective feedback. Conclusions: This study concluded that AI-driven adaptive assessments offer substantial advantages over traditional assessment formats and hold strong potential for enhancing personalized learning in digital classrooms. The findings contribute to the field of educational technology by providing empirical evidence of the role of AI in strengthening formative assessment and differentiated instruction.

This study aims to use dialectical thinking to explore the impacts and responses of Artificial Intelligence (AI) empowerment on students’ personalized learning. The effect of AI empowerment on student personalization is dissected through a literature review and empirical cases. The study finds that AI plays a significant role in promoting personalized learning by enhancing students’ learning effectiveness through intelligent recommendation, automated feedback, improving students’ independent learning ability, and optimizing learning paths, however, the wide application of AI also brings problems such as technological dependence, cheating in exams, weakening of critical thinking ability, educational fairness, and data privacy protection to students. The study proposes recommendations to strengthen technology regulation, enhance the synergy between teachers and AI, and optimize the personalized learning model. AI-enabled personalized learning is expected to play a greater role in improving learning efficiency and educational fairness.

EDFA: Ensemble deep CNN for assessing student's cognitive state in adaptive online learning environments

This article provides a brief overview of the application and development of artificial intelligence (AI) in the field of education under the background of New Liberal Arts. It also highlights the importance and current status of innovative approaches in college English teaching. Furthermore, it presents the research objectives and methods. The application of artificial intelligence in education has witnessed significant growth and advancement in recent years. AI technologies, such as machine learning and natural language processing, have been utilized to enhance various aspects of education, including personalized learning, intelligent tutoring systems, and automated grading. These applications have shown promising results in improving students' learning outcomes and engagement. In the context of college English teaching, innovation is crucial to meet the evolving needs and expectations of students. Traditional teaching methods often fail to cater to individual learning styles and preferences, leading to limited effectiveness and student disengagement. Therefore, there is a pressing need to explore innovative approaches that leverage AI technologies to create more interactive and personalized learning experiences. The research aims to investigate the potential of integrating AI into college English teaching and its impact on students' learning outcomes and engagement. The study will employ a mixed-methods approach, combining quantitative analysis of students' performance data with qualitative interviews and surveys to gather comprehensive insights. By examining the effectiveness and challenges of AI integration in English teaching, this research seeks to provide valuable recommendations for educators and policymakers to enhance the quality of college English education. Above all , this study aims to explore the application of artificial intelligence in college English teaching, considering its potential to improve learning outcomes and engagement. By adopting a mixed-methods research approach, this study intends to provide valuable insights and recommendations for educators and policymakers to promote innovative and effective English teaching practices.

This chapter analyzes the impact of personalized learning on student engagement and achievement within the context of STEAM (science, technology, engineering, arts, and mathematics) education. Personalized learning, a pedagogical strategy that tailors instructional methods and resources to the unique needs, talents, and interests of individual learners, has gained popularity as a means to create deeper engagement and better educational outcomes. This chapter dives into the theoretical underpinnings of individualized learning and evaluates its potential to change established teaching approaches in STEAM education. The chapter illustrates how individualized learning practices, assisted by adaptive technologies, promote active involvement and higher levels of accomplishment among students. Through case studies and examples, this research gives insights into how individualized learning might define the future of STEAM instruction, ensuring that students not only meet academic standards but also develop the skills necessary for innovation and creativity in the 21st century.