Personal Learning Research Articles

Application and Evaluation of the Effectiveness of Artificial Intelligence in Personalized Learning

The application of artificial intelligence (AI) in personalized learning has demonstrated remarkable advantages, including intelligent assessment, resource recommendation, and teaching optimization, effectively enhancing learning outcomes and engagement. However, this process has also revealed several issues. Firstly, technical challenges and limitations hinder AI's ability to fully adapt to each student's individual needs, requiring further improvements in algorithm accuracy and reliability. Secondly, data privacy and security issues have become increasingly prominent, necessitating robust measures to protect student data from misuse and leakage. Lastly, challenges in the educational system and mindset must be addressed, focusing on how to integrate AI technology into traditional education models while maintaining teaching quality. To address these issues, continuous research and development, enhanced data protection, and the updating of educational systems and mindsets are required to fully harness the potential of AI in personalized learning.

Adapting to the 21st Century Learning: Teaching Strategies of Filipino Sped Teachers in the United States

This study employs a narrative research methodology to explore the instructional modifications and teaching practices utilized by Filipino Special Education (SPED) teachers in the United States to meet the demands of 21st-century learning. Through in-depth, semi-structured interviews with ten purposely selected SPED educators, key themes emerged, including technology integration, differentiated instruction, cultural sensitivity, individualized education plans (IEPs), family and community involvement, and the creation of sensory-friendly environments. Participants highlighted the importance of leveraging technology for personalized learning, implementing varied instructional strategies to accommodate diverse learning styles, and incorporating culturally relevant materials to foster inclusivity. Regular IEP reviews and active family engagement were identified as critical for enhancing student motivation and success. Additionally, creating sensory-friendly classroom environments was emphasized as essential for supporting students' sensory processing needs. The findings indicate that these multifaceted strategies not only promote academic achievement but also enhance social-emotional development and inclusivity among students with special needs. Recommendations include ongoing professional development for educators, increased parental involvement in the IEP process, and systematic reviews of instructional strategies. This research contributes to a deeper understanding of how Filipino SPED teachers adapt their practices in diverse contexts, ultimately informing educational policies and teacher preparation programs aimed at fostering equitable learning environments.

Development of Contextual Visual Media in Increasing MTs Students' Learning Motivation

Motivation has a strategic role in a person's learning activities, without motivation no one will learn and the learning process will not continue. The use of media in learning is very important because it makes the material presented easier to understand and makes students more motivated. Developing digital-based learning media is a good learning media to use in learning in the era of the industrial revolution 4.0 and is believed to be able to increase student motivation. The aim of this research is to develop contextual visual media that can increase student motivation. The method used in this research uses the ADDIE model developed by Dick and Carry. The research results are in the form of contextual visual media which was developed using five stages, namely analysis, design, development, implementation and evaluation. The validity test results are in the very good category, so the visual media developed is suitable for use in research. Based on the t test results of 0.012, these results indicate that the hypothesis is accepted, so it can be concluded that there is an increase in motivation after learning activities using the developed contextual visual media.

Analysis of LLMs for educational question classification and generation

Large language models (LLMs) like ChatGPT have shown promise in generating educational content, including questions. This study evaluates the effectiveness of LLMs in classifying and generating educational-type questions. We assessed ChatGPT's performance using a dataset of 4,959 user-generated questions labeled into ten categories, employing various prompting techniques and aggregating results with a voting method to enhance robustness. Additionally, we evaluated ChatGPT's accuracy in generating type-specific questions from 100 reading sections sourced from five online textbooks, which were manually reviewed by human evaluators. We also generated questions based on learning objectives and compared their quality to those crafted by human experts, with evaluations by experts and crowdsourced participants.Our findings reveal that ChatGPT achieved a macro-average F1-score of 0.57 in zero-shot classification, improving to 0.70 when combined with a Random Forest classifier using embeddings. The most effective prompting technique was zero-shot with added definitions, while few-shot and few-shot + Chain of Thought approaches underperformed. The voting method enhanced robustness in classification. In generating type-specific questions, ChatGPT's accuracy was lower than anticipated. However, quality differences between ChatGPT-generated and human-generated questions were not statistically significant, indicating ChatGPT's potential for educational content creation. This study underscores the transformative potential of LLMs in educational practices. By effectively classifying and generating high-quality educational questions, LLMs can reduce the workload on educators and enable personalized learning experiences.

Computer-assisted education and application in the music flipped classroom: A review of the literature

The application of computer-assisted learning in music education not only changes the traditional teaching mode, in addition, it provides students with a richer and more personalized learning experience. However, the implementation of computer-assisted learning also faces many challenges, such as insufficient technological infrastructure, teachers' role change, and the development of students' independent learning ability. Therefore, this study explored the pedagogical effects of computer-assisted learning applications in music-flipped classrooms and how to enhance students' learning and musical expression through a computer-assisted flipped classroom teaching model by using the literature review method. Through a five-step approach, this study analyzes the current status, problems, challenges and opportunities of computer-assisted education in music-flipped classrooms, aiming to assess its effectiveness and propose strategies to promote innovation and development in music education. The study findings demonstrated that utilising computer-assisted learning in the music-flipped classroom substantially enhanced students' musical knowledge, skills, and performance by fostering active learning participation and interaction. Conclusions drawn from the study found that computer-assisted learning is effective in improving learning outcomes and musical performance and can contribute to the advancement of music education theory and practice. Future academic exploration needs to confront these obstacles and delve into inventive applications of computer-assisted learning. Furthermore, there is a necessity for prolonged investigations to evaluate the enduring influence of music education outcomes through computer-assisted music-flipped classroom instruction.

Human Computer Interaction Strategies for Effective Digital Learning Experiences: From Classroom to Screen

The rapid advancement of technology has transformed traditional classroom settings into complex digital learning environments, emphasizing the need for effective Human-Computer Interaction (HCI) strategies to enhance online learning experiences. This research explores the intersection of HCI and digital learning, focusing on how HCI strategies can improve user engagement, comprehension, and learning outcomes. Through a review of existing literature, this study identifies common challenges faced by learners and educators, such as usability issues, technical difficulties, and inadequate localization. It also highlights effective HCI strategies, including user- centered design, interactive multimedia features, and feedback mechanisms, which are crucial for creating engaging and personalized learning experiences. Findings indicate that personalized learning systems and interactive content significantly enhance performance and achievement by addressing individual learning needs and improving content retention. Best practices derived from user feedback suggest adopting a user-centered design approach, integrating interactive elements, focusing on usability, and continuously updating learning tools. This research underscores the importance of aligning HCI principles with educational technology to foster a more effective and inclusive digital learning environment. Keywords - Human-Computer Interaction (HCI), Digital Learning Environments, User Experience (UX),Interactive Design, Personalized Learning, Educational Technology.

A SWOT analysis of generative AI in applied linguistics: Leveraging strengths, addressing weaknesses, seizing opportunities, and mitigating threats

Background The integration of generative Artificial Intelligence (AI), particularly large language models (LLMs) like ChatGPT and Gemini, into the field of applied linguistics presents transformative opportunities alongside notable challenges. This study aims to evaluate the role of AI in applied linguistics through a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. Methods Using a sample derived from Scopus and Web of Science, we identified relevant studies by applying specific search terms. Our qualitative research design used the SWOT framework to systematically review and analyse studies, ensuring a robust synthesis of data. Results The results of our SWOT analysis revealed the following: 1) Strengths: Enhanced educational tools and resources through AI-driven personalization and interactive learning, increased efficiency and accessibility in generating educational materials, and innovative research applications leveraging semantic similarity measures and advanced linguistic analyses. 2) Weaknesses: Quality and accuracy concerns in AI-generated content, potential over-dependence on AI tools leading to diminished student creativity and ethical issues, and technical limitations in handling complex academic writing tasks. 3) Opportunities: Potential for educational innovation and pedagogical integration, advancements in AI technology to support linguistic research, and fostering global collaboration and access through AI tools. 4) Threats: Risks to academic integrity due to AI-generated content, technological and implementation challenges, and privacy and security concerns regarding data handling. Conclusions Based on the SWOT analysis, we introduced a strategic plan to maximize strengths and opportunities while addressing weaknesses and threats. The strategy includes promoting personalized learning through AI tools, streamlining the creation of educational materials, fostering innovative research applications, ensuring human oversight to maintain content quality, developing ethical guidelines to prevent misuse, investing in necessary infrastructure and training, and implementing robust data protection measures.

Effects of Student Choice on Intrinsic Motivation and Language Proficiency in High School L2 Education

This literature review investigates the impact of student choice on intrinsic motivation and language proficiency in high school second language (L2) classrooms. The study addresses the existing gap in comprehensive research regarding how the implementation of student autonomy influences these educational outcomes. A systematic review of empirical studies, teaching practices, and peer-reviewed articles was conducted. Relevant keywords were used to search journals from different academic databases. The identified studies were evaluated based on their methodology, relevance, and quality. This review finds that providing students with autonomy in selecting learning materials and activities could enhance their intrinsic motivation. Increased autonomy fosters greater interest, engagement, and enjoyment in learning. However, the impact of autonomy on language proficiency is mixed. Some studies demonstrate improved proficiency due to personalised learning experiences, while others indicate that excessive autonomy without adequate instructional support can hinder language development. The review highlights the necessity of balancing autonomy with teacher guidance and considers the developmental stage of high school students. While student autonomy in high school L2 classrooms generally increases intrinsic motivation, its impact on language proficiency necessitates carefully combining autonomy with appropriate instructional support. This review underscores the need for longitudinal research to better understand the long-term effects of autonomy on motivation and proficiency, the development of sophisticated tools for measuring intrinsic motivation, and the implementation of practical strategies for integrating student choice in educational practices.

Impact of AI-Powered Personalized Learning Platforms on the Academic Achievement and Motivation of Computer Science Senior Secondary School Students in Abuja Municipal Area Council (AMAC) FCT-Abuja, Nigeria

This study investigated the impact of AI-Powered Personalized Learning Platforms on the Academic Achievement and Motivation of Computer Science senior secondary school students in Abuja Municipal Area Council (AMAC) FCT-Abuja, Nigeria. The study raised two research questions and two corresponding hypotheses which were tested at 0.05 level of significance. The study adopted a mixed research method; of both the quasi-experimental design and descriptive survey research design. The population of the study comprised all the public senior secondary school students in Abuja Municipal Area Council (AMAC), Federal Capital Territory (FCT). A sample of 100 students was selected through a combination of purposive and simple random sampling techniques for the study. The researchers adopted Code.org as a treatment instrument while the instrument for data collection was a researchers’ designed Computer Achievement Test (CAT) and Motivation Based Questionnaire (MBQ). A draft of the questionnaire was validated through expert judgment involving three lecturers. This was done to establish the face and construct validity. The reliability of the instrument was established through a test-re-test method. A reliability co-efficient of 0.85 and 0.80 was established for both instruments (CAT & MBQ). The data collected were analysed by using independent t-test statistics to test the two formulated hypotheses. The results indicated that Code.org significantly improved the academic achievement of secondary school students compared to traditional learning methods. Furthermore, Code.org positively impacted the motivation of secondary school students. The study recommended amongst others that schools should adopt AI-powered personalized learning platforms such as Code.org for teaching and learning.

How to Construct Immersive Anti-Fraud Education Curriculum

With the rapid development of online commercial services and mobile payments, internet fraud methods are emerging in an endless stream, posing serious threats to individuals and society. To enhance the effectiveness of anti-fraud education and improve the cyber security awareness and anti-fraud capabilities of the public, especially the younger generation. Based on an analysis of the current severe situation of internet fraud and its impact on society, this paper summarizes and evaluates the shortcomings and areas for improvement of existing traditional anti-fraud education methods. It combines the theoretical basis of immersive education and its application in other educational fields to propose the idea of constructing an immersive anti-fraud education curriculum. The paper analyzes the advantages of immersive anti-fraud education, including enhancing student engagement and experience, improving learning outcomes, providing personalized learning plans, stimulating learning interest, and achieving real-time feedback and adjustments. Several feasible implementation methods for immersive anti-fraud education are presented, combining emerging virtual reality technology and real-life interactive games. Each method can enhance students' fraud prevention awareness and skills to varying degrees. The paper also provides a detailed introduction on how to design an immersive anti-fraud education curriculum, including the setting of teaching objectives, content design, technology application, teaching methods, evaluation methods, and continuous improvement. Through this research, we hope to provide educators with a new perspective and method to help students better protect themselves in the online environment, while also contributing to the development of the field of cybersecurity education.

A systematic review of research on emotional artificial intelligence in English language education

In learning English as a foreign language (EFL), students often experience foreign language anxiety. Artificial intelligence (AI) applications that provide emotional support and/or create emotional impacts on student learning, so-called emotional AI applications, have received increased attention. However, there is a lack of a systematic review of studies on emotional AI in EFL education. This paper presents a systematic review of research in this field. The results reveal five affordances of emotional AI in EFL education, namely (1) enabling human-like conversations, (2) providing personalized real-time feedback or instructions, (3) translating images into English text, (4) generating personalized learning content and tasks, and (5) recognizing and analyzing emotions. The first three affordances are more frequently used and have shown promising effects on improving students’ behavioral, cognitive, and affective learning outcomes. Moreover, the findings reveal that emotional support is often integrated with cognitive support; providing emotional support alone may not be enough to support student learning. Meanwhile, providing cognitive support alone can enhance both affective and cognitive learning outcomes. Finally, attention should be paid to the factors that might influence the adoption and effects of emotional AI in EFL education.

Generative AI: hopes, controversies and the future of faculty roles in education

Purpose Generative artificial intelligence (GAI) has seen exponential growth in recent years due to its capability to generate original content through natural language processing and comprehensive language models. This paper aims to investigate the transformative impact of GAI on higher education, focusing on the evolving roles of faculty in the classroom. Design/methodology/approach Using a phenomenological perspective and a process approach, the study involved 25 semi-structured interviews with academicians in higher education. Findings The findings reveal that GAI currently creates biased and commercially driven learning environments, challenging traditional pedagogical models. Despite its potential for enhancing education, the autonomous nature of GAI often prioritizes commercial interests over pedagogical goals. Research limitations/implications The study is limited to faculty perspectives, suggesting future research should include student viewpoints and diverse educational contexts. Practical implications The study highlights the need for higher education institutions to develop comprehensive policies, provide training for faculty and students and design new courses that leverage GAI for personalized learning experiences and enhanced faculty research. Originality/value This paper contributes to the emerging literature on GAI’s impact on education, highlighting its dual nature as both a transformative tool and a potential threat to traditional educational roles and outcomes.

INNOVATIONS IN DISTANCE EDUCATION PRACTICES: A COMPREHENSIVE REVIEW

This study reviewed recent technological advancements in distance education to evaluate their impact on learning experiences and outcomes. The research focused on innovations such as artificial intelligence (AI), virtual reality (VR), augmented reality (AR), learning management systems (LMS), and collaborative tools. Data were collected through a systematic review of peer-reviewed journal articles and scholarly sources, and thematic analysis was employed to identify critical patterns and impacts of these technologies. The findings revealed that AI-driven systems enhanced personalized learning by offering adaptive content and real-time feedback. VR and AR technologies created immersive and interactive learning environments, facilitating experiential learning. The evolution of LMS platforms improved course management and student tracking through advanced features and integration capabilities. Collaborative tools fostered peer interaction and teamwork in remote settings, supporting effective online learning communities. The study concluded that these technological innovations significantly transformed distance education, making it more engaging and efficient. Recommendations for future research included exploring the long-term effects of these technologies, their applications in various educational contexts, and the role of emerging tools in further enhancing distance education practices. KEYWORDS: distance education, technological advancements, artificial intelligence, virtual reality, augmented reality, learning management systems, collaborative tools

Enhancing EFL Higher Education through Fliki Videos: An Artificial Intelligence Implementation Approach

Artificial Intelligence (AI) presents different opportunities for enhancing personalized learning experiences within the field of English as a Foreign Language (EFL) education. Its capacity for adaptive learning is particularly noteworthy, as it caters to individual students’ unique styles. In this context, the purpose of this study was to investigate the impact of Fliki AI videos on EFL teaching and learning and determine students’ perceptions of their use. The participants included 84 pre-service EFL teachers at a private university in Ecuador. A mixed-method design was used to analyze quantitative and qualitative data. Instruments were diagnostic and perception questionnaires and an interview. The main findings revealed that Fliki AI videos effectively enhanced student engagement and comprehension. Additionally, participants reported improved understanding of linguistic and pedagogical concepts through clear explanations and visual representations provided by the videos. The adaptability of Fliki AI videos allowed for personalized learning experiences, while supplementary activities reinforced learning outcomes. Further research could explore optimal design features and instructional strategies for maximizing the effectiveness of these resources.

Re-Evaluating Components of Classical Educational Theories in AI-Enhanced Learning: An Empirical Study on Student Engagement

The primary goal of this research was to empirically identify and validate the factors influencing student engagement in a learning environment where AI-based chat tools, such as ChatGPT or other large language models (LLMs), are intensively integrated into the curriculum and teaching–learning process. Traditional educational theories provide a robust framework for understanding diverse dimensions of student engagement, but the integration of AI-based tools offers new personalized learning experiences, immediate feedback, and resource accessibility that necessitate a contemporary exploration of these foundational concepts. Exploratory Factor Analysis (EFA) was utilized to uncover the underlying factor structure within a large set of variables, and Confirmatory Factor Analysis (CFA) was employed to verify the factor structure identified by EFA. Four new factors have been identified: “Academic Self-Efficacy and Preparedness”, “Autonomy and Resource Utilization”, “Interest and Engagement”, and “Self-Regulation and Goal Setting.” Based on these factors, a new engagement measuring scale has been developed to comprehensively assess student engagement in AI-enhanced learning environments.

Improving The Effectiveness of Mathematics Learning Through Artificial Intelligence: Literature Review

Industrial Revolution 4.0 has fundamentally transformed the educational landscape. Rapid advances in technology, especially artificial intelligence (AI), demand significant curriculum adaptations. Mathematics, as the foundation of science, is the main focus in efforts to integrate AI into the learning process. This research aims to examine in depth the effect of applying AI in mathematics learning on students' learning capacity. Through a comprehensive literature study referring to various scientific journals and Google Scholar articles, this research analyzes various aspects related to the application of AI in mathematics learning. Analysis includes mapping trends in AI implementation, evaluating the effectiveness of various AI approaches, identifying factors that influence successful implementation, and exploring the positive and negative impacts of AI on the learning process. The findings of this research show that the application of AI in mathematics learning has enormous potential to increase student learning motivation, personalize learning, and deepen understanding of concepts. However, research also identifies a number of challenges, such as gaps in technology access, the need for appropriate pedagogical development, and the potential for AI to replace teachers' roles. Based on these findings, this research concludes that integrating AI in mathematics learning requires careful planning and strong collaboration between educators, technology developers, and policymakers. The implications of this research include the importance of flexible curriculum development, continuous teacher training, and the provision of adequate technological infrastructure.

Strategi Manajemen Organisasi Pendidikan dalam Menghadapi Tantangan Globalisasi

This research aims to identify and analyze management strategies of educational organizations in facing the challenges of globalization, focusing on the enhancement of teacher and educational staff competencies, integration of technology in teaching and learning processes, and adaptation to changes in international educational policies. The research methodology employed is a literature review, involving an examination of literature from various sources such as academic journals, books, and research reports. The findings indicate that enhancing teacher competencies through continuous training and international collaboration is crucial in creating an interactive and relevant learning environment. The integration of technology in education allows for more flexible and personalized learning experiences, as well as broad access to global educational resources. Additionally, adaptation to international educational policies, including curriculum adjustments and evaluation standards, ensures the relevance and competitiveness of the education provided. These strategies are interconnected and mutually reinforcing, creating a dynamic and responsive educational system to global changes, thus preparing students to compete at the international level.

Graph neural network based intelligent tutoring system: A survey

Online education is developing rapidly driven by artificial intelligence technology. The massive learning resources lead to information overload and low resource utilization. Intelligent tutoring system (ITS) plays a vital role in the education platform, providing personalized learning services for students. The data obtained from the online education platform has complex correlations, which can be potentially transformed into multi-level graph structures. In recent years, graph neural networks (GNNs) have been tried to be introduced into intelligent learning services due to their superior performance in processing graph-structured data. This paper aims to provide researchers and engineers with a general overview of modeling processes and techniques for intelligent learning services based on GNNs. Through a careful review of the advanced models published between 2019 and 2023, existing research primarily focuses on four detailed areas within the smart services scenario. The GNN models involved are systematically classified, and the principles, pioneers and variants of various models are summarized in detail. Simultaneously, this paper analyzes the applications, the specific problems to be solved, and the technologies and innovations of graph-based models in the four key areas. In addition, we examine the commonly used datasets and evaluation metrics in the field of education. Finally, the current challenges and future development trends are summarized to provide comprehensive and in-depth guidance for research in related fields.

Literacy And Artificial Intelligence: Applications And Concerns

Background: The intersection of literacy and Artificial Intelligence (AI) holds transformative potential for education. AI can significantly enhance literacy by offering personalized learning approaches, automated assessments, and diverse educational applications. In this paper, specific examples of AI's role in literacy are provided, alongside discussions of challenges such as reduced human interaction and the risk of perpetuating educational inequalities. Emphasizing the ethical and social considerations in developing and using AI in education is crucial. Materials and Methods: Our research falls under exploratory studies and aimed to capture the current state in the field of Primary and Secondary Education educators regarding Artificial Intelligence. It was conducted through data collection via an anonymous questionnaire. The questionnaire was sent to all school units in the country through the Primary and Secondary Education Directorates in December 2023. Data were collected through the Google Form platform, and statistical processing was done using SPSS 28. The research sample was formed from the voluntary participation of N=1736 educators. A unique aspect of our research was that after the demographic information, participants were asked not to proceed with completing the questionnaire if they answered "Not at all" to question A1: How well do you know what Artificial Intelligence is? Thus, the first data showed that 51% of the participating educators knew nothing about AI, while 49% knew a little, a lot, or very much. After filtering out incomplete questionnaires, our sample was N=866, and data processing was based on this. Results: Our research aimed to explore educators' perceptions regarding the application of AI in literacy development and to identify the key factors influencing these perceptions. The theoretical framework highlighted the potential benefits of AI in education, such as personalized learning, automated assessment, and increased student engagement. Our findings largely align with this framework, indicating that educators recognize the positive effects of AI, particularly in areas such as efficient teaching methods and enhanced learning. Conclusion: The conclusion highlights the importance of harnessing AI for literacy development while fostering students' critical thinking to mitigate potential negative impacts. Initial findings from exploratory research on AI in education are also presented.

The Untold Story of Training Students with Generative AI: Are We Preparing Students for True Learning or Just Personalization?

This special issue joins the recent but growing effort to expand understanding of integrating generative AI. While generative AI tools like ChatGPT offer great opportunities for personalized learning, it is important to think critically about what type of learning we are reinforcing through the convenience and customization offered by AI. The reliance on AI for scaffolding and personalized prompts can risk undermining students' independent thinking and problem-solving abilities. Additionally, the emphasis on personalized learning could deter the development of collaboration skills. As we continue to integrate AI into educational practices, we need to work towards balancing the benefits of personalization with the goal of fostering self-reliant, critical thinkers who can collaborate and evaluate AI-generated content.