ChatGPT integration significantly boosts personalized learning outcomes: A Philippine study

The article examines personalized learning as one of the key directions of educational transformation in the context of digitalization. The relevance of the issue has significantly increased in recent years due to the widespread implementation of distance and blended learning caused by the COVID-19 pandemic and wartime challenges, which have intensified the need for flexible, learner-centered educational models. The study provides a theoretical overview of personalized learning, its conceptual foundations, and its correlation with the concept of individualized learning, emphasizing the shift from teacher-centered instruction to learner agency and self-directed educational trajectories. Special attention is paid to the role of digital technologies in enabling personalized learning. Learning management systems, adaptive educational platforms, learning analytics tools, artificial intelligence applications, and cloud-based solutions are considered as key technological instruments supporting individual pacing, content differentiation, automated feedback, and data-driven educational decision-making. At the same time, the article highlights that digital technologies function as supportive tools rather than substitutes for pedagogical interaction and professional expertise. The empirical part of the study is based on the results of an online survey conducted in January 2026 among 327 educators representing different levels of education, including general secondary, vocational, pre-tertiary, and higher education institutions in Ukraine. The survey explored teachers’ understanding of personalized learning, their practical experience with its implementation, attitudes toward digital technologies, awareness of regulatory frameworks, perceived barriers, and professional needs. The findings reveal a high level of conceptual awareness among educators and a generally positive attitude toward personalized learning, with more than half of the respondents already applying certain elements in their teaching practice. However, the study also identifies significant challenges hindering large-scale implementation, including excessive teacher workload, limited time resources, insufficient methodological support, large class sizes, and the lack of effective tools for rapid learning diagnostics. Educators emphasize the need for professional development, access to modern digital tools, administrative support, and favorable organizational conditions. The article concludes that personalized learning holds substantial potential for improving educational quality and learner motivation in Ukraine. Nevertheless, its effective implementation requires systemic support at institutional and policy levels, integration with broader educational reforms, and targeted measures aimed at strengthening teachers’ digital and pedagogical competencies.

Despite recent advances in technology, personalized learning to address diverse needs of students remains difficult to achieve at scale. With the availability and affordability of smart devices in the era of the Internet of Things, learners, parents, and educators are more “connected” than ever before. Education stakeholders and technology developers can leverage these advances to collect data about, inform, deliver, and improve education for all learners. In this paper, we review the core components of a Smart Learning framework and describe a personalized mastery-based learning system that leverages the framework to deliver personalized learning at scale. In the context of Smart Learning in the Internet of Things, we propose an Ambient and Pervasive Personalized Learning Ecosystem (APPLE), a learner-centered approach that uses Bloom’s Four Agents of Change in the Internet of Things ecosystem to provide learners a comprehensive and personalized learning experience. This ecosystem uses people, processes, data, things, and networked connections to create new capabilities, richer learning experiences, and unprecedented educational opportunities for learners, educators, and families. We further discuss the challenges surrounding the implementation of such an ecosystem, specifically calling for applications of learning engineering approaches, the need of interoperability across systems and components, and the importance of ethical considerations.

Many education institutes are planning to use virtual worlds to offer distance education programs and to reach potential students globally. However, the success of such programs depends on students’ willingness to use virtual worlds for learning continuously. Students’ learning continuance helps to decrease the drop-out rate of the programs. Motivated thus, this study uses the lenses of the balanced thinking-feelings model and socio-constructivism to model how students’ personal learning and social learning experiences influence virtual world learning continuance. Our results show that the utilitarian and hedonic values are critical to learners’ continuous intention of virtual world learning, and they are influenced by learners’ social learning experiences. Social learning factors are also found to interact with students’ personal learning factors to influence their perceived utilitarian and hedonic values of virtual world learning. The findings provide theoretical implications by integrating information systems theories and education psychology to understand the factors influencing the virtual world learning continuance in a systematic way. The findings also provide educators empirical evidence on what influences users’ learning continuance in virtual worlds.

This study investigates the potential of artificial intelligence (AI) to revolutionize personalized learning by developing and empirically evaluating the Adaptive Personalization Theory of Learning (APT) model. The APT paradigm uses AI-powered personalized learning algorithms, real-time adaptive assessments, learner engagement strategies, cognitive scaffolding, and ethical safeguards to provide flexible, personalized learning experiences. The study confirms the model's constructs through qualitative methods such as case studies, interviews, and classroom observations. It illustrates how AI improves learning outcomes by continuously tailoring content and evaluations to individual learner needs. The findings show that AI-powered systems increase learner motivation, engagement, and knowledge retention while providing scalable solutions for various educational scenarios. However, the study also identifies ethical concerns, such as potential biases in AI algorithms, emphasizing the significance of establishing transparent, fair systems. Limitations include the scope of the implementation and the necessity for additional quantitative study. The paper continues by identifying areas for further research, emphasizing long-term impacts, ethical frameworks, and practical implementation tactics, and establishing the APT model as a significant contribution to AI in education.

The realm of nursery and primary education in Cameroon has witnessed the introduction of novel curricula aimed at fostering competencies among learners, particularly accentuating Science, Technology, Engineering, and Mathematics (STEM) education. This paradigm shift underscores the pressing demand for educator’s adept at navigating and fully leveraging these curricular frameworks to facilitate the acquisition of knowledge, skills, and attitudes requisite for the learners' holistic development. Concurrently, the enactment of the National Development Strategy 2020-2030 (NDS30), geared towards propelling Cameroon towards emergent status by 2035, serves as a resounding call for concerted efforts across all sectors, including education, to realise the stipulated objectives. Consequently, there is an urgent imperative for a cadre of high-calibre educators equipped with the specific knowledge, skills, and competencies necessary to yield the desired educational outcomes. Such educator proficiency can only be cultivated through rigorous professional programmes that address contemporary challenges and foster avenues for lifelong learning. It is well-established that higher levels of education are intricately linked with elevated pedagogic standards (Howes et al., 2003; Siraj-Blatchford, 2010). In alignment with this premise, the Faculty of Education at the University of Buea has devised a master's programme in nursery and primary education employing a blended learning model, encompassing both face-to-face and online learning modalities. Nonetheless, to provide truly tailored educational experiences catering to the diverse talents, interests, and needs of each learner, a personalised learning approach is indispensable. The creation of a personalised distance learning environment holds paramount significance, particularly in light of the burgeoning prominence and acceptance of personalised learning within higher education circles. This initiative furnishes a roadmap for transitioning from conventional face-to-face instruction to online learning through the lens of personalised learning principles.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0736/a.php" alt="Hit counter" /></p>

Flipped Learning (FL) inverts traditional teaching by delivering content online outside the classroom, allowing class time for interactive, hands-on activities. This approach enhances student engagement and understanding. The integration of AI in education has revolutionized traditional teaching methods. Enhancing FL with AI has led to the emergence of what was labeled in this research paper as Flipped Interactive Learning (FIL), which integrates AI-driven tools to support and enrich interactive, personalized learning experiences. This study aimed to examine the impact of the FIL instructional model based on Chat Generative Pre-Trained Transformer (ChatGPT) on students’ programming skills in an undergraduate course. The participants in this study were 74 students. This study used a semi-experimental research design; the control group consisted of 36 students, while the experimental group consisted of 38 students. The experimental group received instruction using the FIL method while the control group received instruction using the conventional FL method. The analysis of the collected data showed that using FIL-based ChatGPT contributed significantly to improving students’ programming skills. Based on the findings of this study, educational institutions should adopt the FIL instructional model in academic settings in general and programming courses in particular, as it significantly improves student learning outcomes compared to conventional FL methods. In addition, institutions should also evaluate the FIL model’s scalability and allocate necessary resources, such as technology infrastructure and support staff, for successful implementation. Further research should identify the most effective components of the FIL model to refine it for broader use. Moreover, continuous student feedback is essential for iterative improvements to meet their needs.

The primary objective of this research is to investigate the impact of ChatGPT on enriching personalized learning in senior teacherled instructional frameworks in the Philippines. through a qualitative approach involving semi-structured interviews with ten senior teachers from Mindanao city, Philippines, this study aims to delve into senior teachers’ perceptions and approaches towards the integration of ChatGPT. The results indicate a generally positive outlook among participants regarding ChatGPT’s capacity to offer customized feedback, adjust to diverse learning preferences, and promote student independence. Despite these benefits, apprehensions were expressed regarding ChatGPT’s cultural awareness, dependability, and the risk of diminishing human interaction. Participants stressed the significance of providing adequate training and assistance to teachers, incorporating ChatGPT into current methodologies, and tailoring it to suit the local educational environment. The strategies identified include personalized learning trajectories, responsive feedback systems, structured learning tasks, student-centered investigations, and reflective metacognition. This research underscores the transformative capacity of ChatGPT in personalized learning, while underscoring the importance of addressing cultural and operational obstacles, thereby presenting valuable insights for educators, policymakers, and stakeholders striving to enhance educational practices in technology-driven societies.

Measuring personalized learning through the Lens of UDL: Development and content validation of a student self-report instrument

This study explores the relationship between human agency and the use of sociotechnical AI technologies in educational contexts. While AI applications in education promise benefits such as personalised learning experiences and enhanced efficiency, empirical evidence supporting these claims remains limited, particularly regarding their implications for agency across different levels. Reflecting on these issues, this research critically evaluates how these technologies impact agency dynamics, namely on the subjective, intersubjective, and collective dimensions of agency as perceived by educators. The current study forms part of a broader Educational Design Research project, structured across multiple stages (including a Systematic Literature Review, a Delphi study, and focus groups with teachers). Drawing on this triangulated methodological architecture, the paper specifically focuses on the qualitative analysis of discussions about how AI systems may transform human agency across different levels. This methodology facilitates a comprehensive examination of both the opportunities and risks associated with the increasing integration of AI in educational settings. Insights gathered reveal that educators present a layered understanding of their students’ subjective agency but may overlook the broader intersubjective impacts of AI systems, potentially leading to narratives that envision a future where the role of teachers is diminished. While the immediate focus is on understanding educators’ perceptions of agency transformations through AI integration, the study also contributes to a wider. Objective informing the design of a professional development framework for teachers. In particular, the research aims to provide critical foundations for the development of pedagogical interventions that intentionally address agency within educational contexts where AI systems are increasingly present. education, providing meaningful substance for debates among educators, policymakers, and researchers.

With continuous advancements in education and the rapid proliferation of artificial intelligence technology, leveraging AI to improve university course evaluation and personalize learning experiences has garnered significant interest. This article aims to investigate the effective utilization of artificial intelligence technology in enhancing university course evaluation and achieving personalized learning outcomes. Firstly, it provides an overview of the current landscape of university course evaluation and personalized learning while highlighting the limitations of existing methodologies. It then delves into the application of artificial intelligence technology to address these challenges, discussing the utilization of machine learning algorithms for course evaluation, personalized recommendations, and student data analysis. In conclusion, the article summarizes the potential impact of artificial intelligence technology on university course evaluation and personalized learning.

The study aimed to assess the impact of media technology on the teaching and learning process of Islamic religious education within an independent curriculum. Through interviews, observations, and document analysis, the study found that integrating media technology positively influenced the teaching and learning experience. Multimedia presentations, educational websites, and digital applications enhanced students' engagement, motivation, and understanding of Islamic principles and teachings. Interactive and collaborative learning was facilitated, allowing students to participate actively in discussions and group activities. Media technology also provided opportunities for personalized learning, allowing students to explore Islamic concepts at their own pace and according to their learning styles. It fostered creativity and critical thinking skills by encouraging students to analyze and interpret religious texts through interactive media platforms. However, challenges such as limited access to technology resources, technical difficulties, and concerns about content authenticity were identified. The study emphasized the need for appropriate teacher training and support to integrate media technology into the curriculum effectively. In conclusion, the study demonstrated the effectiveness of media technology in Islamic Religious Education within an independent curriculum. It was integrating media tools enhanced engagement, motivation, and understanding among students while enabling interactive and personalized learning experiences. Addressing resources, technical issues, and content reliability challenges is essential for successful implementation. Keywords: Media Technology, Independent Curriculum, Interactive Learning

Given the great potential of integrating Computer-Assisted Language Learning (CALL) and Artificial Intelligence-Assisted Language Learning (AIALL) to enhance language learning outcomes, there is a growing interest in exploring their combined effects. In this vein, the present study aimed to develop and test an interactive pedagogical model of language learning (IPMLL) by integrating CALL and AIALL elements in a combined module. To further investigate the effects of this model, a comprehensive evaluation was conducted, considering various aspects such as learner motivation, personalized learning experiences, and feedback effectiveness. The results indicate that (1) the integration of CALL and AIALL in the IPMLL positively influenced learner motivation, leading to greater involvement and active participation in language learning activities; (2) the personal learning interactions facilitated by the IPMLL, including adaptive instruction and intelligent feedback, contributed to improved language proficiency and learner satisfaction. Theoretically, this integration aligns with established pedagogical theories and frameworks, such as cognitive theories of multimedia learning, emphasizing the significance of interactive and technology-enhanced learning environments. Pedagogically, the IPMLL offers practical implications for teachers, highlighting the benefits of incorporating CALL and AIALL elements in language teaching methodologies. This study contributes to the growing body of research on technology-enhanced language learning and provides insights for future developments in this field.

Objective: This study explores students' perceptions of using Personal Learning Environments (PLEs) in the era of generative artificial intelligence as a learning resource in higher education. The specific objectives are: 1) to determine the current tools that students incorporate into their PLEs and 2) to identify students' perceptions of the use of PLEs for lifelong learning. Theoretical Framework: UNESCO and other institutions recognize lifelong learning as an essential competency for personal and professional development. Personal Learning Environments (PLEs) are considered effective tools for promoting self-directed learning, allowing students to manage their learning processes, especially with the advent of generative artificial intelligence technologies. Method: A qualitative study was conducted with 207 first-semester engineering students from a public university in Peru. Participants were introduced to a training program to develop their PLEs using various digital tools, including generative AI tools such as ChatGPT DALL-E. The instrument used to evaluate students' perceptions was the MUSIC model (Jones, 2009), which focuses on empowerment and usefulness. Results: The results indicate that more than 80% of the students positively perceive PLEs, considering them valuable and empowering tools for achieving their learning objectives. Integrating generative AI tools into PLEs is notable, as it provides a dynamic and personalized learning experience. Most students found PLEs helpful for their future and relevant for promoting lifelong learning skills. Research Implications: The research suggests that PLEs, especially when integrated with generative AI tools, positively impact self-directed learning and promote collaborative and social learning. These findings can inform the development of educational programs and policies that encourage using PLEs as a student-centered learning strategy. Originality/Value: This study contributes to the understanding of the role of PLEs in the context of higher education and generative artificial intelligence. It is one of the first studies to examine students' perceptions of using PLEs with generative AI tools, highlighting their potential to enhance learning autonomy and personalization.

<p style="text-align: justify;"><span style="font-family: "times new roman";">In the context of big data, with the rapid development of cloud computing and mobile networks, smart education has opened up the era of digitization, networking and smart cloud. Smart learning, smart education, interactive online classrooms and personalized learning have become new trends in future education development. The era of big data has had a great impact on my country's education field. A variety of network technologies and smart classrooms have been widely used in the teaching process, realizing large-scale reforms of traditional education models. The realization of the smart education platform can provide students with a learning platform with a personalized learning experience and a good smart learning atmosphere, which greatly improves the traditional teaching and learning mode. This article first analyzes the key points of smart education and provides the design principles of smart math education platform. In order to promote smart education and smart learning, secondly, it designs and implements an open and shared interactive smart math education platform. Provide specific reference materials for researchers in smart service, smart management, smart evaluation, and smart education.</span>

The year 2030 marks a significant juncture in the evolution of education, where Generative Artificial Intelligence (AI) tools are poised to revolutionize the learning experience. In education society, the importance of generative AI is to improve the accessibility of learning at the global level so that personalized learning experiences can be provided to every learner as per their needs. This chapter explores the multifaceted role of generative AI tools in reshaping educational practices, envisioning a future where these tools foster personalized, adaptive, and engaging learning environments. Generative AI tools, characterized by their ability to create and adapt content autonomously, are instrumental in tailoring educational materials to individual learner needs. This chapter surveys the landscape of generative AI applications in education, including content generation, interactive simulations, intelligent tutoring systems, and dynamic learning pathways. These tools aim to provide adaptive, context-aware learning experiences that cater to diverse learning styles and preferences. The adaptability of generative AI tools extends to the creation of personalized learning pathways. By leveraging data analytics and machine learning algorithms, these tools dynamically adjust content delivery, pacing, and complexity, ensuring that each learner's educational journey is optimized for their unique requirements. The discussion encompasses the potential of generative AI tools to support both formal and informal learning settings. Generative AI tools also play a crucial role in promoting inclusivity in education. By generating diverse and culturally relevant content, these tools contribute to breaking down barriers and addressing disparities in access to quality education. This chapter explores how generative AI can be leveraged to create content that resonates with learners from different backgrounds, fostering a more inclusive educational landscape.