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Journal articles on the topic 'Personalized Content-Delivery and ContextAware Content-Delivery Framework'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Alaa, Edein M. Qoussini. "Personalized and Intelligent Agent Based Context-Aware Mobile Learning Framework." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 6 (2025): 31–40. https://doi.org/10.35940/ijrte.D7832.13060325.

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<strong>Abstract:</strong> Mobile learning promises the freedom to learn anytime, anywhere. However, effectively integrating personalization within mobile learning environments presents a significant challenge. This research aims to address this challenge by developing a novel framework for personalized content delivery in mobile learning environments (PCDMLE). The proposed PCDMLE framework leverages three key personalization aspects: student preferences, academic level, and assessment unit. By dynamically adapting learning content based on these individual characteristics, the framework aims

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Shivakumar, Shailesh, and Venkata Suresh Pachigolla. "MCWDF." International Journal of Information Technology and Web Engineering 13, no. 1 (2018): 1–19. http://dx.doi.org/10.4018/ijitwe.2018010101.

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Segregating the web page content into logical chunks is one of the popular techniques for modular organization of web page. While chunk-based approach works well for public web scenarios, in case of mobile-first personalization cases, chunking strategy would not be as effective for performance optimization due to dynamic nature of the Web content and due to the nature of content granularity. In this paper, the authors propose a novel framework Micro chunk based Web Delivery Framework which proposes and uses a novel concept of “micro chunk”. The micro chunk based Web Delivery framework aims to

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Qoussini, Alaa Edein M., and Shaima M. Al-Tabib. "Personalized and Intelligent Agent Based Context-Aware Mobile Learning Framework." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 6 (2025): 31–40. https://doi.org/10.35940/ijrte.d7832.13060325.

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Mobile learning promises the freedom to learn anytime, anywhere. However, effectively integrating personalization within mobile learning environments presents a significant challenge. This research aims to address this challenge by developing a novel framework for personalized content delivery in mobile learning environments (PCDMLE). The proposed PCDMLE framework leverages three key personalization aspects: student preferences, academic level, and assessment unit. By dynamically adapting learning content based on these individual characteristics, the framework aims to enhance student performa

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Hu, Jing, Yaling Lai, Ao Peng, Xuemin Hong, and Jianghong Shi. "Proactive Content Delivery with Service-Tier Awareness and User Demand Prediction." Electronics 8, no. 1 (2019): 50. http://dx.doi.org/10.3390/electronics8010050.

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Cost-effective delivery of massive data content is a pressing challenge facing modern mobile communication networks. In the literature, two primary approaches to tackle this challenge are service-tier differentiation and personalized proactive content caching. However, these two approaches have not been integrated and studied in a unified framework. This paper proposes an integrated proactive content delivery scheme that jointly exploits the availability of multiple service tiers and multi-user behavior prediction. Three optimal algorithms and one heuristic algorithm are introduced to solve th

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Nadila, Dewi, Mezan El-Khaeri Kesuma, Mala Pratiiwi, Yurika Witazora, and Khairun Nita Aulia. "The Impact of Artificial Intelligence on User Engagement and Content Delivery in Digital Media." KnE Social Sciences 10, no. 14 (2025): 620–35. https://doi.org/10.18502/kss.v10i14.19126.

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The advent of artificial intelligence (AI) is transforming the landscape of digital media platforms, offering unprecedented opportunities for enhancing user engagement and optimizing content delivery. This research aims to explore the transformative potential of AI in enhancing digital media platforms by creating more personalized and engaging content experiences for users. The research methodology is based on a comprehensive literature review, encompassing research aims and boundaries, search approaches, surveys, and the impact on the research framework. The article examines case studies of l

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Luo, Zhengrong, Jinyang Jiang, Huajie Zhou, and Jian Wu. "Enhancing Personalized Services in Tianfu Cultural Tourism: Pathways and Strategies Based on User Profiling." Philosophy and Social Science 1, no. 9 (2024): 86–93. https://doi.org/10.62381/p243915.

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Against the backdrop of a burgeoning global cultural tourism industry, Tianfu cultural tourism has emerged as a vital driver of economic growth in Sichuan, owing to its unique natural and cultural resources. To address the growing demand for personalized experiences among tourists, this study explores the application of user profiling technologies to enhance the delivery of personalized services in Tianfu cultural tourism. Leveraging big data and artificial intelligence, the study proposes a service optimization framework centered on data collection, profile analysis, and precise content deliv

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Danish, Fajer, Naeem Akhtar, and Muhammad Imran. "AI-Driven Personalization in Educational Marketing: A Framework for Enhancing Student Recruitment and Retention." Journal of Political Stability Archive 3, no. 2 (2025): 559–90. https://doi.org/10.63468/jpsa.3.2.33.

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Higher education universities require marketing knowledge to overcome growing student enrollment and retention issues in universities. This research examines how educational institutions perform in commercialization alongside studying AI customization effects on that area. In the study narrative literature review combined with secondary data analysis enabled researchers to create a conceptual framework which demonstrates how artificial intelligence tools should be applied for awareness, engagement, conversion, retention, and Advocacy phases. The study investigates three technological solutions

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Jin, Jennifer, and Mira Kim. "GPT-Empowered Personalized eLearning System for Programming Languages." Applied Sciences 13, no. 23 (2023): 12773. http://dx.doi.org/10.3390/app132312773.

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The eLearning approach to programming language instruction has gained widespread acceptance due to advantages such as accessibility, temporal flexibility, and content reusability. However, the current eLearning for programming predominantly employs the delivery of one-size-fits-all content, engendering elevated costs in both the development of language coursework and administration of eLearning sessions, which includes the labor-intensive task of grading student submissions. A compelling research question to consider is how to construct an eLearning system capable of delivering personalized, s

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Gonul, Suat, Tuncay Namli, Sasja Huisman, Gokce Banu Laleci Erturkmen, Ismail Hakki Toroslu, and Ahmet Cosar. "An expandable approach for design and personalization of digital, just-in-time adaptive interventions." Journal of the American Medical Informatics Association 26, no. 3 (2018): 198–210. http://dx.doi.org/10.1093/jamia/ocy160.

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AbstractObjectiveWe aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people’s individual needs, momentary contexts, and psychosocial variables.Materials and MethodsWe propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. T

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Surjadeep Dutta. "Leveraging AI Chatbots for Personalized Content Delivery: An Empirical Study in the Pharmaceutical Industry." Journal of Intelligent Communication 4, no. 1 (2025): 98–113. https://doi.org/10.54963/jic.v4i1.1265.

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This empirical study examines the strategic incorporation of AI-driven chatbots in the pharmaceutical sector for tailored content distribution, improved user engagement, and data-driven decision-making. The digital revolution is reshaping pharmaceutical marketing, with AI chatbots providing a scalable, round-the-clock solution for engaging patients, healthcare professionals, and consumers while ensuring compliance within a regulated framework. A quantitative methodology was employed to gather data from 120 participants within the Indian pharmaceutical industry. Findings from reliability analys

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Wainaina, Peter James, Dr Henry Nkoru Nabea, and Prof Kyalo Wa Ngula. "Citizen TV’s Viusasa: A Tool for Encouraging Local Content Creators." International Journal of Research and Innovation in Social Science VIII, no. VIII (2024): 2313–25. http://dx.doi.org/10.47772/ijriss.2024.8080174.

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Video-on-Demand (VoD) platforms have revolutionized media consumption globally, offering personalized content experiences and challenging traditional broadcasting norms. This paper investigates the role of Citizen TV’s Viusasa in promoting local content within Kenya’s dynamic media landscape and evaluates the effectiveness of its personalized content delivery. Through a comprehensive analysis of user feedback, scholarly perspectives, and industry insights, the study examines how Viusasa leverages VoD technology and personalized recommendation algorithms to enhance user engagement and satisfact

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Satmunee, Jakkapan, Suwut Tumthong, and Supatcha Tupsup. "Study and analysis of the need for an intelligent lesson content management system for undergraduate digital technology education." International Journal of Innovative Research and Scientific Studies 8, no. 2 (2025): 1875–83. https://doi.org/10.53894/ijirss.v8i2.5557.

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This research focuses on the development and implementation of an intelligent learning content management system (AI-LMS) designed for undergraduate digital technology education, aiming to meet user needs and improve personalized learning experiences. A mixed-method approach was used, involving the collection of data from a purposefully selected sample of students, teachers, and experts. Statistical analysis, including calculation of means and standard deviations, was used to assess system requirements and user expectations. The study identified 12 main elements for an effective AI-LMS, includ

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Ebrahimian, Zahra, Nima Yaqmuri, and Mohammad Ali Akhaee. "Revolutionizing Short Video Recommendations Using the Vision Mamba Framework." Journal of Artificial Intelligence, Applications, and Innovations 1, no. 1 (2024): 1–11. https://doi.org/10.61838/jaiai.1.1.1.

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The rapid proliferation of short-form video content on platforms such as TikTok, Instagram, and YouTube Shorts has introduced significant challenges for recommendation systems, as traditional methods often struggle to keep up with the dynamic nature of user engagement and the large influx of data. In this paper, we present the Vision Mamba (Vim) framework, a cutting-edge approach in visual representation learning that employs bidirectional state space models to improve both the efficiency and accuracy of short video recommendations. The Vim framework excels by effectively capturing temporal dy

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Chow, James C. L., and Kay Li. "Developing Effective Frameworks for Large Language Model–Based Medical Chatbots: Insights From Radiotherapy Education With ChatGPT." JMIR Cancer 11 (February 18, 2025): e66633. https://doi.org/10.2196/66633.

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This Viewpoint proposes a robust framework for developing a medical chatbot dedicated to radiotherapy education, emphasizing accuracy, reliability, privacy, ethics, and future innovations. By analyzing existing research, the framework evaluates chatbot performance and identifies challenges such as content accuracy, bias, and system integration. The findings highlight opportunities for advancements in natural language processing, personalized learning, and immersive technologies. When designed with a focus on ethical standards and reliability, large language model–based chatbots could significa

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Zhang, Xun. "Optimizing AIGC-Driven Visual Communication: A Deep Learning Framework for Adaptive Image Generation and User-Centered Content Scheduling." Applied and Computational Engineering 163, no. 1 (2025): 21–27. https://doi.org/10.54254/2755-2721/2025.23788.

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The rapid development of AIGC technology has opened a new avenue for personalized visual communication. This study builds a deep learning framework that integrates adaptive image generation and user-driven content arrangement, aiming to improve the expressiveness and delivery efficiency of visual creation. The technical architecture consists of two modules: the image generation engine based on Stable Delivery and the content scheduling system driven by the GRU algorithm. The two modules are connected by a feedback loop and can dynamically optimize image quality and push timing based on user in

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Debangan Mandal, Mohammad Aasim, Piyush Dirg, Rajashri Biswas, Rutvik Ramteke, and Manpreet Kaur. "Beyond the Hype : Building a Personalized Movie Experience with Content-Based Recommendation." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 3 (2024): 28–39. http://dx.doi.org/10.32628/cseit24102129.

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This review paper provides a detailed analysis of how a movie recommendation system based on content was planned, executed and evaluated using Streamlit framework. The exponential expansion of digital content has led to the need for efficient recommendation systems, particularly in the realm of movies. In this respect, the proposed recommender system utilizes cosine similarity computations and content-based filtering methods to offer personalized film suggestions relying upon various features such as genres, keywords, castings as well as crews. The author further illustrates rigorous data prep

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Brandon Fangmbeng Atonte. "Personalized AI-driven education: An AI framework for enhanced learning outcomes among school-age children." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 2326–27. https://doi.org/10.30574/wjaets.2025.15.1.0453.

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This paper presents Tutor AI, an educational platform that integrates AI-driven personalization with evidence-based pedagogical approaches for learners aged 3-17. The system features customizable 3D pedagogical agents, adaptive content delivery, and dynamic educational visualizations. Our implementation targets documented academic challenges in mathematics, reading, and science while addressing educational equity concerns. The paper outlines the theoretical framework, technical architecture, and projected impacts of the system. Based on meta-analytic evidence from comparable interventions, we

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Guo, Ruina, Arina Anis Azlan, and Emma Mohamad. "Mechanisms of short video selection behavior in elderly hypertensives under health information overload: a cognitive load theory." Future Technology 4, no. 3 (2025): 107–18. https://doi.org/10.55670/fpll.futech.4.3.11.

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The proliferation of digital health information through short video platforms creates cognitive overload challenges for elderly hypertensive patients managing chronic conditions, compromising effective health information processing and decision-making capabilities. This research investigates the mechanisms of short video selection behavior among elderly hypertensive patients under health information overload, employing cognitive load theory integrated with artificial intelligence analytics to optimize content delivery strategies. A mixed-methods design involving 128 elderly participants (mean

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Hu, Jingan, and Gaimin Jin. "An Intelligent Framework for English Teaching through Deep Learning and Reinforcement Learning with Interactive Mobile Technology." International Journal of Interactive Mobile Technologies (iJIM) 18, no. 09 (2024): 74–87. http://dx.doi.org/10.3991/ijim.v18i09.49289.

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As globalization deepens, the significance of English teaching in the educational landscape has become more prominent. Traditional teaching methods are increasingly inadequate for providing personalized and efficient learning experiences. This gap is being addressed by the rapid advancements in artificial intelligence, especially through deep and reinforcement learning. These technologies provide a framework for intelligent English teaching systems by mimicking human learning processes to customize personalized learning experiences, optimize learning paths, and enhance efficiency. However, cha

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Omar, Zoel-Fazlee, Mior Harris Mior Harun, Nor Irvoni Mohd Ishar, Nur Arfah Mustapha, and Zurina Ismail. "Enhancing professional development and training through AI for personalized learning: a framework to engaging learners." International Journal on e-Learning and Higher Education 19, no. 3 (2023): 115–38. http://dx.doi.org/10.24191/ijelhe.v19n3.1937.

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This paper explores the transformative potential of AI-driven personalized learning in enhancing professional development and training programs. As the workforce landscape rapidly changes, the demand for tailored and engaging learning experiences has become increasingly evident. This paper presents a comprehensive framework that leverages artificial intelligence (AI) to determine suitable learning theories and strategies for individual learners, thus promoting higher learner engagement and skill acquisition. Through analysing learner data, preferences, and performance, AI algorithms enable the

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Lolade Hamzat, Dare Abiodun, and Abidemi Joseph. "Empowering entrepreneurial growth through data-driven financial literacy, market research, and personalized education tool." World Journal of Advanced Research and Reviews 19, no. 2 (2023): 1692–711. https://doi.org/10.30574/wjarr.2023.19.2.1568.

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Entrepreneurship plays a critical role in driving economic growth, innovation, and job creation, particularly in emerging economies with large informal sectors. However, many aspiring and existing entrepreneurs struggle to scale their businesses due to limited financial literacy, poor access to reliable market information, and a lack of tailored educational resources. In regions like Nigeria, where micro and small enterprises dominate, overcoming these barriers is essential to achieving sustainable development and inclusive economic transformation. This paper presents an integrated framework f

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Rashitovna, Kayumova Nazokat. "Developing A Functional Model for Effective Education Based on Adaptive Content, Personalization, Inclusive Interface, And Feedback Mechanisms." European International Journal of Pedagogics 5, no. 4 (2025): 62–67. https://doi.org/10.55640/eijp-05-04-15.

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This article presents the development of a functional model that integrates adaptive content, personalization, inclusive interface, and feedback mechanisms to ensure effective education within the framework of digital pedagogy. The main goal of the study is to create a learner-centered digital learning environment that considers the needs, abilities, and learning conditions of each student. The model is based on the principles of Universal Design for Learning (UDL), Bloom's taxonomy, metacognitive approaches, and the capabilities of artificial intelligence (AI). It integrates modular and diffe

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Khandpur, Neha, Laura Y. Zatz, Sara N. Bleich, et al. "Supermarkets in Cyberspace: A Conceptual Framework to Capture the Influence of Online Food Retail Environments on Consumer Behavior." International Journal of Environmental Research and Public Health 17, no. 22 (2020): 8639. http://dx.doi.org/10.3390/ijerph17228639.

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The rapid increase in online shopping and the extension of online food purchase and delivery services to federal nutrition program participants highlight the need for a conceptual framework capturing the influence of online food retail environments on consumer behaviors. This study aims to develop such a conceptual framework. To achieve this, mixed methods were used, including: (1) a literature review and development of an initial framework; (2) key informant interviews; (3) pilot testing and refinement of the draft framework; and (4) a group discussion with experts to establish content validi

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Afolabi I.Y, Ugah J.O, Igwe J.S, and Nwali M.E. "DEVELOPMENT OF AN AI-DRIVEN ADAPTIVE LEARNING MANAGEMENT SYSTEM USING DATA ANALYTICS." International Journal of Scientific Research in Modern Science and Technology 4, no. 7 (2025): 52–63. https://doi.org/10.59828/ijsrmst.v4i7.349.

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This study presents the development of an AI-driven Adaptive Learning Management System (LMS) that delivers personalized learning experiences using data analytics and natural language processing. The system addresses limitations of traditional LMS platforms by dynamically adapting content based on student performance in real-time. Designed with Object-Oriented Methodology and the CRISP-DM data analytics model, the system integrates Python, web technologies, and a BERT (Bidirectional Encoder Representations from Transformers)-based semantic engine. When students fail a quiz, the BERT model iden

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Vlahova-Takova, Maria, and Milena Lazarova. "A Recommender System Model for Presentation Advisor Application Based on Multi-Tower Neural Network and Utility-Based Scoring." Electronics 14, no. 13 (2025): 2528. https://doi.org/10.3390/electronics14132528.

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Delivering compelling presentations is a critical skill across academic, professional, and public domains—yet many presenters struggle with structuring content, maintaining visual consistency, and engaging their audience effectively. Existing tools offer isolated support for design or delivery but fail to promote long-term skill development. This paper presents a novel intelligent application, the Presentation Advisor application, powered by a personalized recommendation engine that goes beyond fixing slide content and visualization, enabling users to build presentation competence. The recomme

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Wang, Liqun, Wei Han, and Zhimin Xi. "Exploring Semantic Web Tools in Education to Boost Learning and Improve Organizational Efficiency." International Journal on Semantic Web and Information Systems 21, no. 1 (2025): 1–27. https://doi.org/10.4018/ijswis.370315.

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Semantic Web technologies in education provide opportunities to improve learning outcomes and organizational efficiency via organized and significant information representation. However, with progress in educational technology, there is an absence of frameworks using Semantic Web capabilities to provide tailored learning experiences and enhance administrative procedures efficiently. Hence, this study proposes a framework called “Education using Semantic Web Tools (E-SWT),” employing ontologies, metadata tagging, and intelligent reasoning systems. The method supports seamless platform interoper

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Ji, Zefang. "Analysis of Bilibili Company's Business Strategy." Transactions on Social Science, Education and Humanities Research 11 (August 20, 2024): 426–33. http://dx.doi.org/10.62051/sw0wat87.

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This paper presents a detailed examination of Bilibili Inc.'s strategic framework, positioning the company as a formidable player in China's digital entertainment sector. Bilibili has carved out a niche in the competitive landscape through its innovative approaches to community engagement, content diversification, strategic alliances, monetization strategies, and technological investments. The core of Bilibili's success lies in its community-centric model, which prioritizes user-generated content (UGC) and interactive features, creating a highly engaged user base. This is complemented by the p

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Vijya Tulsani. "Real-Time Facial Emotion Recognition for AI-Enhanced Personalized Learning." Journal of Information Systems Engineering and Management 10, no. 40s (2025): 427–36. https://doi.org/10.52783/jisem.v10i40s.7313.

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Introduction: The integration of emotional intelligence into digital learning platforms has emerged as a significant avenue for enhancing learner engagement and academic performance. This research presents a Real-Time Facial Emotion Recognition (FER) framework specifically designed to support AI-enhanced personalized learning environments. The proposed study identifies key research gaps in the existing FER-based learning systems, including limited real-time applicability, lack of contextual adaptability, and insufficient accuracy in dynamic educational settings. To address these gaps, we devel

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Saputri, Arliani, Indrian Satria, Julia Nofiani, and Rania Zulfi Fajriyah. "Membangun Silabus Yang Kreatif : Menjawab Kebutuhan Belajar Secara Personal dan Fleksibel." Future Academia : The Journal of Multidisciplinary Research on Scientific and Advanced 3, no. 2 (2025): 926–35. https://doi.org/10.61579/future.v3i2.500.

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This article explores the development of a creative syllabus designed to address the growing need for personalized and flexible learning. In an educational landscape that increasingly values individual student needs and diverse learning styles, traditional, rigid syllabi often fall short. This paper argues for an innovative approach to syllabus construction that moves beyond static content delivery towards a dynamic framework. Key elements discussed include incorporating student choice, leveraging adaptive technologies, fostering interdisciplinary connections, and emphasizing project-based lea

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Onditi, Sharon, Jeremiah Onunga, and Steve Oloo. "Enhancing Teachers' Content Delivery to Children with Disabilities Using ICT-Based Technologies to Promote Inclusive Early Learning in Kenya." International Journal of Research and Scientific Innovation XII, no. V (2025): 1594–601. https://doi.org/10.51244/ijrsi.2025.120500152.

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Inclusive education is essential for ensuring equitable learning opportunities for all children, including those with disabilities. However, in Kenya, teachers often struggle to deliver content effectively to learners with diverse needs due to limited resources and inadequate training. This study explores the potential of Information and Communication Technology (ICT)-based technologies to enhance teachers' content delivery and promote inclusive early learning. Using a mixed-methods approach, the research examines barriers to inclusive education in Kenyan early childhood settings and evaluates

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Muniasamy, Anandhavalli, and Areej Alasiry. "Deep Learning: The Impact on Future eLearning." International Journal of Emerging Technologies in Learning (iJET) 15, no. 01 (2020): 188. http://dx.doi.org/10.3991/ijet.v15i01.11435.

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eLearning as technology becomes more affordable in higher education but having a big barrier in the cost of developing its resources. Deep learning using artificial intelligence continues to become more and more popular and having impacts on many areas of eLearning. It offers online learners of the future with intuitive algorithms and automated delivery of eLearning content through modern LMS platforms. This paper aims to survey various applications of deep learning approaches for developing the resources of the eLearning platform, in which predictions, algorithms, and analytics come together

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Harpreet Paramjeet Singh. "The Transformative Potential of AI in Educational Settings: From Personalized Learning to Global Access." Journal of Computer Science and Technology Studies 7, no. 4 (2025): 507–14. https://doi.org/10.32996/jcsts.2025.7.4.59.

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Artificial intelligence continues to reshape contemporary educational systems, offering both transformative opportunities and inherent challenges. The integration of AI technologies has facilitated unprecedented personalization of learning experiences, enabling adaptive content delivery that responds to individual student needs while simultaneously providing real-time feedback and support. Beyond the classroom, AI systems have demonstrated capacity to streamline administrative workflows, generate actionable insights through learning analytics, and democratize access to quality educational reso

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Aqilah Mohd Sapri, Nur Areena, Asma Nadia Zanol Rashid, and Wan Ainol Mursyida Ahmad Tarmizi. "Leveraging AI for Effective Content Marketing in Libraries: Maximizing User Engagement." International Journal of Research and Innovation in Social Science VIII, no. IX (2024): 3510–21. http://dx.doi.org/10.47772/ijriss.2024.8090293.

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This paper examines the transformative potential of artificial intelligence (AI) in enhancing content marketing strategies within libraries, with a specific focus on the Malaysian context. As libraries adapt to the digital preferences of a younger, tech-savvy population, AI has become essential for improving user engagement and service delivery. In Malaysia, over 75% of libraries are exploring or implementing AI technologies, driven by initiatives like My DIGITAL and the growing demand for digital content. AI tools, such as personalized recommendation systems, have demonstrated a 45% increase

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Ding, Zixi. "The Synergy of AI and VR for Personalized Learning." Lecture Notes in Education Psychology and Public Media 93, no. 1 (2025): 120–25. https://doi.org/10.54254/2753-7048/2025.bo25217.

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In the digital age, traditional education methods are increasingly insufficient in meeting diverse learning needs. The integration of Artificial Intelligence (AI) and Virtual Reality (VR) presents a paradigm-shifting potential to personalize and enhance the educational experience. This paper investigates how AI-enhanced VR can overcome the limitations of conventional VRparticularly its non-adaptive, standardized delivery modeby adapting learning content in real time based on individual student performance and engagement. Through a synthesis of recent studies, case analyses, and theoretical fra

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Ahamad, Shahbaz, Noorishta Hashmi, and Ehtesham Hussain. "SENTIMENT-GUIDED MULTIMODAL CONTENT RECOMMENDATION USING GRU AND CNN-LSTM ARCHITECTURES." Journal of Dynamics and Control 9, no. 5 (2025): 46–56. https://doi.org/10.71058/jodac.v9i5005.

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In the era of social commerce, recommendation systems must transcend traditional paradigms by incorporating multimodal data to personalize content more effectively. This study proposes a sentiment-guided, multimodal recommendation framework that integrates text and image-based features to enhance product recommendation strategies, particularly in influencer marketing and fashion retail. The proposed architecture utilizes Gated Recurrent Units (GRUs) for analyzing user-generated textual comments to capture emotional tone and sentiment directionality. Concurrently, a hybrid Convolutional Neural

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Ahamad, Shahbaz, Noorishta Hashmi, and Ehtesham Hussain. "SENTIMENT-GUIDED MULTIMODAL CONTENT RECOMMENDATION USING GRU AND CNN-LSTM ARCHITECTURES." Journal of Dynamics and Control 9, no. 5 (2025): 182–92. https://doi.org/10.71058/jodac.v9i5015.

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In the era of social commerce, recommendation systems must transcend traditional paradigms by incorporating multimodal data to personalize content more effectively. This study proposes a sentiment-guided, multimodal recommendation framework that integrates text and image-based features to enhance product recommendation strategies, particularly in influencer marketing and fashion retail. The proposed architecture utilizes Gated Recurrent Units (GRUs) for analyzing user-generated textual comments to capture emotional tone and sentiment directionality. Concurrently, a hybrid Convolutional Neural

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Singh, Bismanjeet, and Dr Ajay Kumar Pathania. "AI-Driven Content Creation and Curation in Digital Marketing Education: Tools and Techniques." International Journal of Engineering Science and Humanities 14, Special Issue 1 (2024): 14–26. http://dx.doi.org/10.62904/8fbh3144.

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This research paper investigates the utilization of artificial intelligence (AI) technologies for content creation and curation within the realm of digital marketing education and its subsequent impact on student engagement and learning outcomes. As digital marketing continues to evolve rapidly, educators are increasingly turning to AI-driven tools to tailor content delivery to the individual needs and preferences of students. Drawing upon a comprehensive review of literature spanning AI, education, and digital marketing, this study establishes a theoretical framework to elucidate the mechanis

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Arunraju Chinnaraju. "AI-powered consumer segmentation and targeting: A theoretical framework for precision marketing by autonomous (Agentic) AI." International Journal of Science and Research Archive 14, no. 2 (2025): 401–24. https://doi.org/10.30574/ijsra.2025.14.2.0370.

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Consumer segmentation and targeting are essential for precision marketing, allowing businesses to deliver personalized experiences. The article explores the transformative role of autonomous AI agents in enhancing consumer segmentation and targeting within the data-driven marketing landscape. The proposed framework integrates machine learning (ML), natural language processing (NLP), and predictive analytics to continuously optimize segmentation models, enabling real-time targeting and hyper-personalization without human oversight. Autonomous agents dynamically manage segmentation by leveraging

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Ashawa, Moses, Joshua David Bryan, and Nsikak Owoh. "Personality Prediction Model: An Enhanced Machine Learning Approach." Electronics 14, no. 13 (2025): 2558. https://doi.org/10.3390/electronics14132558.

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In today’s digital era, social media platforms like Instagram have become deeply embedded in daily life, generating billions of content items each day. This vast stream of publicly accessible data presents a unique opportunity for researchers to gain insights into human behaviour and personality. However, leveraging such unstructured and highly variable data for psychological analysis introduces significant challenges, including data sparsity, noise, and ethical considerations around privacy. This study addresses these challenges by exploring the potential of machine learning to infer personal

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Saragih, Rijois Iboy Erwin. "AI-Powered Education: Transforming Learning through Personalized and Scalable Solutions." International Journal of Information System and Innovative Technology 3, no. 2 (2024): 1–9. https://doi.org/10.63322/qm9dk118.

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The rapid evolution of Artificial Intelligence (AI) has profoundly influenced various sectors, with education emerging as a pivotal area of transformation. The integration of AI into educational systems is redefining teaching methodologies, learning experiences, and administrative efficiencies. However, this intersection of AI and education faces significant challenges, including disparities in access, ethical concerns, and the lack of standardized frameworks for implementation. To address these challenges, this paper proposes a comprehensive AI-powered educational framework designed to person

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Malek, Manuela, Julia Nitsche, Claudia Dinand, et al. "Interprofessional Needs Analysis and User-Centred Prototype Evaluation as a Foundation for Building Individualized Digital Education in Dementia Healthcare Supported by Artificial Intelligence: A Study Protocol." Healthcare 11, no. 10 (2023): 1508. http://dx.doi.org/10.3390/healthcare11101508.

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Continuous profession-specific training is necessary to provide high-quality care for people with dementia. Research shows the need for more educational programmes that are personalized and responsive to the learning needs and preferences of staff. Digital solutions supported by artificial intelligence (AI) may be a means of making these improvements. There is a lack of formats that support learners in selecting the right content according to their learning needs and preferences. The project “My INdividual Digital EDucation.RUHR” (MINDED.RUHR) addresses this problem and seeks to develop an aut

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Welch Bacon, Cailee E., Sara L. Nottingham, and Tricia M. Kasamatsu. "Development and Validation of an Active Educational Resource to Address Quality Gaps Regarding Clinical Documentation." Athletic Training Education Journal 19, no. 1 (2023): 1–9. http://dx.doi.org/10.4085/1062-6050-015.23.

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Context Athletic trainers (ATs) have self-reported inadequate documentation of patient encounters and a desire for more educational resources to inform their documentation practices. However, continuing education opportunities regarding clinical documentation are limited and not easily accessible by all ATs. Objective To develop and validate a comprehensive educational resource on clinical documentation using an established personalized learning pathway (PLP) framework. Design Multiphase development process consisting of consensus development, subject matter expert content validation, and pilo

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Youm, Sungkwan, and Taeyoon Kim. "Enhancing Federated Intrusion Detection with Class-Specific Dynamic Sampling." Applied Sciences 15, no. 9 (2025): 5067. https://doi.org/10.3390/app15095067.

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Federated Learning (FL) presents a promising approach for collaborative intrusion detection while preserving data privacy. However, current FL frameworks face challenges with non-independent and identically distributed (non-IID) data and class imbalances in network security contexts. This paper introduces Dynamic Sampling-FedIDS (DS-FedIDS), a novel framework that enhances federated intrusion detection through adaptive sampling and personalization. DS-FedIDS extends the Federated Learning with Personalization Layers (FedPer) architecture by incorporating dynamic up/down sampling to address cla

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Siddharth Gupta. "Scaling and Optimizing Consumer Tech Products with Multi-Armed Bandit Algorithms: Applications in eCommerce." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 275–86. https://doi.org/10.32628/cseit251112370.

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This article explores the application of Multi-Armed Bandit (MAB) algorithms in optimizing consumer tech products, with a particular focus on eCommerce platforms. It provides a comprehensive article overview of the theoretical framework behind MAB algorithms, including the exploration-exploitation trade-off and comparisons with traditional A/B testing methods. The article delves into various MAB strategies commonly used in eCommerce, such as epsilon-greedy, Upper Confidence Bound (UCB), and Thompson Sampling, and examines their applications in personalized product recommendations, dynamic pric

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Indies, Yasser, and Leonard Dharmawan. "Strategi Komunikasi Program Jurnal Adhyaksa dalam Edukasi Hukum Publik: Analisis Teori Multimedia Kognitif di Kanal YouTube." Jurnal Indonesia : Manajemen Informatika dan Komunikasi 6, no. 2 (2025): 1260–72. https://doi.org/10.63447/jimik.v6i2.1431.

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The Jurnal Adhyaksa program is a public legal education initiative developed by the Attorney General’s Office of the Republic of Indonesia through its YouTube channel. This study aims to analyze the communication strategy applied in the program by referring to communication strategy theory and the Cognitive Theory of Multimedia Learning. A descriptive qualitative approach was employed, utilizing observation of twelve episodes, in-depth interviews, and document analysis. The data were analyzed using the SWOT framework to evaluate the strengths, weaknesses, opportunities, and threats of the comm

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Kumar, Periketi Shashi. "Movie Review Sentiment Analysis and AI-Story Generation Web Application." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 310–16. http://dx.doi.org/10.22214/ijraset.2024.58826.

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Abstract: This paper presents a web application designed to enhance the interaction between users and multimedia content through two innovative platforms: "MOVIE REVIEW SENTIMENT ANALYSIS" and "AI-STORY GENERATION." The first platform leverages the Fastai library and LSTM networks to perform sentiment analysis on user-submitted movie reviews, determining their polarity as positive or negative. This analysis directly influences the movie's aggregate rating, with the system capable of processing extensive textual input with high accuracy. Ratings are dynamically updated and stored in JSON format

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Torres, Vanessa N., Emily C. Williams, Rachel M. Ceballos, Dennis M. Donovan, and India J. Ornelas. "Participant Satisfaction and Acceptability of a Culturally Adapted Brief Intervention to Reduce Unhealthy Alcohol Use Among Latino Immigrant Men." American Journal of Men's Health 14, no. 3 (2020): 155798832092565. http://dx.doi.org/10.1177/1557988320925652.

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Latino immigrant men are at increased risk for unhealthy alcohol use, yet few interventions have been designed to meet their unique needs. The current study assessed participant satisfaction and acceptability of a culturally adapted brief intervention to reduce unhealthy alcohol use in this population. Adaptations to the brief intervention included delivering it in Spanish by promotores in a community setting. The mixed methods approach included surveys ( N = 73) and in-depth interviews ( N = 20) with participants in a pilot randomized controlled trial. The study drew on Sekhon’s theoretical f

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Mahesh, Mokale. "Privacy-First Architectures for Cross-Border Media Content Distribution." International Journal of Leading Research Publication 4, no. 6 (2023): 1–11. https://doi.org/10.5281/zenodo.15050964.

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With the rapid globalization of media content distribution, ensuring user privacy while adhering to international regulations is a critical challenge. Cross-border media distribution involves handling user data across multiple jurisdictions, each with its own privacy regulations and compliance requirements. Traditional centralized content distribution models often compromise user privacy due to excessive data collection, exposure to security vulnerabilities, and reliance on third-party intermediaries. As digital media consumption grows, the need for privacy-focused solutions becomes more urgen

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Lei, Chengyao. "Research on the Influence of Digital Transformation within the Tourism Sector on Location-Based Services." Advances in Economics, Management and Political Sciences 147, no. 1 (2025): 10–22. https://doi.org/10.54254/2754-1169/2024.ga19119.

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With the advent of globalization, tourism, as a pivotal service industry, is assuming an increasingly pivotal role in market competition. Digital transformation has emerged as the predominant trend in tourism development. This study conducts an extensive analysis of the impact of digital transformation on location-based services (LBS) within the tourism industry. Drawing upon an extensive review of the nature and characteristics of tourism digitization, this paper delves into the trajectory of development, core functions, and current applications of LBS technology in the tourism sector. Specif

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Kamini Murugaboopathy. "Leveraging Cloud Computing for Real-Time Marketing Analytics: A Technical Perspective." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1229–43. https://doi.org/10.32628/cseit25112450.

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This article examines how cloud computing has revolutionized marketing analytics by enabling real-time data processing and decision-making capabilities previously unattainable with traditional on-premise systems. It presents a comprehensive technical analysis of cloud-native architectures for marketing analytics, detailing the multi-layered framework that spans from data ingestion through to action delivery. The article explores how organizations have overcome the historical limitations of traditional analytics environments—including data silos, batch processing constraints, and limited comput

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