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Journal articles on the topic 'AI-driven personalized learning'

Author: Grafiati

Published: 7 June 2025

Last updated: 17 July 2025

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1

Elazab, Mohamed. "AI-driven personalized learning." International Journal of Internet Education 22, no. 3 (2024): 6–19. http://dx.doi.org/10.21608/ijie.2024.350579.

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Doshi, Mosam, Nishita Modh, Aadesh Borse, et al. "AI-Driven Zero-Shot Learning for Personalized Student Course Recommendations." International Journal of Research Publication and Reviews 6, no. 4 (2025): 5698–703. https://doi.org/10.55248/gengpi.6.0425.14113.

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Thajchayapong, Ploy, and Ashok K. Goel. "Personalized Learning through AI-Driven Data Pipeline." Proceedings of the AAAI Symposium Series 5, no. 1 (2025): 111–14. https://doi.org/10.1609/aaaiss.v5i1.35572.

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The integration of artificial intelligence (AI) in education holds significant promise for transforming personalized learning. By analyzing student learning data, AI systems can adapt instruction to meet individual needs through tailored content, adaptive learning paths, real-time feedback, and continuous improvement loops. However, effective personalization at scale demands not only access to large volumes of learner data but also robust data architectures to collect, organize, standardize, and analyze that data in a secure and meaningful way. However, note that the ability of AI to personali

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Jian, Maher Joe Khan Omar. "Personalized learning through AI." Advances in Engineering Innovation 5, no. 1 (2023): None. http://dx.doi.org/10.54254/2977-3903/5/2023039.

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The realm of education is witnessing a transformative integration with Artificial Intelligence (AI), poised to redefine the contours of pedagogical strategies. Central to this transformation is the emergence of personalized learning experiences, where AI endeavors to tailor educational content and interactions to resonate with individual learners' unique needs, preferences, and pace. This paper delves into the multifaceted dimensions of AI-driven personalized learning, from its potential to enhance e-learning modules, the advent of AI-powered virtual tutors, to the ethical challenges it surfac

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Shaumiwaty, Shaumiwaty, Mochamad Heru Riza Chakim, Heni Nurhaeni, and Victorianda. "Enhancing Personalized Learning Using Artificial Intelligence and Machine Learning Approaches." Blockchain Frontier Technology 4, no. 2 (2025): 156–70. https://doi.org/10.34306/bfront.v4i2.715.

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The convergence of artificial intelligence (AI) and machine learning (ML) technologies has revolutionized the education landscape, shifting paradigms toward individualized and optimized learning environments. By harnessing AI predictive power and ML adaptive capabilities, educational outcomes are enhanced while equipping teachers with data driven insights for informed decision making. The primary objective of this research is to explore how customized learning environments, ML models, performance measurement, and AI algorithms improve educational outcomes and learning experiences. Despite the

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Syed, Rizwan Ali, Zunain Uddin Shaikh, Faraz Muhammad, and Bin Shuja Talha. "AI-Driven Personalized Learning in Entrepreneurship Education." Academy of Education and Social Sciences Review 5, no. 1 (2025): 88–103. https://doi.org/10.5281/zenodo.15007337.

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This study investigates the impact of AI-driven personalized learning on entrepreneurship education outcomes, highlighting the mediating role of experience and engagement and the moderating influence of contextual factors. The study used a quantitative research design and a cross-sectional survey of 100 students in entrepreneurship programs. The study confirms that AI-driven learning significantly enhances entrepreneurial knowledge and skills. It also demonstrates that engagement mediates the relationship between personalized learning and educational outcomes, emphasizing the importance of tai

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Oladele Jegede, Olusegun. "Artificial Intelligence and English Language Learning: Exploring the Roles of AI-Driven Tools in Personalizing Learning and Providing Instant Feedback." Universal Library of Languages and Literatures 01, no. 02 (2024): 06–19. http://dx.doi.org/10.70315/uloap.ullli.2024.0102002.

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This study investigated the impact of AI-driven tools on English language learning, motivated by the increasing integration of artificial intelligence in education and the need for empirical evidence on its effectiveness. The purpose of the study was to explore how AI-driven tools personalize learning, provide instant feedback, and affect learner perceptions. Using a quantitative research design, data were collected from 200 students across four international schools via questionnaires. Three major findings emerged: AI-driven tools significantly enhanced personalized learning experiences, with

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Singh, Ajit Pal, Rahul Saxena, and Suyash Saxena. "The Future of Learning: AI-Driven Personalized Education." Asian Journal of Current Research 9, no. 4 (2024): 207–26. https://doi.org/10.56557/ajocr/2024/v9i49018.

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Virtual assistants are rapidly transforming the educational landscape. These AI-driven tools offer personalized support, enhancing student engagement and improving learning outcomes. Virtual assistants can answer questions instantly, clarify complex concepts, and create tailored study plans. By automating routine tasks and providing on-demand assistance, they free up educators to concentrate on more advanced instructional activities. Moreover, virtual assistants can analyze student performance data to pinpoint areas needing improvement and suggest targeted interventions. This data-driven appro

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Ojha, Dharma Raj. "AI-Driven Personalized Learning Systems for Gen Alpha and Beta: Opportunities and Challenges." American Journal of Innovation in Science and Engineering 4, no. 2 (2025): 17–22. https://doi.org/10.54536/ajise.v4i2.4644.

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This research paper explores the potential of AI-driven personalized learning systems for Generation Alpha (born 2010-2024) and Generation Beta (born 2025 onwards). As these digital natives enter educational institutions, there is a growing need for innovative learning approaches that cater to their unique characteristics and expectations. This study examines the opportunities and challenges associated with implementing AI-powered personalized learning systems for these generations. Through a comprehensive literature review and analysis of existing AI-driven educational technologies, we identi

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Garg, Shally. "Intelligent Tutoring Systems: The Future of AI-Powered Personalized Learning." International Scientific Journal of Engineering and Management 01, no. 03 (2022): 1–6. https://doi.org/10.55041/isjem00114.

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The current research looks at the transformative power of artificial intelligence (AI) in education. It looks at how these technologies personalize learning, automate processes, and provide data-driven insights. The abstract goes on to explore the positives, such as improved learning outcomes and efficiency, as well as concerns such data privacy and ethical considerations. Finally, it briefly discusses the future direction of AI in education. Keywords— Artificial intelligence in education, AIEd. Personalized learning with AI, AI ethics in education

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Alouthah, Mohammad Ghatyan Sulaiman, Mateb Falah Nahar Alshammari, Hamoud Faraj Freej Alsaadi, et al. "Advanced technologies in rehabilitation programs: Role of AI in diagnosis-an updated review." International journal of health sciences 8, S1 (2024): 1588–604. http://dx.doi.org/10.53730/ijhs.v8ns1.15335.

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Background: Cognitive decline, particularly associated with aging and neurodegenerative disorders, significantly affects individuals' ability to perform daily activities. Cognitive Rehabilitation Therapy (CRT) offers a non-pharmacological intervention that focuses on regaining or compensating for lost cognitive functions. The integration of Artificial Intelligence (AI) into rehabilitation programs has shown transformative potential in enhancing diagnosis, personalized care, and improving outcomes for patients with cognitive impairments. Aim: This updated review explores the role of AI in perso

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Arman Z., Serrano,, Mandapat, Jose Emmanuel C., Cuzzamu, Emerson B., and Vinoya, Armando S. "Optimizing Stem Curriculum Design through Artificial Intelligence: A Comparative Study of Personalized Learning Algorithms in Secondary Education." Asian Journal of Advanced Research and Reports 19, no. 6 (2025): 507–27. https://doi.org/10.9734/ajarr/2025/v19i61075.

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The research compares AI-driven personalized learning algorithms and traditional learning in Science, Technology, Engineering and Mathematics curriculum design optimization, with emphasis on secondary-level biology class in West Wendover High School, Nevada, United States. Using a two-shot quasi-experimental design, the research investigates the academic performance of Grade 10 students, a sample size of (n=60) who are divided into an experimental group (AI-driven learning) and a control group (traditional learning). Pre and post-tests assessed student performance in two biology units studied—

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Faisal Tariq Hasan and Rafiq Rahman. "Artificial Intelligence in Personalized Learning: A New Era of Education." Proceeding of The International Conference of Inovation, Science, Technology, Education, Children, and Health 4, no. 2 (2024): 306–8. https://doi.org/10.62951/icistech.v4i2.127.

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Artificial Intelligence (AI) is reshaping education by enabling personalized learning experiences tailored to individual students' needs. This paper examines the application of AI-driven systems in adaptive learning platforms, intelligent tutoring, and automated assessments. It highlights how AI enhances student engagement, provides real-time feedback, and improves learning outcomes. The study concludes that AI-driven education fosters better student performance and learning efficiency.

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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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Kamaghe, Juliana. "esign and Implementationof AI-Driven Personalized Learning Tools for Tanzanian SecondarySchools." International Journal of Advances in Scientific Research and Engineering 11, no. 01 (2025): 10–22. https://doi.org/10.31695/ijasre.2025.1.2.

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This study investigates the effectiveness of AI-powered personalisedlearning tools in Tanzanian secondary schools. The research explores the potential of these tools to address the unique challenges these schools face, including large class sizes, limited resources, and significant language diversity. Through a comparative analysis of various AI tools, the study examines their adaptability to Tanzania's educational context, considering language diversity, cultural relevance, and infrastructure constraints. The research employs qualitative design, incorporating comparative case study elements t

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Zaharuddin, Chen Yu, and Goh Yao. "Enhancing Student Engagement with AI-Driven Personalized Learning Systems." International Transactions on Education Technology (ITEE) 3, no. 1 (2024): 1–8. http://dx.doi.org/10.33050/itee.v3i1.662.

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This paper explores the impact of AI-driven personalized learning systems on enhancing student engagement in educational settings. With the increasing integration of artificial intelligence (AI) in various sectors, education is also experiencing a shift towards more adaptive and personalized learning environments. The study investigates how personalized learning paths, powered by AI algorithms, can address diverse learning needs and promote greater involvement from students. Through a comprehensive analysis of engagement metrics, pre-and post-implementation comparisons, and surveys from both s

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Kushwaha, Prince, Deepak Namdev, and Sakshi Singh Kushwaha. "SmartLearnHub: AI-Driven Education." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 1396–401. http://dx.doi.org/10.22214/ijraset.2024.58583.

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Abstract: SmartLearnHub is an innovative AI-driven education platform designed to revolutionize traditional learning experiences. This research paper explores the development, implementation, and impact of SmartLearnHub, focusing on its adaptive learning paths, personalized quizzes, and intelligent content recommendations. The study delves into the system's architecture, machine learning models employed, and the resulting user engagement and performance metrics. Through a comprehensive evaluation, we highlight the positive impact of SmartLearnHub on the education landscape, emphasizing the sig

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Xu, Katherine. "AI-Driven Personalized Fall Prevention for Older Adults." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29610–12. https://doi.org/10.1609/aaai.v39i28.35342.

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Falls among older adults pose a significant public health challenge, impacting quality of life and healthcare costs. This research proposal aims to develop an innovative AI-driven personalized fall prevention system for older adults, leveraging advanced machine learning techniques in computer vision, natural language processing, and reinforcement learning. The proposed system will encompass five key components: (1) Advanced pose estimation and activity recognition using HRNet with attention mechanisms and hybrid LSTM-GCN models; (2) Personalized risk assessment through multi-modal deep learnin

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Ismayilli, Fatima. "REVOLUTIONIZING LANGUAGE LEARNING: INTEGRATION OF AI TO PERSONALIZED LANGUAGE LEARNING." Deutsche internationale Zeitschrift für zeitgenössische Wissenschaft 79 (May 7, 2024): 22–23. https://doi.org/10.5281/zenodo.11127272.

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"Revolutionizing Language Learning: Integration of AI to Personalized Language Learning" examines the profound influence of AI on language education. The article explores the fundamental nature of customized learning, emphasizing the importance of artificial intelligence in customizing learning experiences according to individual requirements. The text explores a range of AI-driven tools and methods used in language learning, with a particular focus on their effectiveness in improving learning results. The authors highlight the significance of AI in delivering instantaneous feedback, adaptable

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Mahniza, Melda, Resti Elma Sari, Puji Hujria Suci, Indra Saputra, and Elviza Yeni Putri. "AI-Driven Learning: Mediating and Moderating Dynamics in Self-Regulated Learning." Journal of Educational Science and Technology (EST) 10, no. 3 (2024): 229. https://doi.org/10.26858/est.v10i3.68254.

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The rapid integration of artificial intelligence (AI) in education has transformed how students learn, particularly in fostering self-regulated learning (SRL). However, understanding the mechanisms and conditions under which AI adoption influences SRL remains underexplored. This study investigates the roles of achievement goals, cognitive load, personalized learning, students' adaptability, and AI competence in shaping SRL within an AI-enhanced educational framework. The research employs Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach to analyze direct, mediati

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Wang, Junyao, Yasmin Hussain, and Chencheng Mao. "Artificial Intelligence-Driven Personalized Learning: Psychological Implications and Educational Outcomes." International Journal of Education, Humanities and Social Sciences 2, no. 1 (2025): 24–39. https://doi.org/10.70088/3wnrs278.

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This paper explores the psychological implications and educational outcomes of artificial intelligence (AI)-driven personalized learning systems. The study delves into how AI facilitates customized learning experiences, adapting to individual student needs and learning styles. The research highlights the impact of AI on student motivation, cognitive load, and academic performance, as well as potential ethical concerns such as data privacy and algorithmic bias. Empirical findings from various case studies demonstrate how AI-driven platforms enhance engagement and retention rates. The study conc

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Chima Abimbola Eden, Olabisi Oluwakemi Adeleye, and Idowu Sulaimon Adeniyi. "A review of AI-driven pedagogical strategies for equitable access to science education." Magna Scientia Advanced Research and Reviews 10, no. 2 (2024): 044–54. http://dx.doi.org/10.30574/msarr.2024.10.2.0043.

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Access to quality science education is essential for equitable development and advancement in society. However, disparities in access to science education persist, particularly among marginalized and underserved populations. Artificial intelligence (AI) offers innovative solutions to address these disparities by enhancing pedagogical strategies that promote equitable access to science education. This review examines AI-driven pedagogical strategies aimed at improving equitable access to science education. The review explores how AI technologies, such as machine learning, natural language proce

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Rana, Ashutosh. "AI-DRIVEN PERSONALIZED LEARNING: TRANSFORMING EDUCATION THROUGH ADAPTIVE TECHNOLOGY." INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND MANAGEMENT INFORMATION SYSTEMS 16, no. 2 (2025): 1273–89. https://doi.org/10.34218/ijitmis_16_02_080.

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Kai, Qi. "Artificial Intelligence Applications in Healthcare Diagnosis and Treatment in the Era of Big Data." Pacific International Journal 8, no. 2 (2025): 60–66. https://doi.org/10.55014/pij.v8i2.791.

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The integration of artificial intelligence (AI) into healthcare is transforming medical practices, particularly in diagnosis, treatment, and drug discovery. This study explores the applications of AI in healthcare through the case studies of Tempus Labs, PathAI, and Insilico Medicine, focusing on their use of big data and machine learning algorithms to enhance precision medicine, optimize drug development, and improve patient outcomes. Tempus Labs leverages AI for personalized cancer treatment by analyzing genomic and clinical data, while PathAI applies deep learning algorithms for accurate me

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Sumanth Reddy, Shiva, Girish N, Anil Kumar B, Jahnavi S, Manjunath D R, and Nandini C. "AI-Driven Personalized Learning and Content Extraction: An Emerging Paradigm." Journal of Information Security System and Cyber Criminology Research 1, no. 3 (2025): 32–39. https://doi.org/10.46610/joissccr.2024.v01i03.004.

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Integrating Artificial Intelligence (AI) into personalized learning platforms revolutionizes the educational landscape. This paper examines recent advancements in AI-driven customized learning systems that dynamically adapt to the needs of individual learners, offering tailored content and lesson planning. These systems utilize AI algorithms to adjust learning materials based on a student’s performance, creating an adaptive environment that fosters engagement and improves learning outcomes. Additionally, AI technologies in content extraction enhance the ability to summarize and organize inform

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Rini, Atmi Sapta, Son Wandrial, Lutfi Lutfi, Ida Jaya, and Bambang Satrionugroho. "Data-Driven Marketing: Harnessing Artificial Intelligence to Personalize Customer Experience and Enhance Engagement." Join: Journal of Social Science 1, no. 6 (2024): 282–95. http://dx.doi.org/10.59613/akx6j040.

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This study explores the integration of artificial intelligence (AI) in data- driven marketing to personalize customer experiences and enhance engagement. Using a qualitative research approach through literature review and library research, this paper examines various AI-driven marketing strategies and their impact on customer interactions. The analysis reveals that AI technologies, such as machine learning, predictive analytics, and natural language processing, have revolutionized marketing by enabling personalized content, product recommendations, and dynamic customer segmentation. These adva

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Researcher. "ARTIFICIAL INTELLIGENCE IN EDUCATION: ENABLING PERSONALIZED LEARNING AND ENHANCED NETWORKING." International Journal of Engineering and Technology Research (IJETR) 9, no. 2 (2024): 368–78. https://doi.org/10.5281/zenodo.13843529.

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This article explores the transformative role of Artificial Intelligence (AI) in education, focusing on its applications in personalized learning and enhanced networking. It examines how AI algorithms analyze student data to create customized learning paths, the implementation of intelligent tutoring systems and adaptive learning platforms, and the use of AI-driven networking to connect students, educators, and experts globally. The article also discusses AI's role in streamlining administrative tasks, the challenges and ethical considerations in AI implementation, and future directions includ

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Abisoye, Ajayi. "Creating a Conceptual Framework for AI-Powered STEM Education Analytics to Enhance Student Learning Outcomes." Journal of Frontiers in Multidisciplinary Research 5, no. 1 (2024): 157–67. https://doi.org/10.54660/.ijfmr.2024.5.1.157-167.

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Artificial intelligence (AI) is increasingly transforming education by providing data-driven insights that enhance student learning outcomes. In STEM education, AI-powered learning analytics enable personalized instruction, real-time assessment, and adaptive curriculum adjustments based on individual student needs. This paper proposes a conceptual framework for AI-driven STEM education analytics, outlining its core components, including data collection, processing, and insights generation. The study explores key AI applications in education, such as machine learning models for personalized lea

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Venkateswaran Petchiappan. "AI-driven vehicle customization and personalization in automobile industry." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 157–68. https://doi.org/10.30574/wjaets.2025.15.3.0921.

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The automobile industry is experiencing a profound digital transformation with artificial intelligence emerging as a cornerstone technology reshaping customer experiences and operational paradigms. AI-powered vehicle selection and configuration systems represent transformative applications revolutionizing how consumers discover, personalize, and purchase vehicles. Modern automotive manufacturers leverage sophisticated data analytics platforms like SAP HANA, with in-memory computing capabilities processing configuration variables during real-time customer interactions. These systems analyze sub

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Zailani Iman, Muhammad, Alfian Airlangga Asis, and Aynu Uzma Zein Rahma. "Enhancing Personalized Learning: The Impact of Artificial Intelligence in Education." Edu Spectrum: Journal of Multidimensional Education 1, no. 2 (2024): 101–12. https://doi.org/10.70063/eduspectrum.v1i2.55.

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This research explores the transformative impact of artificial intelligence (AI) on personalized learning in educational settings. As traditional teaching methods struggle to cater to the diverse needs of students, AI offers innovative solutions that can tailor educational experiences to individual learning styles, preferences, and paces. This study investigates the various applications of AI technologies, including intelligent tutoring systems, adaptive learning platforms, and data-driven insights, to enhance personalized learning outcomes. By employing a mixed-methods approach that combines

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Pushkar, Mehendale. "Personalization in Education through AI." European Journal of Advances in Engineering and Technology 10, no. 3 (2023): 60–65. https://doi.org/10.5281/zenodo.12789606.

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Personalized learning, aided by artificial intelligence (AI), is a groundbreaking shift in education that abandons traditional one-size-fits-all approaches. This paper investigates the impact of AI on personalized education, particularly emphasizing how adaptive learning systems can enhance student engagement, motivation, and academic performance. A comprehensive review of current research and case studies highlights the technological, pedagogical, and ethical implications of implementing AI-driven personalized learning. The findings underscore the potential of AI to revolutionize education by

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Adriana Rodríguez, Rosales. "The Impact of Statistics and Probability on Educational Artificial Intelligence." Advances and Applications in Statistics and Probability 1, no. 1 (2024): 001–4. http://dx.doi.org/10.17352/aasp.000001.

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Artificial intelligence has transformed e-learning by enabling personalized and efficient teaching. This manuscript analyzes the importance of statistics and probability in educational AI. Statistical methodologies improve decision-making, personalize learning, and optimize educational outcomes. Challenges such as data privacy and ethics are addressed. Case studies demonstrate the practical applications of AI in diverse educational contexts. Future directions suggest a need for robust research to further understand and implement AI-driven educational strategies. The findings underscore the cri

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Zahra, Sadat Roozafzai, and Zaeri Parisa. "Digital dynamics: Exploring the intersection of AI, animation, and personalized learning." i-manager's Journal of Educational Technology 21, no. 1 (2024): 1. http://dx.doi.org/10.26634/jet.21.1.20850.

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This study investigates the transformative potential of integrating Artificial Intelligence (AI), animation, and personalized learning in contemporary education. Employing a mixed-methods approach involving interviews and experimental manipulations, the research examines the interconnectedness of these three domains and their collective impact on student engagement and English reading comprehension learning outcomes. The study employed a quasi-experimental design. Participants engaged in an 8-week AI-driven personalized learning intervention that incorporated animated content, with pre- and po

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Atole, Hruturaj. "Capit-AI: An AI driven Financial Advisory System." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5124–36. https://doi.org/10.22214/ijraset.2025.69398.

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AI-driven financial advisory systems are transforming investment planning, tax optimization, and financial literacy by automating key decision-making processes. Capit-AI is an intelligent AI-powered financial advisor that integrates machine learning (ML), Natural Language Processing (NLP), and automation to assist users in managing mutual funds, stocks, tax saving strategies, and real-time expense tracking. The system consists of multiple AI agents, including a Web Agent, Finance Agent, Mutual Fund Agent, and Tax Agent, to provide personalized financial insights. Capit-AI overcomes traditional

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Leon, Ramona-Diana, Ángel Ortiz, Mª del Mar Alemany Díaz, and Ana Esteso Alvarez. "Using AI for developing personalized learning paths." International Journal of Advanced Statistics and IT&C for Economics and Life Sciences 14, no. 1 (2024): 13–19. https://doi.org/10.2478/ijasitels-2024-0001.

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Abstract This research aims to examine how artificial intelligence (AI) can be used within the educational framework for developing personalized learning paths. In order to achieve this goal, an etic approach is employed, and a qualitative-quantitative perspective is adopted. Thus, following the PRISMA guidelines, 71 articles published on Web od Science, during January 2014 – June 2024, are selected and analysed using cluster and density analysis. The results bring forward that the peak of the scientific production was reached in 2022 and that the topic is more appealing to the scholars from t

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Prof., Ashlesha Deole. "AI-Driven Personalized Learning: Enhancing Student Engagement and Academic Performance." International Journal of Advance and Applied Research S6, no. 22 (2025): 1043–45. https://doi.org/10.5281/zenodo.15534465.

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<em>This research explores the impact of AI-driven personalized learning systems on enhancing student engagement and improving academic performance. By utilizing machine learning algorithms such as Reinforcement Learning (RL) and Collaborative Filtering, the study investigates how AI can provide tailored educational experiences that meet the diverse needs and preferences of individual students. Data collected from several higher education institutions shows that AI-powered systems significantly increase student engagement, improve academic performance, and reduce dropout rates by offering real

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Goutham, Bilakanti. "AI-Powered Precision Medicine Transforming Personalized Healthcare." International Journal of Leading Research Publication 4, no. 10 (2023): 1–12. https://doi.org/10.5281/zenodo.15196949.

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AI is redefining precision medicine from vast amounts of genetic, clinical, and patient data to provide personalized healthcare approaches. AI-powered genetically diverse models examine genetic variations, forecast patient response to treatment, and optimize therapy options per person. Machine learning and deep learning technologies facilitate the development of drugs by speeding up biomarker identification and minimizing trial-and-error for pharmaceutical development. AI helps in the identification of disease pattern, allowing for early diagnosis and preventive treatment. AI-based application

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Pawar, Dr Suvarna. "Advancing Interview Preparation: An AI-driven Approach." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28705.

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This research paper presents a transformative approach to interview preparation through the integration of cutting-edge artificial intelligence and personalized learning techniques. In today's competitive job market, traditional methods of interview readiness often lack the immediacy and tailored support needed for success. Addressing this gap, our platform utilizes advanced AI algorithms to analyze user responses, offer real-time feedback, and dynamically adjust question difficulty based on performance. By prioritizing user-centric design and ethical considerations, our research explores the

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Harsh Tiwari, Shrut Jain, Shubham Kumar, Varun Soni, and Aradhana Negi. "AI-driven English language learning: Leveraging applications/APIs for dynamic content and feedback." World Journal of Advanced Research and Reviews 22, no. 3 (2024): 1611–16. http://dx.doi.org/10.30574/wjarr.2024.22.3.1882.

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In our interconnected world, language proficiency is crucial for communication and cultural exchange. Digital tools like Duolingo and Babbel have revolutionized language learning. This paper introduces Fluency Mentor, an AI-driven application using the Generative AI Technologies Like ChatGPT & Gemini API to offer personalized lessons, interactive exercises, and instant feedback, fostering a supportive community for continuous improvement and global engagement. Going beyond standard memorization, this method focuses on genuine future-speaking ability with real-life conversation practice dri

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Silva, Gabriel, Gelard Godwin, and Oscar Jayanagara. "The Impact of AI on Personalized Learning and Educational Analytics." International Transactions on Education Technology (ITEE) 3, no. 1 (2024): 36–46. http://dx.doi.org/10.33050/itee.v3i1.669.

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The rapid advancement of artificial intelligence (AI) has revolutionized personalized learning and educational analytics, presenting new opportunities and challenges for adaptive education. This paper explores the impact of AI-driven technologies in creating personalized learning environments by examining how adaptive algorithms and data analytics shape educational experiences. The primary objective of this study is to assess the effectiveness of AI in enhancing learner engagement and outcomes through tailored instructional methods. Utilizing a mixed-method approach, this research gathers quan

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Meiramova, S. A., and S.B. Zagatova. "AI-POWERED SMART TECHNOLOGIES FOR ENHANCING INNOVATIVE ENGLISH TEACHING IN HIGHER EDUCATION IN KAZAKHSTAN." Proceeding of International Conference on Social Science and Humanity 2, no. 3 (2025): 861–69. https://doi.org/10.61796/icossh.v2i3.141.

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Objective: The development of foreign language teaching methodologies in the context of an evolving educational landscape is directly linked to the continuous integration of Artificial Intelligence (AI) in various fields. The integration of AI-driven tools and technologies has reshaped communication and educational experiences by creating opportunities for innovative pedagogical approaches in higher education in Kazakhstan. This paper explores the use of AI-powered smart technologies in English language learning and teaching, emphasizing their role in interactive educational activities, person

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Taşkın, Meltem. "Artificial Intelligence in Personalized Education: Enhancing Learning Outcomes Through Adaptive Technologies and Data-Driven Insights." Human Computer Interaction 8, no. 1 (2025): 173. https://doi.org/10.62802/ygye0506.

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The integration of Artificial Intelligence (AI) in personalized education is revolutionizing traditional learning paradigms, enabling adaptive, data-driven approaches to enhance learning outcomes. This research investigates how AI-driven technologies, including intelligent tutoring systems, adaptive learning platforms, and predictive analytics, transform the educational landscape by providing tailored, learner-centered experiences. AI facilitates the identification of individual learning patterns, preferences, and challenges, offering customized content delivery and real-time feedback to optim

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Lombardi, Dario, Luigi Traetta, Antonio Maffei, and Primož Podžaj. "Evolving Educational Horizons: Integrating AI with Innovative Teaching and Assessment Strategies." EDUCATION SCIENCES AND SOCIETY, no. 2 (January 2025): 185–203. https://doi.org/10.3280/ess2-2024oa18462.

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This systematic review examines 39 studies to identify Teaching and Learning Activities (TLAs) and Assessment Tasks (ATs) aligned with Bloom's Taxonomy, highlighting their role in fostering critical thinking and creativity. TLAs such as simulations, problem-solving, and gamification, combined with peer assessments and formative feedback, support higher-order cognitive skills. However, the review reveals a critical gap in integrating AI into these frameworks, despite AI's potential to personalize learning and enhance assessments. This absence limits the development of adaptive learning environm

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Zhao, Zhonglin. "AI-Driven Learning-Style Detection for Personalized MOOC Content Delivery in Lifelong Learning." International Journal of Education and Social Development 3, no. 1 (2025): 110–16. https://doi.org/10.54097/7d530x88.

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To investigate how artificial intelligence (AI) techniques detect learners’ individual learning styles and enable customized content delivery in Massive Open Online Courses (MOOCs) for lifelong learners. This review synthesizes findings from peer-reviewed studies on AI-driven personalization in MOOCs covering adopted methods, empirical results and gaps. The review shows that AI algorithms (e.g., neural networks, decision trees, clustering) can automatically identify learning style preferences by analyzing learners’ online interactions, often with high accuracy (frequently above 90%). Incorpora

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Yang, Ming, and FuYuan Weng. "AI-Powered Personalized Learning Journeys: Revolutionizing Information Management for College Students in Online Platforms." Journal of Information Systems Engineering and Management 8, no. 1 (2023): 23196. http://dx.doi.org/10.55267/iadt.07.14079.

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Since college students rely more on online education, artificial intelligence (AI) is changing virtual learning paths. The study shows how schools are personalising instruction and improving student engagement, comprehension, and retention with AI algorithms and data analytics. The essay covers key features&nbsp;of AI-powered personalised learning , from content recommendations to customisable evaluations and real-time feedback. The essay critiques these innovations' ethical and transparency difficulties, despite their potential benefits. It emphasises ethical AI-driven teaching by highlig

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Aadhi, Gayathri, and Asadi Srinivasulu Dr. "Enhancing Education through AI: Tailored Learning for Future Generations." International Journal of Contemporary Research in Multidisciplinary 2, no. 6 (2023): 52–59. https://doi.org/10.5281/zenodo.10446712.

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Tailored learning for future generations utilizes AI-driven personalized approaches to optimize educational experiences, adapting to individual needs and fostering lifelong learning. As education evolves in the digital era, the integration of Artificial Intelligence (AI) stands at the forefront, revolutionizing learning methodologies. This paper delves into the latest issues surrounding the application of AI in education, particularly focusing on tailored learning for future generations. Addressing concerns regarding equitable access and ethical implications, this research navigates the comple

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MOUMEN, Aziz. "Informatics and Didactics: A New Approach Based on Artificial Intelligence in Education." International Journal of Scientific Research and Innovative Studies 3, no. 4 (2024): 94–97. https://doi.org/10.5281/zenodo.14179305.

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<strong>Abstract</strong> In recent years, the integration of <strong>Informatics</strong> and <strong>Didactics</strong> in education has been revolutionized by the advent of <strong>Artificial Intelligence (AI)</strong>. AI has the potential to reshape how educational content is delivered, how students learn, and how teachers manage classrooms. In the context of Morocco, where educational reforms are underway, the integration of AI into teaching methodologies offers exciting possibilities for improving the quality of education, enhancing personalized learning experiences, and addressing the

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Vishal, Ananda Kuwar, Mahesh Asawa Parth, Ravindra Patil Ganesh, Nandkumar Pawar Vedant, Jain Preeti, and Yedurkar Amit. "AI Driven Healthcare Solution." TIJER - INTERNATIONAL RESEARCH JOURNALS 12, no. 2 (2025): a117—a121. https://doi.org/10.5281/zenodo.15209814.

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Artificial Intelligence (AI) is transforming the healthcare sector by providing innovative, data-centric solutions to various medical challenges. This project aims to develop spe- cialized AI agents tailored for healthcare applications, focusing on creating systems such as Ortho AI for orthopedic-related queries and Dermatology AI for skin-related issues. These AI solutions provide patients with accurate, personalized healthcare advice, supporting them in obtaining quicker, more reliable responses. Additionally, they assist healthcare professionals by enhancing diagnosis and treatment recommen

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Pradhan, Sneha S. "AI-Driven Travel Planner." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 3107–15. https://doi.org/10.22214/ijraset.2025.68012.

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The AI-Driven Travel Planner project leverages artificial intelligence and the Flutter framework to transform the travel planning process, making it more efficient, personalized, and user-friendly. The platform analyses user preferences such as destination type, budget, and travel dates to provide tailored destination recommendations. It generates detailed itineraries that optimize time, cost, and convenience, while also offering real-time assistance for on-the-go adjustments. With the ability to continuously learn from user behavior and feedback, the AI ensures that each recommendation become

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Sonawane, Prasad R. "Nexmind." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41910.

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This paper presents NextMind, an AI-driven interactive system designed to provide human-like companionship through three AI models: Girlfriend AI, Doppelgänger AI, and Friend AI. These models serve distinct purposes: the Girlfriend AI simulates real relationships with human-like interaction, requiring users to build and maintain engagement; the Doppelgänger AI acts as a personal memory assistant, recalling past conversations and experiences for a seamless, personalized experience; and the Friend AI functions as a problem-solving assistant akin to ChatGPT. The NextMind system utilizes Natural L

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