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Relevant bibliographies by topics / AI-driven personalized learning

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Academic literature on the topic 'AI-driven personalized learning'

Author: Grafiati

Published: 7 June 2025

Last updated: 17 July 2025

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

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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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Book chapters on the topic "AI-driven personalized learning"

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Rana, Shaila, and Rhonda Chicone. "AI-Driven Personalized Learning in Cybersecurity Training." In Fortifying the Future. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81780-9_2.

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Yekollu, Roop Kumar, Tejal Bhimraj Ghuge, Sammip Sunil Biradar, Shivkumar V. Haldikar, and Omer Farook Mohideen Abdul Kader. "AI-Driven Personalized Learning Paths: Enhancing Education Through Adaptive Systems." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3191-6_38.

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Tariq, Muhammad Usman. "AI-Driven Personalized Learning." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9770-1.ch004.

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The revolutionary potential of AI-driven personalized learning to improve teacher effectiveness and student engagement is examined in this chapter. By customizing content to each learners unique learning needs and preferences AI technologies like intelligent tutoring platforms and adaptive learning systems are revolutionizing educational experiences. By letting students advance at their own speed and addressing their weaknesses and strengths personalized learning creates a more stimulating and productive learning environment. The importance of artificial intelligence (AI) in boosting student motivation is examined in this chapter using interactive tools such as gamification augmented and virtual reality apps and real-time feedback systems. The chapter discusses how AI affects student learning as well as how it can help teachers become more efficient by automating repetitive administrative duties like attendance and grading. Teachers can concentrate more on professional development teamwork and instruction by freeing up valuable time.

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Babu, C. V. Suresh, Yenumala Bhargavi, Priya R. Krishna, and Deva Darshini S. "AI-Driven Personalized Healthcare Solutions." In Advances in Healthcare Information Systems and Administration. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7858-8.ch009.

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This study aims to explore the transformative potential of AI-driven personalized healthcare in enhancing patient outcomes and optimizing healthcare delivery. Utilizing a comprehensive literature review and analysis of current AI technologies, the research identifies key areas such as data integration, machine learning algorithms, and patient engagement strategies. The findings reveal that AI can significantly improve treatment accuracy, predict disease risks, and foster patient adherence through tailored interventions. However, challenges related to data privacy, algorithmic bias, and regulatory compliance must be addressed to ensure equitable implementation. The study concludes that while AI holds promise for revolutionizing healthcare, a collaborative approach involving stakeholders is essential for overcoming barriers and maximizing benefits. The implications of this research underscore the need for ongoing innovation and ethical considerations in the deployment of AI technologies in healthcare settings.

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Kaur, Harjinder, and Shilpa. "AI-Driven Healthcare." In Advances in Electronic Government, Digital Divide, and Regional Development. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7832-8.ch006.

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Artificial Intelligence has brought significant advances in the healthcare sector regarding diagnosis, medication, and patient-centric care. Artificial intelligence technologies, such as natural language processing and machine learning, have been utilized to predict various diseases early to help diagnose and treat them appropriately. In the healthcare sector, integrating AI tools and applications facilitates healthcare services such as remote monitoring and telehealth, hereby improving healthcare accessibility which further aids in continuous patient engagement, monitoring, and care outside traditional medical situations. Additionally AI helps in extending personalized medicine, which further helps where it helps in the modification of treatment plans for individual patients by considering their health-related risk factors such as genetic makeup, lifestyle, and previous medical history, thereby augmenting their therapeutic outcomes. AI is revolutionizing the field of drug discovery by accelerating and identifying possible therapeutic applicants by optimizing their clinical trials.

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Aeni, Nur, Muthmainnah Muthmainnah, La Sunra La Sunra, Auliyanti Sahril Nurfadhilah, Faidhul Inayah, and Nurwahida. "AI-Driven Classroom Conversations." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-6165-8.ch009.

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This study looks at how modern education uses Artificial Intelligence (AI) technologies to boost student engagement and speaking skills. This study underlines the relevance of personalized learning experiences and AI's ability to transform language learning in the context of Education 5.0. The study aims to fill a vacuum in the education literature by demonstrating how AI-driven classroom discussions might boost student engagement and speaking abilities. The study used a qualitative research approach to extensively examine AI-facilitated speaking activities in primary and secondary schools. The chatbot offers real-time feedback on grammar, vocabulary, and pronunciation, fostering an environment conducive to learning. Research findings indicate that students experience increased confidence in their speaking abilities, improved fluency, and heightened motivation to practice outside of the classroom. The study uses case studies to demonstrate how AI transforms student learning by increasing engagement and speaking proficiency.

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Akpabio, Edidiong, Idaraesit Akpabio, and Ifiokobong Akpabio. "Leveraging AI for Personalized English Language Learning." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9077-1.ch015.

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AI in English language learning is transforming the education sector by providing personalized and adaptive instruction. Techniques such as machine learning algorithms, speech recognition, and intelligent feedback systems meet the needs of individual learners. Dynamic adaptation of instructions ensures that the difficulty of the content aligns with a learner's level of proficiency. These include data privacy, lack of access due to the digital divide, and issues regarding bias and fairness. The current trends associated with AI-driven personalized learning involve AI-based virtual tutors, intelligent learning analytics, and content generation using AI that is driven by the learners' strengths and weaknesses. AI is expected to support teachers rather than replace them and emphasize lifelong learning needs for individual pathways in acquiring a language. AI will help the universal enrichment of English language skills. It promises to engage the students in inclusive environments with practical learning, great entertainment, and an emphasis on privacy, accessibility, and ethics.

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Babu, C. V. Suresh, Bhavesh M., Albert Gururaja V., and Venkatesh T. D. "AI-Driven Instructional Design." In Advances in Educational Technologies and Instructional Design. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-6527-4.ch003.

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This study aims to explore the transformative potential of AI-driven instructional design in enhancing personalized learning experiences and improving educational efficiency. Utilizing a mixed-methods approach, the research combines quantitative data analysis with qualitative insights from educators and students to assess the effectiveness of AI tools in instructional practices. Key findings indicate that AI technologies significantly enhance student engagement, motivation, and learning outcomes while alleviating administrative burdens on educators. The study concludes that while AI presents substantial opportunities for innovation in education, it also raises ethical concerns regarding data privacy and algorithmic bias that must be addressed. The implications of this research underscore the need for responsible AI integration in educational settings to ensure equitable access and support for all learners.

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Yambal, Sucheta, and Yashwant Arjunrao Waykar. "AI-Driven Classroom Management." In Advances in Educational Technologies and Instructional Design. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-8191-5.ch004.

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AI is revolutionizing education, especially classroom management. AI-driven technologies boost productivity, student engagement, and personalized instruction. AI's role in classroom management is examined in this research. It analyzes how AI technologies like intelligent tutoring systems, behavior analysis tools, and adaptive learning platforms may reduce administrative tasks, enhance interactive learning environments, and provide quick student performance insights. The report also addresses ethical problems including data protection, algorithmic bias, and transparency, which are crucial to AI inclusion in education. This research explores the pros and cons of AI-driven classroom management to demonstrate how AI may be utilized ethically and effectively. This talk underlines the need of properly employing AI technology to balance operational efficiency with fair and transparent processes, making education more effective and engaging.

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Vashishth, Tarun Kumar, Vikas Sharma, Kewal Krishan Sharma, Bhupendra Kumar, Rajneesh Panwar, and Sachin Chaudhary. "AI-Driven Learning Analytics for Personalized Feedback and Assessment in Higher Education." In Advances in Media, Entertainment, and the Arts. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-0639-0.ch009.

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Advancements in artificial intelligence (AI) and learning analytics have opened up new possibilities for personalized education in higher education institutions. This chapter explores the potential of AI-driven learning analytics in higher education, focusing on its application in personalized feedback and assessment. By leveraging AI algorithms and data analytics, personalized feedback can be provided to students, targeting their specific strengths and areas for improvement. Adaptive and formative assessments can also be facilitated through AI-driven learning analytics, enabling personalized and accurate evaluation of students' knowledge and skills. However, ethical considerations, implementation challenges, and faculty training are crucial aspects that must be addressed for successful adoption. As technology continues to evolve, embracing AI-driven learning analytics can enhance student engagement, support individualized learning, and optimize educational outcomes.

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Conference papers on the topic "AI-driven personalized learning"

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Istanti, Wati, Suseno, Santi Pratiwi, and Kundharu Saddhono. "AI-Driven Personalized Learning: Revolutionizing Language Education." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10923274.

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Ghag, Aum, Bhavya Bavishi, Nehaal Choudhary, and Ami Munshi. "Revolutionizing Education with PersonifAI: A Deep Dive into AI-Driven Personalized Learning." In 2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, 2025. https://doi.org/10.1109/sceecs64059.2025.10940280.

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V, Kumaresan, Jeyakarthic M, Manjula Devi P, Jona Innisai Rani P, Mohan E, and Surendra K R. "Personalized AI-Driven Online Education Technologies for Adaptive Learning and Cognitive Skill Enhancement." In 2025 7th International Conference on Signal Processing, Computing and Control (ISPCC). IEEE, 2025. https://doi.org/10.1109/ispcc66872.2025.11039410.

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D, Anitha Kumari, Sudha S, Gowtham M, Divya R, Bala Thirupura Sundari K. S, and Vardhini V. "AI-Driven MBA Programs for Personalized Learning and Skill Development for Future Business Leaders." In 2025 International Conference on Automation and Computation (AUTOCOM). IEEE, 2025. https://doi.org/10.1109/autocom64127.2025.10957450.

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Garcia-Suarez, David, Bárbara Regina Granados Guzmán, Carlos Andrés Hernández Alamillo, and Edgar Paul Martínez Ludert Muñoz de Cote. "AI-Driven Personalized Learning Profiles to Enhance Student Performance in Basic Physics: A Pilot Study." In 2025 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2025. https://doi.org/10.1109/educon62633.2025.11016294.

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Mondal, Somnath, Sujan Das, Shib Shankar Golder, Rajesh Bose, Sharabani Sutradhar, and Haraprasad Mondal. "AI-Driven Big Data Analytics for Personalized Medicine in Healthcare: Integrating Federated Learning, Blockchain, and Quantum Computing." In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). IEEE, 2024. https://doi.org/10.1109/icaiqsa64000.2024.10882330.

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Saardloun, Punpaporn, Napacha Mahadumrongkul, Kanis Surajarus, et al. "Designing a User-Centric AI-Driven Mobile App for Personalized Time Management: Integrating Machine Learning and Design Thinking." In 2024 23rd International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2024. https://doi.org/10.1109/iscit63075.2024.10793695.

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Kanjalkar, Jyoti, Pramod Kanjalkar, Raj Khanke, Rushikesh Mane, Kunal Kharat, and Kanad Kolhe. "An AI-Driven Framework for Personalized Diet Generation and Nutrition Suggestions Using Machine Learning, Computer Vision and NLP." In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2024. https://doi.org/10.1109/iciics63763.2024.10859953.

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Almeida, Yuri, Manisha Sirsat, Sergi Bermúdez i Badia, and Eduardo Fermé. "AI-Rehab: A Framework for AI Driven Neurorehabilitation Training - The Profiling Challenge." In Special Session on Machine Learning and Deep Learning Improve Preventive and Personalized Healthcare. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009369108450853.

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Almeida, Yuri, Manisha Sirsat, Sergi Bermúdez i Badia, and Eduardo Fermé. "AI-Rehab: A Framework for AI Driven Neurorehabilitation Training - The Profiling Challenge." In Special Session on Machine Learning and Deep Learning Improve Preventive and Personalized Healthcare. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009369100002513.

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Reports on the topic "AI-driven personalized learning"

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Pasupuleti, Murali Krishna. Empathetic AI in Action: Transforming Customer Service with Emotional Intelligence. National Education Services, 2025. https://doi.org/10.62311/nesx/rr725.

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Abstract: This article explores the transformative impact of Emotionally Intelligent AI on customer service, focusing on how AI systems are designed to understand and respond to human emotions with empathy and precision. It delves into the core technologies, such as sentiment analysis, emotion recognition models, and reinforcement learning, that enable AI to provide emotionally aware interactions. Practical applications are discussed, including AI-powered customer support, personalized experiences, and crisis management solutions. The Article also covers the psychological foundations of AI-dri

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