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Littérature scientifique sur le sujet « Personalization of Learning »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Personalization of Learning"

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Alzain, Alzain, Steve Clark, Gren Ireson, and Ali Jwaid. "Learning Personalization Based on Learning Style instruments." Advances in Science, Technology and Engineering Systems Journal 3, no. 3 (2018): 108–15. http://dx.doi.org/10.25046/aj030315.

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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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Ginns, Paul, and Jennifer Fraser. "Personalization enhances learning anatomy terms." Medical Teacher 32, no. 9 (2010): 776–78. http://dx.doi.org/10.3109/01421591003692714.

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Yoganarasimhan, Hema. "Search Personalization Using Machine Learning." Management Science 66, no. 3 (2020): 1045–70. http://dx.doi.org/10.1287/mnsc.2018.3255.

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Komilova, M.O, and L.D Kholmurodova. "EFFECTIVE PERSONALIZATION IN BLENDED LEARNING." International Journal of Education, Social Science & Humanities. FARS Publishers 11, no. 1 (2023): 974–79. https://doi.org/10.5281/zenodo.7590956.

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Amulya, Mrs A., Abhishek Sai Dubey, B. Ainesh, and K. Vinay Kumar. "Immersive Learning: Revolutionizing Education Through Sense Stimulation and Personalization." International Journal of Research Publication and Reviews 6, no. 5 (2025): 16805–9. https://doi.org/10.55248/gengpi.6.0525.19113.

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Zhang, Chuxu, Huaxiu Yao, Lu Yu, et al. "Inductive Contextual Relation Learning for Personalization." ACM Transactions on Information Systems 39, no. 3 (2021): 1–22. http://dx.doi.org/10.1145/3450353.

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Web personalization, e.g., recommendation or relevance search, tailoring a service/product to accommodate specific online users, is becoming increasingly important. Inductive personalization aims to infer the relations between existing entities and unseen new ones, e.g., searching relevant authors for new papers or recommending new items to users. This problem, however, is challenging since most of recent studies focus on transductive problem for existing entities. In addition, despite some inductive learning approaches have been introduced recently, their performance is sub-optimal due to rel

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Buha, Vesna, Verica Vasiljevic, and Slobodanka Djenic. "Distance learning and process of personalization." Tehnika 72, no. 4 (2017): 591–600. http://dx.doi.org/10.5937/tehnika1704591b.

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Rigou, Maria, Spiros Sirmakessis, and Athanasios Tsakalidis. "Integrating Personalization in E-Learning Communities." International Journal of Distance Education Technologies 2, no. 3 (2004): 47–58. http://dx.doi.org/10.4018/jdet.2004070105.

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Bezza, Assma, Amar Balla, and Farhi Marir. "Improving Personalization In E-Learning Systems." International Journal of Technology and Educational Marketing 4, no. 2 (2014): 75–84. http://dx.doi.org/10.4018/ijtem.2014070107.

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Individual learners have different requirements and characteristics, and as a result learning content should be able to be personalized and adaptable to the e-learner' profile. Little research work undertaken to tackle this issue, and it has been limited to ad-hoc work on personalizing, and adapting learning content in e-Learning. This paper presents two methods for modeling user profile and for personalizing and adapting a given content to match that profile: inductive (without user intervention) and deductive (with user intervention). These methods will be used as a base to review and classi

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Thèses sur le sujet "Personalization of Learning"

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Anderson, Corin R. "A machine learning approach to Web personalization /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/6875.

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Clement, Benjamin. "Adaptive Personalization of Pedagogical Sequences using Machine Learning." Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0373/document.

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Les ordinateurs peuvent-ils enseigner ? Pour répondre à cette question, la recherche dans les Systèmes Tuteurs Intelligents est en pleine expansion parmi la communauté travaillant sur les Technologies de l'Information et de la Communication pour l'Enseignement (TICE). C'est un domaine qui rassemble différentes problématiques et réunit des chercheurs venant de domaines variés, tels que la psychologie, la didactique, les neurosciences et, plus particulièrement, le machine learning. Les technologies numériques deviennent de plus en plus présentes dans la vie quotidienne avec le développement des

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Khaled, Mélissa. "Learning styles, Personalization, and Learning Management Systems : Towards a Student-Centred LMS Approach." Thesis, Uppsala universitet, Institutionen för ABM, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447989.

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This study investigates existing learning management systems practices, in this case Canvas and Moodle in relation to user personalization and students’ learning styles as both factors are closely contribute to the design of a meaningful learning experience for learners. With the expansion of these teaching tools and methods, it seems crucial to determine to what extent they actually serve the learner and what role is really given to the student using these online platforms. Factors such as instructors’ feedback, peer communication, learning objects and follow-up will be examined. This study i

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Baldiris, Navarro Silvia Margarita. "Supporting competence development processes on open learning systems through personalization." Doctoral thesis, Universitat de Girona, 2012. http://hdl.handle.net/10803/98478.

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This thesis aim for promoting the learning objects economy by offering teacher the possibility of generating adaptive and standardized learning designs. The adaptation of the generated learning design considers two of the most relevant users characteristic: their competences and their learning styles. Standardized and Adaptive Learning Design Generation Process was implemented using HTN planning. Generation process considers a few inputs from the teachers, in particular, those related with the standardized competence definition, the learning objects metadata as well as the data from the initia

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FERRARI, ANNA. "Personalization of Human Activity Recognition Methods using Inertial Data." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2021. http://hdl.handle.net/10281/305222.

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Recognizing human activities and monitoring population behavior are fun- damental needs of our society. Population security, crowd surveillance, healthcare support and living assistance, lifestyle and behavior tracking are some of the main applications which require the recognition of activities. Activity recognition involves many phases, i.e. the collection, the elaboration and the analysis of information about human activities and behavior. These tasks can be fulfilled manually or automatically, even though a human-based recognition system is not long-term sustainable and scalable. Neverthel

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Abou-Khalil, Victoria. "Supporting Learner Centered Vocabulary Learning in Informal Learning Environments." Kyoto University, 2020. http://hdl.handle.net/2433/253413.

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付記する学位プログラム名: デザイン学大学院連携プログラム<br>Kyoto University (京都大学)<br>0048<br>新制・課程博士<br>博士(情報学)<br>甲第22577号<br>情博第714号<br>新制||情||122(附属図書館)<br>京都大学大学院情報学研究科社会情報学専攻<br>(主査)教授緒方広明, 教授守屋和幸, 教授黒田知宏<br>学位規則第4条第1項該当

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Wallin, Jonatan. "Optimization and personalization of a web service based on temporal information." Thesis, Umeå universitet, Institutionen för fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149712.

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Development in information and communication technology has increased the attention of personalization in the 21st century and the benefits to both marketers and customers are claimed to be many. The need to efficiently deliver personalized content in different web applications has increased the interest in the field of machine learning. In this thesis project, the aim is to develop a decision model that autonomously optimizes a commercial web service to increase the click through rate. The model should be based on previously collected data about previous usage of the web service. Different re

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Chatti, Mohamed Amine [Verfasser]. "Personalization in Technology Enhanced Learning : A Social Software Perspective / Mohamed Amine Chatti." Aachen : Shaker, 2010. http://d-nb.info/1084536196/34.

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Vu, Manh Vu Duy. "Online Education : A study about students motivations and personalization." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43479.

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Because of the pandemic Coronavirus and proposals from the Swedish Public Health Agency, schools in Sweden had to adjust their teaching to distance education. The purpose of this study is to study students' motivation, understand what is inspiring for students, many students lack inspiration. Personalization in education, what kind of support could we give the students. Understand personalization from the students perspective different students need different help, make the students feel like they get personalized help. Hands on, how do we apply things that are theoretical to the practical. Th

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Astin, Paul. "Personalization in small learning communities supporting teachers' new roles in a conversion small learning community middle school /." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1693037431&sid=3&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Livres sur le sujet "Personalization of Learning"

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Daradoumis, Thanasis, Stavros N. Demetriadis, and Fatos Xhafa, eds. Intelligent Adaptation and Personalization Techniques in Computer-Supported Collaborative Learning. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28586-8.

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Daradoumis, Thanasis. Intelligent Adaptation and Personalization Techniques in Computer-Supported Collaborative Learning. Springer Berlin Heidelberg, 2012.

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Guido, Maria Grazia. The representation model of second language learning: A computer hypertext approach to sociolinguistic cognition and L2 personalization. Bulzoni, 1996.

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Ongaro, Christopher. Virtual Personalization: Technology-Mediated Interactions and the Opportunities for Engagement and Connection in a Blended Learning Program. [publisher not identified], 2021.

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Klasnja-Milićević, Aleksandra, Lakhmi C. Jain, Boban Vesin, Zoran Budimac, and Mirjana Ivanović. E-Learning Systems: Intelligent Techniques for Personalization. Springer London, Limited, 2016.

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Tadlaoui, Mouenis Anouar, and Mohamed Khaldi. Personalization and Collaboration in Adaptive E-Learning. IGI Global, 2019.

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Jain, Lakhmi C., Mirjana Ivanović, Aleksandra Klašnja-Milićević, Boban Vesin, and Zoran Budimac. E-Learning Systems: Intelligent Techniques for Personalization. Springer, 2016.

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Tadlaoui, Mouenis Anouar, and Mohamed Khaldi. Personalization and Collaboration in Adaptive E-Learning. IGI Global, 2019.

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Tadlaoui, Mouenis Anouar, and Mohamed Khaldi. Personalization and Collaboration in Adaptive E-Learning. IGI Global, 2019.

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Klasnja-Milićević, Aleksandra, Lakhmi C. Jain, Boban Vesin, Zoran Budimac, and Mirjana Ivanović. E-Learning Systems: Intelligent Techniques for Personalization. Springer International Publishing AG, 2018.

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Chapitres de livres sur le sujet "Personalization of Learning"

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Agarwal, Mayank, Mikhail Yurochkin, and Yuekai Sun. "Personalization in Federated Learning." In Federated Learning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96896-0_4.

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Schneider, Johannes, and Michalis Vlachos. "Personalization of Deep Learning." In Data Science – Analytics and Applications. Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32182-6_14.

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Konert, Johannes. "Game Adaptation and Personalization Support." In Interactive Multimedia Learning. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10256-6_5.

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Krumm, John, Rich Caruana, and Scott Counts. "Learning Likely Locations." In User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38844-6_6.

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Ley, Tobias, Barbara Kump, and Cornelia Gerdenitsch. "Scaffolding Self-directed Learning with Personalized Learning Goal Recommendations." In User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13470-8_9.

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Klašnja-Milićević, Aleksandra, Boban Vesin, Mirjana Ivanović, Zoran Budimac, and Lakhmi C. Jain. "Personalization Based on Learning Styles." In Intelligent Systems Reference Library. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41163-7_3.

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Essalmi, Fathi, Leila Jemni Ben Ayed, Mohamed Jemni, Kinshuk, and Sabine Graf. "Automating the E-learning Personalization." In Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39146-0_32.

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Santos, Olga C., Milos Kravcik, and Diana Pérez-Marín. "Personalization Approaches in Learning Environments." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28509-7_12.

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Sweta, Soni. "Personalization Based on Learning Preference." In Modern Approach to Educational Data Mining and Its Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4681-9_6.

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Osborne, Francesco. "A POV-Based User Model: From Learning Preferences to Learning Personal Ontologies." In User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38844-6_43.

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Actes de conférences sur le sujet "Personalization of Learning"

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Palimkar, Sneha S., Kannan Kunnathully, Srinivasarao Thota, Meeta Joshi, Afsha Imran Akkalkot, and Nidal Al Said. "Using Machine Learning to Enhance Personalization in E-Learning." In 2025 International Conference on Pervasive Computational Technologies (ICPCT). IEEE, 2025. https://doi.org/10.1109/icpct64145.2025.10939236.

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Hosain, Md Tanzib, Asif Zaman, Md Shahriar Sajid, Shadman Sakeeb Khan, and Shanjida Akter. "Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning." In 2023 4th International Conference on Data Analytics for Business and Industry (ICDABI). IEEE, 2023. http://dx.doi.org/10.1109/icdabi60145.2023.10629638.

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Zekry, Dina A., Khaled Nagaty, and Gerard T. McKee. "Enhancing Usability of Learning Management Systems through Personalization." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652822.

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Glushkova, Todorka Atanasova, Evgeni Vladimirov Valchev, and Irina Krasimirova Krasteva. "Personalization of Lifelong Learning in School Educational Platform." In 2024 International Conference on Information Technologies (InfoTech). IEEE, 2024. http://dx.doi.org/10.1109/infotech63258.2024.10701358.

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Khan, Ahmad Faraz, Xinran Wang, Qi Le, et al. "IP-FL: Incentive-Driven Personalization in Federated Learning." In 2025 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2025. https://doi.org/10.1109/ipdps64566.2025.00053.

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Xie, Chulin, De-An Huang, Wenda Chu, et al. "Perada: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.02250.

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Liu, Xiaonan, Tharmalingam Ratnarajah, Mathini Sellathurai, and Yonina C. Eldar. "Partial Model Pruning and Personalization for Wireless Federated Learning." In 2024 IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2024. http://dx.doi.org/10.1109/spawc60668.2024.10694371.

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Al Balushi, Maha Yaqoob, and Alia Salim Al Harthi. "Adaptive Learning Pathways: Advancing Personalization in Online Education Platforms." In 2024 2nd International Conference on Computing and Data Analytics (ICCDA). IEEE, 2024. https://doi.org/10.1109/iccda64887.2024.10867273.

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Li, Youpeng, Xinda Wang, Fuxun Yu, Lichao Sun, Wenbin Zhang, and Xuyu Wang. "FedCAP: Robust Federated Learning via Customized Aggregation and Personalization." In 2024 Annual Computer Security Applications Conference (ACSAC). IEEE, 2024. https://doi.org/10.1109/acsac63791.2024.00067.

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Zhao, Qianyi, Chen Qu, Cen Chen, Mingyuan Fan, and Yanhao Wang. "FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization." In 2024 IEEE 30th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2024. https://doi.org/10.1109/icpads63350.2024.00040.

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Rapports d'organisations sur le sujet "Personalization of Learning"

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Marienko, Maiia V., Yulia H. Nosenko, and Mariya P. Shyshkina. Personalization of learning using adaptive technologies and augmented reality. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4418.

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The research is aimed at developing the recommendations for educators on using adaptive technologies and augmented reality in personalized learning implementation. The latest educational technologies related to learning personalization and the adaptation of its content to the individual needs of students and group work are considered. The current state of research is described, the trends of development are determined. Due to a detailed analysis of scientific works, a retrospective of the development of adaptive and, in particular, cloud-oriented systems is shown. The preconditions of their ap

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Spirin, Oleg, and Mariia Shyshkina. Artificial Intelligence in Education and Educational Research: Challenges, Risks, and Prospects for Integration. Institute for Digitalisation of Education of the NAES of Ukraіne, 2025. https://doi.org/10.33407/lib.naes.id/eprint/745119.

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Artificial intelligence (AI) is playing an increasingly important role in education, contributing to the personalization of learning, the automation of administrative processes and educational research. The use of AI allows for increased learning efficiency, improved management of educational resources and improved analytics of educational data. However, the integration of AI into education is accompanied by challenges related, in particular, to technical limitations, pedagogical risks, ethical aspects and security issues. Future research should focus on the integration of AI and augmented and

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Talukder, Noor Mohammad, and Wahid bin Ahsan. The Impact of AI on Student Engagement in Virtual Learning within Bangladesh’s Higher Education Sector. Userhub, 2025. https://doi.org/10.58947/journal.jtgx34.

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This study examines the impact of Artificial Intelligence (AI) on student engagement in virtual learning environments within Bangladesh’s higher education sector, using a mixed-methods approach. The research integrates qualitative interviews with educators, students, and IT professionals, alongside a quantitative survey of 97 university students. Findings reveal that AI enhances academic performance by improving efficiency and simplifying complex tasks. However, concerns about over-reliance on AI, diminished critical thinking skills, and data privacy issues were prominent. Technical challenges

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Sierra Noakes, Sierra Noakes, Alison Shell, Alexis M. Murillo, et al. An Ethical and Equitable Vision of AI in Education: Learning Across 28 Exploratory Projects. Digital Promise, 2024. http://dx.doi.org/10.51388/20.500.12265/232.

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This report shares the learnings across 28 exploratory projects from teams across K-12 school districts, nonprofits, and nonprofit and for-profit edtech companies, leveraging AI to support numerous goals across K-12 educational settings. Through this report, we aim to highlight the early successes of AI, surface the key barriers that call for cross-disciplinary and collective problem-solving, and consider the potential for each sector to drive forward an equitable future for AI in education. Preliminary findings from these projects show early evidence of AI’s effectiveness in various tasks, in

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Osadchyi, Viacheslav V., Hanna B. Varina, Kateryna P. Osadcha, et al. The use of augmented reality technologies in the development of emotional intelligence of future specialists of socionomic professions under the conditions of adaptive learning. CEUR Workshop Proceedings, 2020. http://dx.doi.org/10.31812/123456789/4633.

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In modern conditions, innovative augmented reality technologies are actively developing, which are widespread in many areas of human activity. Introduction of advanced developments in the process of professional training of future specialists of socionomic professions in the conditions of adaptive training, contributes to the implementation of the principles of a personalized approach and increase the overall level of competitiveness. The relevant scientific article is devoted to the theoretical and empirical analysis result of conducting a psychodiagnostic study on an innovative computer comp

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