Articles
Published 2024-01-30
Keywords
- individualisation of learning, personalised educational programmes, artificial intelligence, machine learning, data analysis.
How to Cite
Zavalevskyi, Y., Kyrilenko, S., Kijan, O., Bessarab, N., & Mosyakova, I. (2024). The role of AI in individualizing learning and creating personalized programs. Amazonia Investiga, 13(73), 200–208. https://doi.org/10.34069/AI/2024.73.01.16
Abstract
This article analyses the technical and methodological aspects of implementing individualised curricula using artificial intelligence in the educational process. The study deals in detail with the issues of technological infrastructure, data collection, and processing, as well as the integration of individualised programmes with existing educational platforms. The methodological aspect of the article includes an analysis of methods for determining the needs and capabilities of each student and the development of a methodology for assessing the success of individualised programmes. The study aims to uncover the potential and benefits associated with the utilization of personalized programs in contemporary education. This is done with the intention of enhancing the overall learning experience and attaining superior outcomes for every individual student and pupil. Future areas of research include further development of technical solutions for individualised programmes, studying methodological approaches to adapting programmes to the needs of different categories of student, and developing ethical standards for protecting personal data in education. This article will be useful for teachers, higher education institutions, researchers, and anyone interested in using artificial intelligence to individualise learning and improve education. It offers important discoveries and practical recommendations for implementing individualised programmes in the educational process.
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References
Abed Ibrahim, L., & Fekete, I. (2019). What machine learning can tell us about the role of language dominance in the diagnostic accuracy of German LITMUS non-word and sentence repetition tasks. Frontiers in Psychology, 9, 2757. https://doi.org/10.3389/fpsyg.2018.02757
Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429. https://doi.org/10.30935/cedtech/13152
Adjerid, I., & Kelley, K. (2018). Big data in psychology: A framework for research advancement. The American Psychologist, 73(7), 899-917. https://doi.org/10.1037/amp0000190
Alawi, F. (2023). Artificial intelligence: The future might already be here. Oral Surgery Oral Medicine Oral Pathology Oral Radiology, 12, S2212-4403(23)00003-2. https://doi.org/10.1016/j.oooo.2023.01.002
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: a review and synthesis. Telemat. Inform, 37, 13-49. https://doi.org/10.1016/j.tele.2019.01.007
Ali, S. (2022). The effectiveness of immersive technologies for future professional education. Futurity Education, 2(2), 13-21. https://doi.org/10.57125/FED/2022.10.11.25
Alzain, H. A. (2019). The role of social networks in supporting collaborative e-learning based on Connectivism Theory among students of PNU. Turkish Online Journal of Distance Education, 20(2), 46-63. https://doi.org/10.17718/tojde.557736
Bahri, S., & Lestari, E. T. (2021). Implementation of human-machine friendship learning in the new-normal era. Journal of Education and Learning (EduLearn), 15(2), 291-296. https://doi.org/10.11591/edulearn.v15i2.18404
Chanysheva, A., Krasnov, Y., Pozharova, O., Potopakhina, O., & Nanieva, M. (2023). International standards of rights in the field of social security. Amazonia Investiga, 12(65), 77-83. https://doi.org/10.34069/AI/2023.65.05.8
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: a review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510
Chen, N.-S., Yin, C., Isaias, P., & Psotka, J. (2020). Educational big data: extracting meaning from data for smart education. Interact. Learn. Environ, 28(2), 142-147. https://doi.org/10.1080/10494820.2019.1635395
Cheng, K.-H., & Tsai, C.-C. (2019). A case study of immersive virtual field trips in an elementary classroom: students’ learning experience and teacher-student interaction behaviors. Comp. Educ., 140, 103600. https://doi.org/10.1016/j.compedu.2019.103600
Cutumisu, M., & Guo, Q. (2019). Using topic modeling to extract pre-service teachers’ understandings of computational thinking from their coding reflections. IEEE Transactions on Education, 62(4), 325-332. https://doi.org/10.1109/te.2019.2925253
Daniel, B. K. (2019). Big data and data science: a critical review of issues for educational research. British Journal of Educational Technology, 50(1), 101-113. https://doi.org/10.1111/bjet.12595
Etzrodt, K., Gentzel, P., Utz, S., & Engesser, S. (2022). Human-machine-communication: introduction to the special issue. Journalism, 67(4), 439-448. https://doi.org/10.1007/s11616-022-00754-8
Flindt, N., Magarian, M., & Hohl, G. (2021). The creation of brain-stimulating Online Learning content for a young migrant and refugee project. Muallim Journal of Social Science and Humanities, 5(2), 1-11. https://doi.org/10.33306/mjssh/116
Gierl, M. J., & Lai, H. (2018). Using automatic item generation to create solutions and rationales for computerized formative testing. Appl. Psychol. Measurement, 42(1), 42-57. https://doi.org/10.1177/0146621617726788
Goksel, N., & Bozkurt, A. (2019). Artificial intelligence in education: Current insights and future perspectives. In Handbook of Research on Learning in the Age of Transhumanism (pp. 224–236). IGI Global. https://doi.org/10.4018/978-1-5225-8431-5.ch014
Hendradi, P., Abd Ghani, M. K., Mahfuzah, S. N., Yudatama, U., Prabowo, N. A., & Widyanto, R. A. (2020). Artificial intelligence influence in education 4.0 to architecture cloud based e-learning system. International Journal of Artificial Intelligence Research, 4(1), 30-38. https://doi.org/10.29099/ijair.v4i1.109
Hew, K. F., Lan, M., Tang, Y., Jia, C., & Lo, C. K. (2019). Where is the “theory” within the field of educational technology research? British Journal of Educational Technology: Journal of the Council for Educational Technology, 50(3), 956-971. https://doi.org/10.1111/bjet.12770
Huang, A. Y. Q., Lu, O. H. T., Huang, J. C. H., Yin, C. J., & Yang, S. J. H. (2020). Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs. Interactive Learning Environments, 28(2), 206–230. https://doi.org/10.1080/10494820.2019.1636086
Kumar Basak, S., Wotto, M., & Bélanger, P. (2018). E-learning, M-learning and D-learning: Conceptual definition and comparative analysis. E-Learning and Digital Media, 15(4), 191-216. https://doi.org/10.1177/2042753018785180
Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S. J. H., Ogata, H., … & Tsai, C.-C. (2020). Challenges and future directions of big data and artificial intelligence in education. Frontiers in Psychology, 11, 580820. https://doi.org/10.3389/fpsyg.2020.580820
Niemi, M., Manhica, H., Gunnarsson, D., Ståhle, G., Larsson, S., & Saboonchi, F. (2019). A scoping review and conceptual model of social participation and mental health among refugees and asylum seekers. International Journal of Environmental Research and Public Health, 16(20), 4027. https://doi.org/10.3390/ijerph16204027
Rakhimov, T., & Mukhamediev, M. (2022). Peculiarities of the implementation of the principles of the education of the future analysis of the main dilemmas. Futurity Education, 2(3), 4-13. https://doi.org/10.57125/FED/2022.10.11.29
Razaulla, S. M., Pasha, M., & Farooq, M. U. (2022). Integration of machine learning in education: challenges, issues and trends. In Machine learning and internet of things for societal issues (pp. 23-34). Singapore: Springer Nature Singapore. https://link.springer.com/chapter/10.1007/978-981-16-5090-1_2
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a selfdetermination theory perspective: Definitions, theory, practices, and future directions. Contemporary educational psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
Salnyk, I., Grin, L., Yefimov, D., & Beztsinna, Z. (2023). The Future of Higher Education: Implementation of Virtual and Augmented Reality in the Educational Process. Futurity Education, 3(3), 46-61. https://doi.org/10.57125/FED.2023.09.25.03
Türkmen, H. (2023). A Comparative Analysis of Karplus Learning Cycle Model and Ausubel Meaningful Learning Model on Children’s Environmental Pollution Cognition. Futurity Education, 3(3), 106-129. https://doi.org/10.57125/FED.2023.09.25.06
Xie, P., Cao, Q., Li, X., Yang, Y., & Yu, L. (2022). The effects of social participation on social integration. Frontiers in Psychology, 13, 919592. https://doi.org/10.3389/fpsyg.2022.919592
Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429. https://doi.org/10.30935/cedtech/13152
Adjerid, I., & Kelley, K. (2018). Big data in psychology: A framework for research advancement. The American Psychologist, 73(7), 899-917. https://doi.org/10.1037/amp0000190
Alawi, F. (2023). Artificial intelligence: The future might already be here. Oral Surgery Oral Medicine Oral Pathology Oral Radiology, 12, S2212-4403(23)00003-2. https://doi.org/10.1016/j.oooo.2023.01.002
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: a review and synthesis. Telemat. Inform, 37, 13-49. https://doi.org/10.1016/j.tele.2019.01.007
Ali, S. (2022). The effectiveness of immersive technologies for future professional education. Futurity Education, 2(2), 13-21. https://doi.org/10.57125/FED/2022.10.11.25
Alzain, H. A. (2019). The role of social networks in supporting collaborative e-learning based on Connectivism Theory among students of PNU. Turkish Online Journal of Distance Education, 20(2), 46-63. https://doi.org/10.17718/tojde.557736
Bahri, S., & Lestari, E. T. (2021). Implementation of human-machine friendship learning in the new-normal era. Journal of Education and Learning (EduLearn), 15(2), 291-296. https://doi.org/10.11591/edulearn.v15i2.18404
Chanysheva, A., Krasnov, Y., Pozharova, O., Potopakhina, O., & Nanieva, M. (2023). International standards of rights in the field of social security. Amazonia Investiga, 12(65), 77-83. https://doi.org/10.34069/AI/2023.65.05.8
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: a review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510
Chen, N.-S., Yin, C., Isaias, P., & Psotka, J. (2020). Educational big data: extracting meaning from data for smart education. Interact. Learn. Environ, 28(2), 142-147. https://doi.org/10.1080/10494820.2019.1635395
Cheng, K.-H., & Tsai, C.-C. (2019). A case study of immersive virtual field trips in an elementary classroom: students’ learning experience and teacher-student interaction behaviors. Comp. Educ., 140, 103600. https://doi.org/10.1016/j.compedu.2019.103600
Cutumisu, M., & Guo, Q. (2019). Using topic modeling to extract pre-service teachers’ understandings of computational thinking from their coding reflections. IEEE Transactions on Education, 62(4), 325-332. https://doi.org/10.1109/te.2019.2925253
Daniel, B. K. (2019). Big data and data science: a critical review of issues for educational research. British Journal of Educational Technology, 50(1), 101-113. https://doi.org/10.1111/bjet.12595
Etzrodt, K., Gentzel, P., Utz, S., & Engesser, S. (2022). Human-machine-communication: introduction to the special issue. Journalism, 67(4), 439-448. https://doi.org/10.1007/s11616-022-00754-8
Flindt, N., Magarian, M., & Hohl, G. (2021). The creation of brain-stimulating Online Learning content for a young migrant and refugee project. Muallim Journal of Social Science and Humanities, 5(2), 1-11. https://doi.org/10.33306/mjssh/116
Gierl, M. J., & Lai, H. (2018). Using automatic item generation to create solutions and rationales for computerized formative testing. Appl. Psychol. Measurement, 42(1), 42-57. https://doi.org/10.1177/0146621617726788
Goksel, N., & Bozkurt, A. (2019). Artificial intelligence in education: Current insights and future perspectives. In Handbook of Research on Learning in the Age of Transhumanism (pp. 224–236). IGI Global. https://doi.org/10.4018/978-1-5225-8431-5.ch014
Hendradi, P., Abd Ghani, M. K., Mahfuzah, S. N., Yudatama, U., Prabowo, N. A., & Widyanto, R. A. (2020). Artificial intelligence influence in education 4.0 to architecture cloud based e-learning system. International Journal of Artificial Intelligence Research, 4(1), 30-38. https://doi.org/10.29099/ijair.v4i1.109
Hew, K. F., Lan, M., Tang, Y., Jia, C., & Lo, C. K. (2019). Where is the “theory” within the field of educational technology research? British Journal of Educational Technology: Journal of the Council for Educational Technology, 50(3), 956-971. https://doi.org/10.1111/bjet.12770
Huang, A. Y. Q., Lu, O. H. T., Huang, J. C. H., Yin, C. J., & Yang, S. J. H. (2020). Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs. Interactive Learning Environments, 28(2), 206–230. https://doi.org/10.1080/10494820.2019.1636086
Kumar Basak, S., Wotto, M., & Bélanger, P. (2018). E-learning, M-learning and D-learning: Conceptual definition and comparative analysis. E-Learning and Digital Media, 15(4), 191-216. https://doi.org/10.1177/2042753018785180
Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S. J. H., Ogata, H., … & Tsai, C.-C. (2020). Challenges and future directions of big data and artificial intelligence in education. Frontiers in Psychology, 11, 580820. https://doi.org/10.3389/fpsyg.2020.580820
Niemi, M., Manhica, H., Gunnarsson, D., Ståhle, G., Larsson, S., & Saboonchi, F. (2019). A scoping review and conceptual model of social participation and mental health among refugees and asylum seekers. International Journal of Environmental Research and Public Health, 16(20), 4027. https://doi.org/10.3390/ijerph16204027
Rakhimov, T., & Mukhamediev, M. (2022). Peculiarities of the implementation of the principles of the education of the future analysis of the main dilemmas. Futurity Education, 2(3), 4-13. https://doi.org/10.57125/FED/2022.10.11.29
Razaulla, S. M., Pasha, M., & Farooq, M. U. (2022). Integration of machine learning in education: challenges, issues and trends. In Machine learning and internet of things for societal issues (pp. 23-34). Singapore: Springer Nature Singapore. https://link.springer.com/chapter/10.1007/978-981-16-5090-1_2
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a selfdetermination theory perspective: Definitions, theory, practices, and future directions. Contemporary educational psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
Salnyk, I., Grin, L., Yefimov, D., & Beztsinna, Z. (2023). The Future of Higher Education: Implementation of Virtual and Augmented Reality in the Educational Process. Futurity Education, 3(3), 46-61. https://doi.org/10.57125/FED.2023.09.25.03
Türkmen, H. (2023). A Comparative Analysis of Karplus Learning Cycle Model and Ausubel Meaningful Learning Model on Children’s Environmental Pollution Cognition. Futurity Education, 3(3), 106-129. https://doi.org/10.57125/FED.2023.09.25.06
Xie, P., Cao, Q., Li, X., Yang, Y., & Yu, L. (2022). The effects of social participation on social integration. Frontiers in Psychology, 13, 919592. https://doi.org/10.3389/fpsyg.2022.919592
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