[1] Alam, A. (2021, November 26-27). Possibilities and apprehensions in the landscape of artificial intelligence in education [Paper presentation]. Proceedings of the International Conference on Computational Intelligence and Computing Applications, Nagpur, India. https://doi.org/10.1109/ICCICA52458.2021.9697272
[2] Arvidsson, T. S., & Kuhn, D. (2021). Realizing the full potential of individualizing learning. Contemporary Educational Psychology, 65, 101960. https://doi.org/10.1016/j.cedpsych.2021.101960
[3] Cook, C. R., Kilgus, S. P., & Burns, M. K. (2018). Advancing the science and practice of precision education to enhance student outcomes. Journal of School Psychology, 66, 4–10. https://doi.org/10.1016/j.jsp.2017.11.004
[4] Cummings, R., Maddux, C. D., & Casey, J. (2011). Individualized transition planning for students with learning disabilities. The Career Development Quarterly, 49(1), 60–72. https://doi.org/10.1002/j.2161-0045.2000.tb00751.x
[5] Daghestani, L. F., Ibrahim, L. F., Al‐Towirgi, R. S., & Salman, H. A. (2020). Adapting gamified learning systems using educational data mining techniques. Computer Applications in Engineering Education, 28(3), 568–589. https://doi.org/10.1002/cae.22227
[6] Dogan, M. E., Goru Dogan, T., & Bozkurt, A. (2023). The use of artificial intelligence (AI) in online learning and distance education processes: A systematic review of empirical studies. Applied Sciences, 13(5), 3056. https://doi.org/10.3390/app13053056
[7] Emerson, A., Cloude, E. B., Azevedo, R., & Lester, J. (2020). Multimodal learning analytics for game‐based learning. British Journal of Educational Technology, 51(5), 1505–1526. https://doi.org/10.1111/bjet.12992
[8] Frey, J. R. (2019). Assessment for special education: Diagnosis and placement. Annals of the American Academy of Political and Social Science, 683(1), 149–161. https://doi.org/10.1177/0002716219841352
[9] Garcias, A. P., Ferrer, G. T., Moral, S. V., & Mesquida, A. D. (2022). Flexible learning itineraries in digital environments for personalised learning in teacher training. Revista Iberoamericana de Educación a Distancia, 25(2), 173–188. https://doi.org/10.5944/ried.25.2.32326
[10] Goldschmid, B., & Goldschmid, M. L. (1973). Modular instruction in higher education: A review. Higher Education, 2(1), 15–32. https://doi.org/10.1007/BF00162534
[11] Grannäs, J., & Stavem, S. M. (2021). Transitions through remodelling teaching and learning environments. Education Inquiry, 12(3), 266–281. https://doi.org/10.1080/20004508.2020.1856564
[12] Hardman, M. L., & Dawson, S. (2008). The impact of federal public policy on curriculum and instruction for students with disabilities in the general classroom. Preventing School Failure: Alternative Education for Children and Youth, 52(2), 5–11. https://doi.org/10.3200/PSFL.52.2.5-11
[13] Hofer, S. I., Nistor, N., & Scheibenzuber, C. (2021). Online teaching and learning in higher education: Lessons learned in crisis situations. Computers in Human Behavior, 121, 106789. https://doi.org/10.1016/j.chb.2021.106789
[14] Imaniah, I., & Fitria, N. (2018). Inclusive education for students with disability. SHS Web of Conferences, 42, 00039. https://doi.org/10.1051/shsconf/20184200039
[15] Ingavélez-Guerra, P., Robles-Bykbaev, V. E., Perez-Muñoz, A., Hilera-González, J., & Otón-Tortosa, S. (2022). Automatic adaptation of open educational resources: An approach from a multilevel methodology based on students' preferences, educational special needs, artificial intelligence and accessibility metadata. IEEE Access, 10, 9703–9716. https://doi.org/10.1109/ACCESS.2021.3139537
[16] Joseph, O. B., & Uzondu, N. C. (2024). Integrating AI and machine learning in STEM education: Challenges and opportunities. Computer Science & IT Research Journal, 5(8), 1732–1750. https://doi.org/10.51594/csitrj.v5i8.1379
[17] Kaput, K. (2018). Evidence for student-centered learning. Education Evolving, 1–26. URL: https://files.eric.ed.gov/fulltext/ED581111.pdf
[18] Kem, D. (2022). Personalised and adaptive learning: Emerging learning platforms in the era of digital and smart learning. International Journal of Social Science and Human Research, 5(2), 385–391. https://doi.org/10.47191/ijsshr/v5-i2-02
[19] Lindner, K. T., & Schwab, S. (2020). Differentiation and individualisation in inclusive education: A systematic review and narrative synthesis. International Journal of Inclusive Education, 29(1), 1–2. https://doi.org/10.1080/13603116.2020.1813450
[20] Luan, H., & Tsai, C. C. (2021). A review of using machine learning approaches for precision education. Educational Technology & Society, 24(1), 250–266. https://www.jstor.org/stable/26977871
[21] MacLeod, K., Causton, J. N., Radel, M., & Radel, P. (2017). Rethinking the individualized education plan process: Voices from the other side of the table. Disability & Society, 32(3), 381–400. https://doi.org/10.1080/09687599.2017.1294048
[22] Mikić, V., Ilić, M., Kopanja, L., & Vesin, B. (2022). Personalisation methods in e‐learning—A literature review. Computer Applications in Engineering Education, 30(6), 1931–1958. https://doi.org/10.1002/cae.22566
[23] Moltudal, S., Høydal, K. L., & Krumsvik, R. J. (2020). Glimpses into real-life introduction of adaptive learning technology: A mixed methods research approach to personalised pupil learning. Designs for Learning, 12(1), 13–28. http://doi.org/10.16993/dfl.138
[24] Moreno-Guerrero, A. J., Aznar-Díaz, I., Cáceres-Reche, P., & Alonso-García, S. (2020). E-learning in the teaching of mathematics: An educational experience in adult high school. Mathematics, 8(5), 840. https://doi.org/10.3390/math8050840
[25] Papadopoulos, D. (2023). Individualising processes in adult education research: A literature review. International Journal of Lifelong Education, 42(1), 8–21. https://doi.org/10.1080/02601370.2022.2135141
[26] Rak-Młynarska, E. (2022). Analysis of trends in the development of the educational environment: Education of the future. Futurity Education, 2(2), 4–13. https://doi.org/10.57125/FED/2022.10.11.24
[27] Saha, B., & Adhikari, A. (2023). The Montessori approach to the teaching-learning process. International Journal of Indian Psychology, 11(3), 574–578. https://doi.org/10.25215/1103.054
[28] Schipper, T., Goei, S. L., de Vries, S., & van Veen, K. (2017). Professional growth in adaptive teaching competence as a result of Lesson Study. Teaching and Teacher Education, 68, 289–303. https://doi.org/10.1016/j.tate.2017.09.015
[29] Schwarcz, D., & Farganis, D. (2017). The impact of individualized feedback on law student performance. Journal of Legal Education, 67(1), 139–175. URL: https://scholarship.law.umn.edu/faculty_articles/644?utm_source=scholarship.law.umn.edu%2Ffaculty_articles%2F644&utm_medium=PDF&utm_campaign=PDFCoverPages
[30] Vanslambrouck, S., Zhu, C., Pynoo, B., Lombaerts, K., Tondeur, J., & Scherer, R. (2019). A latent profile analysis of adult students' online self-regulation in blended learning environments. Computers in Human Behavior, 99, 126–136. https://doi.org/10.1016/j.chb.2019.05.021