Adaptive Deep Learning eXperience (ADLX) and Adaptive Curriculum: The Foundation of Modern Learning for Inclusive and Effective Education in the Digital Age
DOI:
10.29303/jppipa.v11i9.12690Published:
2025-09-25Downloads
Abstract
This study aims to explore the implementation of ADLX and adaptive curriculum as a foundation for the development of modern learning at the secondary education level. Using a descriptive qualitative approach, this study involved interviews with teachers at a foundation with junior high school, senior high school, and vocational high school levels. The researcher also directly observed the learning process in the classroom and analysed learning documentation such as lesson plans, syllabi, and evaluation records. The findings indicate that the implementation of ADLX within the framework of an adaptive curriculum is capable of creating a more active, personalised, and participatory learning environment, leading to student engagement in thinking, exploratory, and reflective processes. Additionally, the adaptive curriculum allows teachers to tailor teaching strategies to students' needs, resulting in more optimal learning outcomes. These findings contribute to the development of future learning models that not only rely on technology but also place students at the centre of the educational process. With the right approach, ADLX and the adaptive curriculum have great potential to drive educational transformation toward greater equity, relevance, and meaning in the digital age.
Keywords:
Adaptive curriculum daptive Deep Learning Experience (ADLX) Inclusive education Modern LearningReferences
Ajani, O. A. (2024). Enhancing Pre-Service Teacher Education: Crafting a Technology-Responsive Curriculum for Modern Classrooms and Adaptive Learners. Research in Educational Policy and Management, 6(2), 209–229. https://doi.org/10.46303/repam.2024.32
Almasri, Z., Bahgat, M., Seddek, A., & Elsafty, A. (2024). Maximizing the Benefits of ChatGPT with FIRST-ADLX Framework: Promoting Responsible, Ethical, and Impactful AI Integration in Education. Journal of Education and Training Studies, 12(4), 22. https://doi.org/10.11114/jets.v12i4.6933
Bahgat, M., Almasri, Z., Elsafty, A., & Seddek, A. (2024). Enhancing Team-Based Learning by Moderating FIRST-ADLX Framework inTeacher Professional Development. Journal of Education and Training Studies, 12(2), 87–105. Retrieved from https://shorturl.asia/jsbtE
Bradley, E. G., & Kendall, B. (2014). A Review of Computer Simulations in Teacher Education. Journal of Educational Technology Systems, 43(1), 3–12. https://doi.org/10.2190/ET.43.1.b
Chukwu, C. O., & Cletus, I. (2025). Exploring the Effectiveness of AI-Driven Adaptive Learning Systems in Science Education, Impact on Student Engagement. AJSTME, 11(2), 60–71. Retrieved from https://www.ajstme.com.ng/admin/img/paper/Paper 8.pdf
Cruz, C. D., & A, R. (2023). Assessment of The Adaptive Learning System Implementation in Selected Private School: Basis For Enrichment. Cosmos An International Journal of Art and Higher Education, 12(1), 144–156. https://doi.org/10.46360/cosmos.ahe.520231011
El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students’ engagement. International Journal of Educational Technology in Higher Education, 18(1), 53. https://doi.org/10.1186/s41239-021-00289-4
Gligorea, I., Cioca, M., Oancea, R., Gorski, A. T., Gorski, H., & Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences, 13(12). https://doi.org/10.3390/educsci13121216
Hartini, D., & Suherman, U. (2024). Management Of Active Deep Learner Experience Training In Improving Learning Quality. Journal of Islamic Education Management. https://doi.org/10.15575/aim.v2i2.37991
Hummel, H. G. K., Nadolski, R. J., Eshuis, J., Slootmaker, A., & Storm, J. (2021). Serious game in introductory psychology for professional awareness: Optimal learner control and authenticity. British Journal of Educational Technology, 52(1), 125–141. https://doi.org/10.1111/bjet.12960
Jansen, T., Meyer, J., Wigfield, A., & Moeller, J. (2022). Which student and instructional variables are most strongly related to academic motivation in K-12 education? A systematic review of meta-analyses. Psychological Bulletin, 148(1–2), 1. https://doi.org/10.1037/bul0000354
Johar, N. A., Kew, S. N., Tasir, Z., & Koh, E. (2023). Learning analytics on student engagement to enhance students’ learning performance: A systematic review. Sustainability, 15(10), 7849. https://doi.org/10.3390/su15107849
Johnson, L., Becker, S. A., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016). NMC horizon report: 2016 higher education edition. The New Media Consortium.
Kerimbayeva, B. T., Niyazova, G. Z., Meirbekov, A. K., Kibishov, A. T., & Usembayeva, I. B. (2024). A network communicative culture for future teachers: development of digital literacy and communicative competence. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2024.2363678
Mark, N. S. J., & Bernadeth, L. (2024). Concept Analysis of Adaptive Learning Strategy in English Language Teaching (ALS-ELT. International Journal of Social Sciences and English Literature, 8, 45–56. https://doi.org/10.55220/2576683x.v8.231
Munadirin, A., Muslim, R., & Fatah, Z. (2023). Transformation of PAI Learning Through Approaches to Active Deep Learning Experience (ADLX) In The Digital Era. Nadwa: Jurnal Pendidikan Islam, 17(2), 185–202. https://doi.org/10.21580/nw.2023.17.2.26745
Ng, J., Lei, L., Iseli-Chan, N., Li, J., Siu, F., Chu, S., & Hu, X. (2020). Non-repository Uses of Learning Management System through Mobile Access. Journal of Educational Technology Development and Exchange, 13(1), 1–20. https://doi.org/10.18785/jetde.1301.01
Nkomo, L. M., Daniel, B. K., & Butson, R. J. (2021). Synthesis of student engagement with digital technologies: a systematic review of the literature. International Journal of Educational Technology in Higher Education, 18(1), 34. https://doi.org/10.1186/s41239-021-00270-1
Plooy, E., Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10(21). https://doi.org/10.1016/j.heliyon.2024.e39630
Puspitasari, M. (2024). Navigating classroom challenges and curriculum changes: A qualitative study of an English Teacher’s journey in the Indonesian education system. Power and Education. https://doi.org/10.1177/17577438241275799
Siemens, G. (2014). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851
Stadin, M., Nordin, M., Broström, A., Magnusson Hanson, L. L., Westerlund, H., & Fransson, E. I. (2021). Technostress operationalised as information and communication technology (ICT) demands among managers and other occupational groups – Results from the Swedish Longitudinal Occupational Survey of Health (SLOSH. Computers in Human Behavior, 114, 106486. https://doi.org/10.1016/j.chb.2020.106486
Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2025). AI-driven adaptive learning for sustainable educational transformation. Sustainable Development, 33(2), 1921–1947. https://doi.org/10.1002/sd.3221
Taghap, D. O., & Pabalan, A. P. (2025). Understanding Challenges in The Implementation of Inclusive Education Through The Lens of Educational Management. Ignatian International Journal for Multidisciplinary Research, 3. https://doi.org/10.5281/zenodo.15161782
Tan, L. Y., Hu, S., Yeo, D. J., & Cheong, K. H. (2025). Artificial intelligence-enabled adaptive learning platforms: A review. In Computers and Education: Artificial Intelligence (Vol. 9). Elsevier B.V. https://doi.org/10.1016/j.caeai.2025.100429
Valeri, C., Quinzi, V., Di Giandomenico, D., Fani, E., Leonardi, R., & Marzo, G. (2023). Teledentistry: A bibliometric analysis of the scientific publication’s trend. Digital Health, 9, 20552076231204748. https://doi.org/10.1177/20552076231204747
Zhou, M., & Zhang, X. (2019). Online social networking and subjective well-being: Mediating effects of envy and fatigue. Computers & Education, 140, 103598. https://doi.org/10.1016/j.compedu.2019.103598
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