DESAIN PEMBELAJARAN ADAPTIF BERBASIS PERSONALISASI KOGNITIF UNTUK MENDORONG PENDEKATAN DEEP LEARNING
ADAPTIVE LEARNING DESIGN BASED ON COGNITIVE PERSONALIZATION TO ENCOURAGE DEEP LEARNING APPROACHES
DOI:
https://doi.org/10.21070/pedagogia.v15i2.2232Keywords:
Adaptive Learning, Cognitive Personalization, Deep Learning, Cognitive Load, HOTSAbstract
General Background: The global educational shift requires movement from surface learning toward complex problem-solving supported by intelligent systems. Specific Background: Adaptive learning grounded in Cognitive Load Theory is increasingly deployed to address diverse working memory capacities through data-driven adjustment. Knowledge Gap: Current implementations remain dominated by behavioral personalization while overlooking internal cognitive dynamics during learning processes. Aims: This study synthesizes empirical evidence to examine mechanisms of cognitive personalization, evaluate its contribution to deep learning indicators, and identify pedagogical constraints through a Systematic Literature Review. Results: Analysis of 13 studies reveals that adaptive systems operate as cognitive architects by dynamically reducing extraneous cognitive load, fostering higher-order thinking skills, improving retention, and stabilizing learning emotions. However, challenges arise from dual-task interface complexity, limited teacher data literacy, infrastructure constraints, and ethical concerns regarding data privacy and algorithmic bias. Novelty: The study advances a Human-in-the-Loop framework integrating AI with fading strategies to prevent analytical dependency while maintaining learner autonomy. Implications: The findings extend Cognitive Load Theory through dynamic load management and highlight the necessity of reconstructing teacher competencies toward AI-Pedagogical Content Knowledge while encouraging future validation across diverse learner contexts.
Highlights
• Dynamic load reduction mechanisms support sustained conceptual understanding and retention
• Intelligent scaffolding increases higher-order reasoning but requires gradual assistance withdrawal
• Implementation barriers relate to interface complexity, educator readiness, and system accessibility
Keywords
Adaptive Learning; Cognitive Personalization; Deep Learning; Cognitive Load; HOTS
References
Ahmed, S., Rehman, H., Zaheer, M., & Pervez, N. (2025). The Impact of AI-powered Adaptive Learning Systems on Student Performance in Higher Education. Social Science Review Archives, 3(4). https://doi.org/10.70670/sra.v3i4.1103
Andayanie, L. M., Adhantoro, M. S., Purnomo, E., & Kurniaji, G. T. (2025). Implementation of deep learning in education: Towards mindful, meaningful, and joyful learning experiences. Journal of Deep Learning, 47–56. https://doi.org/10.23917/jdl.v1i1.11157
Ansari, S. R., & Qamari, I. N. (2025). Artificial intelligence and students’ cognitive learning outcomes with bibliometric and content analysis for future research agenda. In Discover Education (Vol. 4, Number 1). https://doi.org/10.1007/s44217-025-00865-0
Guo, Y. (2026). Bridging Cognitive Load Theory and Deep Q-Networks for Real-Time Difficulty Calibration in Automated Tutoring Systems. Multidisciplinary Research in Computing Information Systems, 6(1), 89–98. https://doi.org/10.71465/mrcis200
Halkiopoulos, C., & Gkintoni, E. (2024). Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis. In Electronics (Switzerland) (Vol. 13, Number 18). https://doi.org/10.3390/electronics13183762
Hefter, M. H., & Berthold, K. (2022). Active ingredients and factors for deep processing during an example-based training intervention. Learning Environments Research, 25(1). https://doi.org/10.1007/s10984-020-09347-6
Hu, Z. (2025). A method for generating personalized learning content based on AIGC. Sustainable Futures, 10. https://doi.org/10.1016/j.sftr.2025.101331
Hutabarat, E. J., Naibaho, L., & Rantung, D. A. (2023). Memahami Peran Pendidikan di Era Post Modern Melalui Pandangan John Dewey. Jurnal Kolaboratif Sains, 6(11). https://doi.org/10.56338/jks.v6i11.4403
Imron, A. (2025). Deep Learning Pedagogy Grounded in David Ausubel’s Learning Theory: A Literature Study. Almubin: Islamic Scientific Journal, 8(1). https://doi.org/10.51192/almubin.v8i2.2152
Karimulloh, Khaerudin, & Siregar, E. (2026). An AI-Driven Deep Learning Adaptive Curriculum Model (DNCL) for Indonesian Secondary Schools: Evidence from a Single-Site Intervention. Nusantara: Jurnal Pendidikan Indonesia, 6(1), 26–40. https://doi.org/doi.org/10.62491/njpi.2026.v6i1-3
Khotimah, H., Priyanto, E., & Ahmad, A. (2025). Joyful learning for enhancing pedagogical competence of Islamic education teachers in building meaningful and character-based learning. Jurnal Praktik Baik Pembelajaran Sekolah Dan Pesantren, 4(03), 129–138. https://doi.org/10.56741/pbpsp.v4i03.1147
Methley, A. M., Campbell, S., Chew-Graham, C., McNally, R., & Cheraghi-Sohi, S. (2014). PICO, PICOS and SPIDER: A comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. In BMC Health Services Research (Vol. 14, Number 1). https://doi.org/10.1186/s12913-014-0579-0
Naseer, F., Khan, M. N., Addas, A., Awais, Q., & Ayub, N. (2025). Game Mechanics and Artificial Intelligence Personalization: A Framework for Adaptive Learning Systems. Education Sciences, 15(3). https://doi.org/10.3390/educsci15030301
OECD. (2023). PISA 2022 Result (Colume I): The State of Learning and Equity in Education.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. In BMJ (Vol. 372). https://doi.org/10.1136/bmj.n71
Ramesh, K., Thomas, C. A., Osei-Asiamah, J., Pagidipati, B., Muniyandy, E., Reddy, B. V. S., & El-Ebiary, Y. A. B. (2025). Cognitive Load Optimization in Digital (ESL) Learning: A Hybrid BERT and FNN Approach for Adaptive Content Personalization. International Journal of Advanced Computer Science and Applications, 16(4). https://doi.org/10.14569/IJACSA.2025.0160457
Ruan, S., & Lu, K. (2025). Adaptive deep reinforcement learning for personalized learning pathways: A multimodal data-driven approach with real-time feedback optimization. Computers and Education: Artificial Intelligence, 9. https://doi.org/10.1016/j.caeai.2025.100463
Saputra, R. F. A., Ridha, M., & Sulaimon, J. T. (2025). Deep Learning Applications in Primary Education: A Systematic Literature Review of Emerging Trends, Challenges, and Opportunities. Jurnal Pendidikan Progresif, 15(3). https://doi.org/10.23960/jpp.v15i3.pp1785-1810
Sweller, J. (2020). Cognitive load theory and educational technology. Educational Technology Research and Development, 68(1). https://doi.org/10.1007/s11423-019-09701-3
Tong, C., & Ren, C. (2025). Deep knowledge tracing and cognitive load estimation for personalized learning path generation using neural network architecture. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-10497-x
Troussas, C., Krouska, A., Mylonas, P., & Sgouropoulou, C. (2025). Personalized Instructional Strategy Adaptation Using TOPSIS: A Multi-Criteria Decision-Making Approach for Adaptive Learning Systems. Information (Switzerland), 16(5). https://doi.org/10.3390/info16050409
Ullah, N., Akram, S., & Hanif, A. (2025). AI-Powered Adaptive Learning Systems: Personalizing Education Through Real-Time Data Analytics and Student Behavior Modeling. Computing and Applications Reviews, 2(01), 41–51. https://doi.org/10.64035/car.01.2025.17
Xiao, Y., & Watson, M. (2019). Guidance on Conducting a Systematic Literature Review. In Journal of Planning Education and Research (Vol. 39, Number 1). https://doi.org/10.1177/0739456X17723971
Yi, Y., & Wu, X. (2025). Cognitive Load Early Warning Based on Behavioral Data: A Single-Agent Approach for Dynamically Adjusting Task Difficulty. Proceedings of the 2025 2nd International Conference on Big Data Analytics and Artificial Intelligence Application, 140–144. https://doi.org/10.1145/3788108.3788518
Yu, M. (2024). Application of an Artificial Intelligence-based adaptive learning system to chinese language education in universities. Proceedings of the 2024 International Symposium on Artificial Intelligence for Education, 416–421. https://doi.org/doi.org/10.1145/3700297.3700368
Zheng, Y., Wang, D., Zhang, J., Li, Y., Xu, Y., Zhao, Y., & Zheng, Y. (2025). A unified framework for personalized learning pathway recommendation in e-learning contexts. Education and Information Technologies, 30(6). https://doi.org/10.1007/s10639-024-13045-8
Downloads
Published
How to Cite
License
Copyright (c) 2026 Ibnu Rizqil Maula, Tri Astuti
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
