Main Article Content
Abstract
This research develops an intelligent cognitive load regulation framework for digital learning environments in the context of educational policy reforms. After China's Double Reduction Policy took effect, tutorial-concentrated schooling evolved into technology-facilitated learning, putting unimaginable cognitive burdens on students. In response, the research combines cognitive load theory with adaptive technologies to resolve these issues through real-time recognition of cognitive states and personalized interventions. Based on the mixed-methods design with 320 Dongcheng District students, the research uses established measures such as NASA-TLX adapted to e-learning environments to assess multidimensional patterns of cognitive load. The smart regulation system shows significant efficacy with lower socioeconomic students posting 15.3-point improvements in academic scores, task accomplishment rates enhanced by 32%, and the level of cognitive loads decreased by 23.1% on average across various types of learners. The system can recognize with 87.3% accuracy and respond in 234 milliseconds, thus facilitating timely interventions. Self-paced review activities yield 91.2% success rates, while collaborative tasks remain problematic at 68.4% success rates. The results extend cognitive load theory with dynamic adaptation capacities needed for self-managed digital learning. The present study provides evidence-based practice to maximize cognitive experiences of e-learning, facilitating education equity objectives while developing core self-regulated learning skills in post-reform education systems.
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References
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References
A. Skulmowski and K. M. Xu, "Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load," Educational psychology review, vol. 34, no. 1, pp. 171-196, 2022. DOI: https://doi.org/10.1007/s10648-021-09624-7
A.-L. Le Cunff et al., "Cognitive load and neurodiversity in online education: a preliminary framework for educational research and policy," in Frontiers in Education, 2025, vol. 9: Frontiers Media SA, p. 1437673. DOI: https://doi.org/10.3389/feduc.2024.1437673
M. BĂNUȚ and D. ANDRONACHE, "Students' cognitive load in online education, under the lens of learning theories," Studia Universitatis Babes-Bolyai, Psychologia-Paedagogia, vol. 68, no. 2, 2023.
O. Chen and S. Kalyuga, "Exploring factors influencing the effectiveness of explicit instruction first and problem-solving first approaches," European Journal of Psychology of Education, vol. 35, no. 3, pp. 607-624, 2020. DOI: https://doi.org/10.1007/s10212-019-00445-5
R. E. Mayer, "Evidence-based principles for how to design effective instructional videos," Journal of Applied Research in Memory and Cognition, vol. 10, no. 2, pp. 229-240, 2021. DOI: https://doi.org/10.1016/j.jarmac.2021.03.007
F. Paas, A. Renkl, and J. Sweller, "Cognitive load theory and instructional design: Recent developments," Educational psychologist, vol. 38, no. 1, pp. 1-4, 2003. DOI: https://doi.org/10.1207/S15326985EP3801_1
L. Chen, P. Chen, and Z. Lin, "Artificial intelligence in education: A review," IEEE access, vol. 8, pp. 75264-75278, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2988510
R. F. Kizilcec and H. Lee, "Algorithmic fairness in education," in The ethics of artificial intelligence in education: Routledge, 2022, pp. 174-202.
R. Sajja, Y. Sermet, M. Cikmaz, D. Cwiertny, and I. Demir, "Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education," Information, vol. 15, no. 10, p. 596, 2024. DOI: https://doi.org/10.3390/info15100596
W. Strielkowski, V. Grebennikova, A. Lisovskiy, G. Rakhimova, and T. Vasileva, "AI‐driven adaptive learning for sustainable educational transformation," Sustainable Development, vol. 33, no. 2, pp. 1921-1947, 2025. DOI: https://doi.org/10.1002/sd.3221
E. Gkintoni, H. Antonopoulou, A. Sortwell, and C. Halkiopoulos, "Challenging cognitive load theory: The role of educational neuroscience and artificial intelligence in redefining learning efficacy," Brain Sciences, vol. 15, no. 2, p. 203, 2025. DOI: https://doi.org/10.3390/brainsci15020203
C. Halkiopoulos and E. Gkintoni, "Leveraging AI in e-learning: Personalized learning and adaptive assessment through cognitive neuropsychology—A systematic analysis," Electronics, vol. 13, no. 18, p. 3762, 2024. DOI: https://doi.org/10.3390/electronics13183762
W. Holmes et al., "Ethics of AI in education: Towards a community-wide framework," International Journal of Artificial Intelligence in Education, vol. 32, no. 3, pp. 504-526, 2022. DOI: https://doi.org/10.1007/s40593-021-00239-1
K.-J. Laak and J. Aru, "AI and personalized learning: bridging the gap with modern educational goals," arXiv preprint arXiv:2404.02798, 2024. DOI:
https://doi.org/10.48550/arXiv.2404.02798
J. Sweller, "Cognitive load theory and educational technology," Educational technology research and development, vol. 68, no. 1, pp. 1-16, 2020. DOI:
https://doi.org/10.1007/s11423-019-09701-3
O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, "Systematic review of research on artificial intelligence applications in higher education–where are the educators?," International journal of educational technology in higher education, vol. 16, no. 1, pp. 1-27, 2019. DOI: https://doi.org/10.1186/s41239-019-0171-0
B. Rienties and L. Toetenel, "The impact of learning design on student behaviour, satisfaction and performance: A cross-institutional comparison across 151 modules," Computers in human behavior, vol. 60, pp. 333-341, 2016. DOI: https://doi.org/10.1016/j.chb.2016.02.074
R. Alfredo et al., "Human-centred learning analytics and AI in education: A systematic literature review," Computers and Education: Artificial Intelligence, vol. 6, p. 100215, 2024. DOI: https://doi.org/10.1016/j.caeai.2024.100215
J. Sweller, J. J. Van Merriënboer, and F. Paas, "Cognitive architecture and instructional design: 20 years later," Educational psychology review, vol. 31, no. 2, pp. 261-292, 2019. DOI: https://doi.org/10.1007/s10648-019-09465-5
J.-C. Hong, M.-Y. Hwang, K.-H. Tai, and C.-R. Tsai, "An exploration of students’ science learning interest related to their cognitive anxiety, cognitive load, self-confidence and learning progress using inquiry-based learning with an iPad," Research in Science Education, vol. 47, no. 6, pp. 1193-1212, 2017. DOI: https://doi.org/10.1007/s11165-016-9541-y
R. E. Mayer, "The past, present, and future of the cognitive theory of multimedia learning," Educational Psychology Review, vol. 36, no. 1, p. 8, 2024. DOI: https://doi.org/10.1007/s10648-023-09842-1
C.-C. Lin, A. Y. Huang, and O. H. Lu, "Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review," Smart learning environments, vol. 10, no. 1, p. 41, 2023. DOI: https://doi.org/10.1186/s40561-023-00260-y
T. Huang, J. Geng, H. Yang, S. Hu, Y. Chen, and J. Zhang, "Long short-term attentional neuro-cognitive diagnostic model for skill growth assessment in intelligent tutoring systems," Expert Systems with Applications, vol. 238, p. 122048, 2024. DOI: https://doi.org/10.1016/j.eswa.2023.122048
A. Létourneau, M. Deslandes Martineau, P. Charland, J. A. Karran, J. Boasen, and P. M. Léger, "A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education," npj Science of Learning, vol. 10, no. 1, p. 29, 2025. DOI: https://doi.org/10.1038/s41539-025-00320-7
Y. Choi and H. Lee, "Psychometric properties for multidimensional cognitive load scale in an E-learning environment," International Journal of Environmental Research and Public Health, vol. 19, no. 10, p. 5822, 2022. DOI: https://doi.org/10.3390/ijerph19105822
G. Gutierrez, I. O. Lunsky, S. Van Heer, A. Szulewski, and T. Chaplin, "Cognitive load theory in action: e-learning modules improve performance in simulation-based education. A pilot study," Canadian journal of emergency medicine, vol. 25, no. 11, pp. 893-901, 2023. DOI: https://doi.org/10.1007/s43678-023-00586-z
T. De Jong and A. Lazonder, "The guided discovery principle in multimedia learning," The Cambridge handbook of multimedia learning, vol. 2, pp. 371-390, 2005.
P. A. Kirschner and J. J. Van Merriënboer, "Do learners really know best? Urban legends in education," Educational psychologist, vol. 48, no. 3, pp. 169-183, 2013. DOI: https://doi.org/10.1080/00461520.2013.804395
B. Williamson, "Governing software: Networks, databases and algorithmic power in the digital governance of public education," Learning, Media and Technology, vol. 40, no. 1, pp. 83-105, 2015. DOI: https://doi.org/10.1080/17439884.2014.924527
H. Xie, H.-C. Chu, G.-J. Hwang, and C.-C. Wang, "Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017," Computers & Education, vol. 140, p. 103599, 2019. DOI: https://doi.org/10.1016/j.compedu.2019.103599