The work was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0503000, 2023ZD0503004, SQ2026AAA161144).
References
Ahmed, F. (2024). The digital divide and AI in education: Addressing equity and accessibility. AI EDIFY Journal, 1(2), 12-23.
Bewersdorff, A., Hartmann, C., Hornberger, M., Seßler, K., Bannert, M., Kasneci, E., & Nerdel, C. (2025). Taking the next step with generative artificial intelligence: The transformative role of multimodal large language models in science education. Learning and Individual Differences, 118, 102601.
https://doi.org/10.1016/j.lindif.2024.102601
Boubker, O. (2024). From chatting to self-educating: Can AI tools boost student learning outcomes? Expert Systems with Applications, 238, 121820.
Cai, Z., & Hu, X. (2018). AutoTutor: An intelligent tutoring system and its authoring tools. In Deep comprehension (pp. 140-153). Routledge.
Chi, M. T. H., De Leeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439-477.
Cowan, N. (2010). The magical mystery four: How is working memory capacity limited, and why? Current Directions in Psychological Science, 19(1), 51-57.
https://doi.org/10.1177/0963721409359277
Duanmu, X., Yu, J., Yuan, X., & Zhang, X. (2025). How does digital infrastructure mitigate urban–rural disparities? Sustainability, 17(4), 1561.
Farzanfar, D., Spiers, H. J., Moscovitch, M., & Rosenbaum, R. S. (2023). From cognitive maps to spatial schemas. Nature Reviews Neuroscience, 24(2), 63-79.
https://doi.org/10.1038/s41583-022-00655-9
Gkintoni, E., Antonopoulou, H., Sortwell, A., & Halkiopoulos, C. (2025). Challenging cognitive load theory: The role of educational neuroscience and artificial intelligence in redefining learning efficacy. Brain Sciences, 15(2), 203.
https://doi.org/10.3390/brainsci15020203
Hernika, H., Wahab, A. A., & Susetya, H. H. H. (2025). Eksplorasi penerapan strategi metakognitif dalam pembelajaran membaca kritis di SMAN 1 Paiton: Exploration of metacognitive strategy implementation in critical reading learning at SMAN 1 Paiton. Trans-formatika: Jurnal Bahasa, Sastra, dan Pengajarannya, 9(3), 794-808.
Kirchner, E. A., Kim, S. K., Tabie, M., Wöhrle, H., Maurus, M., & Kirchner, F. (2016). An intelligent man-machine interface—multi-robot control adapted for task engagement based on single-trial detectability of P300. Frontiers in Human Neuro-science, 10, 291.
Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012). The knowledge-learning-instruction framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science, 36(5), 757-798.
Laak, K.-J., & Aru, J. (2024). AI and personalized learning: Bridging the gap with modern educational goals. arXiv preprint-arXiv:2404.02798.
Leppink, J., Paas, F., Van der Vleuten, C. P., Van Gog, T., & Van Merriënboer, J. J. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4), 1058-1072.
Leppink, J., Paas, F., Van Gog, T., van Der Vleuten, C. P., & Van Merrienboer, J. J. (2014). Effects of pairs of problems and examples on task performance and different types of cognitive load. Learning and Instruction, 30, 32-42.
Lyu, C., & Deng, S. (2024). Effectiveness of embodied learning on learning performance: A meta-analysis based on the cognitive load theory perspective. Learning and Individual Differences, 116, 102564.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.
Paas, F., & van Merriënboer, J. J. G. (2020). Cognitive-load theory: Methods to manage working memory load in the learning of complex tasks. Current Directions in Psychological Science, 29(4), 394-398. https://doi.org/10.1177/0963721420922183
Poupard, M., Larrue, F., Bertrand, M., Liguoro, D., Sauzéon, H., & Tricot, A. (2025). Reducing load, fostering curiosity: Empirical validation of the IMCLM-XR. https://hal.science/hal-05219664
Qiao, Y. Q., Shen, J., Liang, X., Ding, S., Chen, F. Y., Shao, L., ... Ran, Z. H. (2014). Using cognitive theory to facilitate medical education. BMC Medical Education, 14(1), 79.
https://doi.org/10.1186/1472-6920-14-79
Ricotta, D. N., Richards, J. B., Atkins, K. M., Hayes, M. M., McOwen, K., Soffler, M. I., ... Schwartzstein, R. M. (2022). Self-directed learning in medical education: Training for a lifetime of discovery. Teaching and Learning in Medicine, 34(5), 530-540.
https://doi.org/10.1080/10401334.2021.1938074
Roshanaei, M., Olivares, H., & Lopez, R. R. (2023). Harnessing AI to foster equity in education: Opportunities, challenges, and emerging strategies. Journal of Intelligent Learning Systems and Applications, 15(4), 123-143.
Seufert, T. (2018). The interplay between self-regulation in learning and cognitive load. Educational Research Review, 24, 116-129.
https://doi.org/10.1016/j.edurev.2018.03.004
Sheffler, P., Rodriguez, T. M., Cheung, C. S., & Wu, R. (2022). Cognitive and metacognitive, motivational, and resource considerations for learning new skills across the lifespan. Wiley Interdisciplinary Reviews: Cognitive Science, 13(2), e1585.
https://doi.org/10.1002/wcs.1585
Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction, 36(6), 495–504.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285.
https://doi.org/10.1016/0364-0213(88)90023-7
Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123-138.
Sweller, J. (2011). Cognitive load theory. In Psychology of learning and motivation (Vol. 55, pp. 37-76). Elsevier.
Sweller, J., & Chandler, P. (1994). Why some material is difficult to learn. Cognition and Instruction, 12(3), 185-233.
Sweller, J., Van Merriënboer, J. J., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31(2), 261-292.
Sweller, J., Van Merrienboer, J. J., & Paas, F. G. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251-296.
Tritsch, T., Lin, S., Pough, A., Schwartz, G., & Shoja, M. M. (2024). Typical brachial plexus: The legacy of a forgotten anatomist, Abram T. Kerr (1873-1938). Child’s Nervous System, 40(5), 1319-1324. https://doi.org/10.1007/s00381-023-06223-5
Van Merriënboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147-177.
Versteeg, M., Bressers, G., Wijnen-Meijer, M., Ommering, B. W. C., de Beaufort, A. J., & Steendijk, P. (2021). What were you thinking? Medical students’ metacognition and perceptions of self-regulated learning. Teaching and Learning in Medicine, 33(5), 473-482.
https://doi.org/10.1080/10401334.2021.1889559
Yeager, D. S., & Dweck, C. S. (2020). What can be learned from growth mindset controversies? American Psychologist, 75(9), 1269-1284.
Young, J. Q., Van Merrienboer, J., Durning, S., & Ten Cate, O. (2014). Cognitive load theory: Implications for medical education: AMEE Guide No. 86. Medical Teacher, 36(5), 371-384.
https://doi.org/10.3109/0142159X.2014.889290
Yuan, K., Steedle, J., Shavelson, R., Alonzo, A., & Oppezzo, M. (2006). Working memory, fluid intelligence, and science learning. Educational Research Review, 1(2), 83-98.
Zheng, L., Li, X., Zhang, X., & Sun, W. (2019). The effects of group metacognitive scaffolding on group metacognitive behaviors, group performance, and cognitive load in computer-supported collaborative learning. The Internet and Higher Education, 42, 13-24.
https://doi.org/10.1016/j.iheduc.2019.03.002