Abstract
The study developed a predictive computational model to identify and reduce cognitive overload in undergraduate business administration students in digital environments. A quantitative approach was applied using academic, cognitive, and technological variables related to digital fatigue, academic stress, and the use of virtual platforms. Machine learning algorithms such as logistic regression, SVM, and Random Forest were implemented and complemented with computational simulation. The results showed that Random Forest achieved the best predictive performance and made it possible to detect critical scenarios of cognitive saturation. It is concluded that computational models can contribute to the design of intelligent educational systems oriented toward academic well-being.
References
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[2] H. Pang, X. Jin, and W. Zhang, “Deciphering the underlying repercussions of cognitive overload on university students’ fatigue, frustration and academic productivity: Implementation of stimulus–organism–response model,” Acta Psychologica, vol. 260, Art. no. 105469, 2025, doi: 10.1016/j.actpsy.2025.105469.
[3] M. A. Pasha, S. Nadeem, A. Rasool, F. Shafique, H. Imran, S. Kanwal, and K. Yasmeen, “Digital Fatigue and Cognitive Overload as Managerial Challenges Understanding the Impact of Workload Management on the Workforce,” The Critical Review of Social Sciences Studies, vol. 3, no. 4, pp. 1629–1641, 2025, doi: 10.52970/grsse.v5i2.1275.
[4] M. Kabir, A. Zafar, Z. Naz, and F. Maqsood, “The Role of Personalized AI-Based Learning Tools in Reducing Cognitive Overload and Enhancing Academic Motivation,” Review of Applied Management and Social Sciences, vol. 8, no. 3, pp. 1261–1275, 2025, doi: 10.47067/ramss.v8i3.544.
[5] S. M. Kashlot, I. Ruthven, and Y. Moshfeghi, “Strategies for managing information overload: a systematic review,” Journal of Documentation, vol. 82, no. 2, pp. 249–265, 2026, doi: 10.1108/JD-09-2025-0257.
[6] S. Barigidad, S. Hameed, N. Karri, S. K. Jangam, P. S. R. Pedda, and D. Gupta, “Computational Modeling of AI-Enhanced Learning Pathways: A Mathematical Framework for Optimizing Knowledge Acquisition, Cognitive Load Management, and Student Performance in STEM Education,” in 2025 International Conference on AI-Driven STEM Education and Learning Technologies (AISTEMEDU), 2025, pp. 1–7.
[7] M. N. Algewatta and K. Manathunga, “A BI Approach for Student Engagement and Retention along with Cognitive Load Analysis for Educator,” in 2025 5th International Conference on Advanced Research in Computing (ICARC), Belihuloya, Sri Lanka, 2025, pp. 1–6, doi: 10.1109/ICARC64760.2025.10962823.
[8] L. Rodrig and L. Marlow, “Cognitive Load Management in Digital Learning Environments: Implications for Student Performance,” Innovative Journal of Educational Research and Insights, pp. 188–197, 2025.
[9] 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, Art. no. 203, 2025, doi: 10.3390/brainsci15020203.
[10] K. Twabu, “Enhancing the cognitive load theory and multimedia learning framework with AI insight,” Discover Education, vol. 4, Art. no. 160, 2025, doi: 10.1007/s44217-025-00592-6.
[11] X. Wan and J. Liu, “Human-computer interaction strategies to alleviate information overload in business contexts,” International Journal of Human–Computer Interaction, vol. 41, no. 21, pp. 13349–13362, 2025, doi: 10.1080/10447318.2025.2470296.
[12] P. Gupta and A. Prashar, “Learners’ psychological needs in online learning environment for executive education: role of cognitive overload and learning self-efficacy,” Behaviour & Information Technology, vol. 44, no. 9, pp. 1942–1963, 2025, doi: 10.1080/0144929X.2024.2383777.
[13] G. Chirayath, K. Premamalini, and J. Joseph, “Cognitive offloading or cognitive overload? How AI alters the mental architecture of coping,” Frontiers in Psychology, vol. 16, Art. no. 1699320, 2025, doi: 10.3389/fpsyg.2025.1699320.
[14] A. Batubara, F. Rachman, R. Nababan, L. Siagian, and A. Latifah, “Brain Flow: A Gamified Digital Assessment with Chunking Enhancing Patriotism Reducing Cognitive Overload,” in International Conference on Social Sciences and Interdisciplinary Studies (ICSSIS 2025). Paris, France: Atlantis Press, 2025, pp. 273–281, doi: 10.2991/978-2-38476-499-0_22.
This work is licensed under a Creative Commons Attribution 4.0 International License.
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