Resumen
La investigación desarrolló un modelo computacional predictivo para identificar y reducir la sobrecarga cognitiva en estudiantes universitarios de administración en entornos digitales. Se aplicó un enfoque cuantitativo utilizando variables académicas, cognitivas y tecnológicas relacionadas con fatiga digital, estrés académico y uso de plataformas virtuales. Se implementaron algoritmos de aprendizaje automático como regresión logística, SVM y Random Forest, complementados con simulación computacional. Los resultados mostraron que Random Forest obtuvo el mejor desempeño predictivo y permitió detectar escenarios críticos de saturación cognitiva. Se concluye que los modelos computacionales pueden contribuir al diseño de sistemas educativos inteligentes orientados al bienestar académico.
Citas
[1] N. Qamar, I. Ullah, F. Zeib, and M. Z. Khan, “The Role of AI in Reducing Cognitive Overload Complex Learning Environments,” Review of Applied Management and Social Sciences, vol. 8, no. 2, pp. 1111–1127, 2025, doi: 10.47067/ramss.v8i2.541.
[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.
[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.
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