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27.01.2026На каком языке издана
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1999-4893Область исследования
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first_pagesettingsOrder Article Reprints Open AccessArticle An Adaptive Task Difficulty Model for Personalized Reading Comprehension in AI-Based Learning Systems by Aray M. Kassenkhan 1,*ORCID,Mateus Mendes 2,3,*ORCID andAkbayan Bekarystankyzy 4,* 1 Department of Software Engineering, Institute of Automation and Information Technologies, Satbayev University, Almaty 050013, Kazakhstan 2 Polytechnic University of Coimbra, Rua da Misericórdia, Lagar dos Cortiços, S. Martinho do Bispo, 3045-093 Coimbra, Portugal 3 RCM2+, Polytechnic University of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal 4 School of Digital Technologies, Narxoz University, Almaty 050035, Kazakhstan * Authors to whom correspondence should be addressed. Algorithms 2026, 19(2), 100; https://doi.org/10.3390/a19020100 Submission received: 24 December 2025 / Revised: 7 January 2026 / Accepted: 21 January 2026 / Published: 27 January 2026 (This article belongs to the Section Algorithms for Multidisciplinary Applications) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract This article proposes an interpretable adaptive control model for dynamically regulating task difficulty in Artificial intelligence (AI)-augmented reading-comprehension learning systems. The model adjusts, on the fly, the level of task complexity associated with reading comprehension and post-text analytical tasks based on learner performance, with the objective of maintaining an optimal difficulty level. Grounded in adaptive control theory and learning theory, the proposed algorithm updates task difficulty according to the deviation between observed learner performance and a predefined target mastery rate, modulated by an adaptivity coefficient. A simulation study involving heterogeneous learner profiles demonstrates stable convergence behavior and a strong positive correlation between task difficulty and learning performance (r = 0.78). The results indicate that the model achieves a balanced trade-off between learner engagement and cognitive load while maintaining low computational complexity, making it suitable for real-time integration into intelligent learning environments. The proposed approach contributes to AI-supported education by offering a transparent, control-theoretic alternative to heuristic difficulty adjustment mechanisms commonly used in e-learning systems.DOI
10.3390/a19020100Тип публикаций
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