An Adaptive Task Difficulty Model for Personalized Reading Comprehension in AI-Based Learning Systems
Kassenkhan A.M. Mendes M. Bekarystankyzy A.
February 2026Multidisciplinary Digital Publishing Institute (MDPI)
Algorithms
2026#19Issue 2
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.
adaptive learning , artificial intelligence , cognitive development , educational technology , mathematical modeling , personalized e-learning , task difficulty adjustment
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Department of Software Engineering, Institute of Automation and Information Technologies, Satbayev University, Almaty, 050013, Kazakhstan
Polytechnic University of Coimbra, Coimbra, 3045-093, Portugal
RCM2+, Polytechnic University of Coimbra, Rua Pedro Nunes, Coimbra, 3030-199, Portugal
School of Digital Technologies, Narxoz University, Almaty, 050035, Kazakhstan
Department of Software Engineering
Polytechnic University of Coimbra
RCM2+
School of Digital Technologies
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Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026