Proceedings of International Conference on Applied Innovation in IT  ·  2026/07/22  ·  Vol. 14  ·  Issue 4  ·  pp. 1771–1778
Cognitive-Semantic Modeling and AI-Driven Adaptive Learning Systems for Enhancing Foreign Language Comprehension
Oybek Achilov, Tony Brown, Diyora Ishankulova, Yusup Kushakov and Shokhista Mansurova
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Abstract
The increasing integration of artificial intelligence and digital platforms in higher education has created new opportunities for enhancing foreign language comprehension. However, many technology-enhanced language learning environments lack a strong cognitive-semantic foundation to support deep meaning construction. This study proposes and empirically examines an Applied IT instructional model that integrates cognitive-semantic theory with e-learning platforms and adaptive AI-based systems to improve foreign language reading and understanding competence among university students. Grounded in frame semantics, mental spaces theory, cognitive domains, and reframing methodology, the model operationalizes meaning-centered instruction through Moodle, Quizlet, Kahoot, Duolingo for Higher Education, and Smart Sparrow. A quasi experimental mixed-methods design was employed with undergraduate learners divided into experimental and control groups. Quantitative results demonstrate statistically significant improvements in reading comprehension for learners exposed to the Applied IT component, while qualitative findings reveal enhanced contextual interpretation and semantic awareness. The study contributes to digital pedagogy by demonstrating how cognitive linguistic principles can be systematically embedded into adaptive educational technologies through learning analytics and AI-driven personalization to support academic reading in higher education. The paper presents the design, implementation, and empirical validation of an AI-driven cognitive-semantic adaptive learning system for foreign language reading comprehension. The system integrates a formally specified rule-based adaptation engine, structured learning analytics pipeline, and reproducible deployment configuration. A quasi-experimental study (N = 84) demonstrated statistically significant improvement in comprehension gains (t(82) = 4.73, p < .001, Cohen’s d = 0.92).
Keywords
Digital Pedagogy Cognitive Semantics Adaptive Learning Systems Learning Analytics Higher Education AI in Education Transformation Frame Analyses Reframing Modelling Cognitive Domains Mental Spaces Conceptual Analyses Technology-Enhanced Learning
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