Cognitive Approaches in Artificial Intelligence–Based Intelligent Learning
Keywords:
Intelligent learning, cognitive approaches, artificial intelligence, personalized learning, deep learning, cognitive self-regulationAbstract
This study aims to examine and analyze cognitive approaches in AI-based intelligent learning and explain how mental processes can be integrated with emerging educational technologies. This qualitative study employed a systematic literature review design. The research population included peer-reviewed articles published between 2015 and 2025 on artificial intelligence and cognitive learning, collected from Scopus, Web of Science, ScienceDirect, and Google Scholar databases. After applying inclusion and exclusion criteria, 12 eligible articles were selected. Data were analyzed through qualitative content analysis using NVivo 14 software, and coding continued until theoretical saturation was achieved. The analysis identified three major themes across the reviewed studies: (1) integration of cognitive processes such as attention, memory, and metacognition into intelligent learning systems; (2) enhancement of learning through personalized and cognitive deep learning models based on AI; and (3) ethical, technical, and cultural challenges in implementing these approaches. The results also revealed that technologies such as natural language processing, virtual reality, and EEG-based systems contribute to more accurate modeling of learners’ cognitive states and enhance their engagement with educational content. The findings suggest that AI-based intelligent learning, when integrated with cognitive theories, can improve educational quality, increase self-regulated learning, and promote meaningful understanding. Nevertheless, challenges related to algorithm interpretability and ethical management of cognitive data must be carefully addressed to ensure effective and responsible use.
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