Revolutionizing education with AI: The adaptive cognitive enhancement model (ACEM) for personalized cognitive development
Year: 2024
Type: article
Abstract: The integration of artificial intelligence (AI) into education has opened doors to personalized learning experiences. This paper introduces the Adaptive Cognitive Enhancement Model (ACEM), a cutting-edge AI-driven framework designed to personalize cognitive development for students. Leveraging advanced machine learning algorithms and quantitative analysis, ACEM adapts educational content and learning strategies to individual cognitive needs. The model encompasses five key components: cognitive profiling, adaptive learning paths, intelligent feedback, motivational strategies, and longitudinal tracking. Through quantitative analysis and mathematical modeling, the paper demonstrates how ACEM significantly enhances learning outcomes compared to traditional education models. The discussion section provides a detailed exploration of each model component, its architecture, and its role in optimizing personalized cognitive development. Furthermore, challenges such as data privacy, scalability, and model interpretability are examined, alongside potential solutions. The conclusion underscores the transformative potential of ACEM in revolutionizing education.
Author: Yun Luo
Institution: Xiangnan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4404223116