Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis
Использование искусственного интеллекта в электронном обучении: персонализированное обучение и адаптивное оценивание с позиций когнитивной нейропсихологии — систематический анализ
2024-09-22
SCID: 54.1/wew9595f
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AI in e-learningPRISMA systematic reviewadaptive assessmentcognitive neuropsychologypersonalized learning
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Abstract (AI)
This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive Assessment (AA). This review follows the PRISMA systematic review methodology and synthesizes the results of 85 studies that were selected from an initial pool of 818 records across several databases. The results indicate that AI can improve students’ performance, engagement, and motivation; at the same time, some challenges like bias and discrimination should be noted. The review covers the historic development of AI in education, its theoretical grounding, and its practical applications within PL and AA with high promise and ethical issues of AI-powered educational systems. Future directions are empirical validation of effectiveness and equity, development of algorithms that reduce bias, and exploration of ethical implications regarding data privacy. The review identifies the transformative potential of AI in developing personalized and adaptive learning (AL) environments, thus, it advocates continued development and exploration as a means to improve educational outcomes.
Key Findings
1
A PRISMA systematic review synthesized 85 studies selected from 818 records on AI integration into e-learning for Personalized Learning and Adaptive Assessment.
2
AI-powered Personalized Learning and Adaptive Assessment show substantial potential for transforming education through individualized and adaptive learning experiences.
3
Future research should empirically validate effectiveness and equity, develop bias-reducing algorithms, and address data-privacy implications.
4
The review highlights bias and discrimination as important ethical challenges associated with AI-driven educational systems.
5
The reviewed literature indicates that AI can improve students’ academic performance, engagement, and motivation in e-learning environments.
Research Object
AI-powered e-learning and educational systems
Research Subject
Personalized learning and adaptive assessment, including their effects on student performance, engagement, motivation, bias, discrimination, equity, and privacy
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2024-09-22
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