Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis

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 f...

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Vydáno v:Electronics (Basel) Ročník 13; číslo 18; s. 3762
Hlavní autoři: Halkiopoulos, Constantinos, Gkintoni, Evgenia
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.09.2024
Témata:
Adaptive algorithms
Adaptive learning
Analysis
Artificial intelligence
Bias
Cognitive ability
Cognitive style
Computational linguistics
Customization
Distance learning
Education
Ethics
Language processing
Machine learning
Methods
Natural language interfaces
Neuropsychology
Online instruction
Personalized learning
Students
System effectiveness
Teaching methods
Tutoring
ISSN:2079-9292, 2079-9292
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Abstract 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.
AbstractList 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.
Audience Academic
Author Halkiopoulos, Constantinos
Gkintoni, Evgenia
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Snippet This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive...
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SubjectTerms Adaptive algorithms
Adaptive learning
Analysis
Artificial intelligence
Bias
Cognitive ability
Cognitive style
Computational linguistics
Customization
Distance learning
Education
Ethics
Language processing
Machine learning
Methods
Natural language interfaces
Neuropsychology
Online instruction
Personalized learning
Students
System effectiveness
Teaching methods
Tutoring
Title Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis
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