Data-Driven Precision Learning: Transforming Adult Education with AI and Analytics.

4区 医学 Q2 Biochemistry, Genetics and Molecular Biology
Elissavet Karageorgou, Styliani Adam, Spyridon Doukakis, Panagiotis Vlamos
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引用次数: 0

Abstract

Lifelong learning has become essential in modern society, driven by technological advancements and the increasing demand for continuous skill development. E-learning platforms have transformed education by enhancing accessibility, flexibility, and personalization. Artificial intelligence (AI) and data-driven technologies further refine these platforms, enabling adaptive learning, real-time feedback, and predictive analytics to improve engagement and outcomes. The COVID-19 pandemic accelerated the adoption of e-learning, shifting it from a supplementary tool to a primary mode of education.This study examines the impact of e-learning on adult education, focusing on AI-driven personalization and data analytics. In Greece, national and European policies have facilitated digital education, yet challenges such as infrastructure limitations and digital inequalities persist. Additionally, data mining in e-learning environments has the potential to optimize instructional methods and predict learning outcomes. A proposed framework for precision education integrates multimodal data sources, including biometric indicators, to enhance individualized learning experiences.While AI-powered e-learning systems offer transformative opportunities, ethical concerns surrounding data privacy and equitable access must be addressed. Ensuring responsible data management and inclusive policies will be crucial in maintaining the effectiveness of digital education and fostering lifelong learning in an increasingly technology-driven world.

数据驱动的精确学习:用人工智能和分析改变成人教育。
在技术进步和对持续技能发展的需求不断增长的推动下,终身学习在现代社会变得至关重要。电子学习平台通过增强可访问性、灵活性和个性化改变了教育。人工智能(AI)和数据驱动技术进一步完善了这些平台,实现了自适应学习、实时反馈和预测分析,从而提高了参与度和结果。2019冠状病毒病大流行加速了电子学习的采用,使其从补充工具转变为主要的教育模式。本研究考察了电子学习对成人教育的影响,重点关注人工智能驱动的个性化和数据分析。在希腊,国家和欧洲的政策促进了数字教育,但基础设施限制和数字不平等等挑战依然存在。此外,电子学习环境中的数据挖掘具有优化教学方法和预测学习结果的潜力。提出的精准教育框架整合了多种数据来源,包括生物特征指标,以增强个性化的学习体验。虽然人工智能驱动的电子学习系统提供了变革性的机会,但必须解决有关数据隐私和公平访问的道德问题。确保负责任的数据管理和包容性政策,对于在日益技术驱动的世界中保持数字教育的有效性和促进终身学习至关重要。
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来源期刊
Advances in experimental medicine and biology
Advances in experimental medicine and biology 医学-医学:研究与实验
CiteScore
5.90
自引率
0.00%
发文量
465
审稿时长
2-4 weeks
期刊介绍: Advances in Experimental Medicine and Biology provides a platform for scientific contributions in the main disciplines of the biomedicine and the life sciences. This series publishes thematic volumes on contemporary research in the areas of microbiology, immunology, neurosciences, biochemistry, biomedical engineering, genetics, physiology, and cancer research. Covering emerging topics and techniques in basic and clinical science, it brings together clinicians and researchers from various fields.
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