教育认知系统:架构、创新和比较分析

Hanane Bahassi , Mohamed Azmi , Azeddine Khiat
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引用次数: 0

摘要

近年来,教育与技术的不断发展紧密相连,尤其是基于人工智能认知能力的智能系统。这里强调的是认知计算在教育和学习领域的巨大潜力。这种关联意味着,通过在学习和教学过程中整合先进的认知系统,将对教育的提供、获取和个性化产生变革性影响。本文概述了在教育领域用于加强学习和教学活动的几种认知计算技术。本研究确定了这些系统的三种概念架构:分层架构、基于代理的架构和混合架构;然后描述了它们的组成部分。最后,本研究探讨了教育领域使用的知名平台,即 IBM Watson、Kenwton、Carnegie Learning 和 DreamBox Learning。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cognitive Systems for Education: Architectures, Innovations, and Comparative Analyses

In recent years, education has been closely linked to the continued development of technology, especially smart systems based on the use of artificial intelligence with cognitive capabilities. The emphasis here is on the significant potential of cognitive computing in the domain of education and learning. This association implies a transformative impact on how education is delivered, accessed, and personalized through the integration of advanced cognitive systems in the learning and teaching process. This article conducts an overview of the several cognitive computing technologies in the context of education used to enhance learning and teaching activities. This study identifies three conceptual architectures of these systems, Layered Architecture, Agent-Based Architecture, and Hybrid Architecture; then describes their components. Finally, it explores well-known platforms that are used in the education field namely IBM Watson, Kenwton, Carnegie Learning, and DreamBox Learning.

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