论文标题

Z代成瘾的智能辅导系统

Intelligent Tutoring Systems for Generation Z's Addiction

论文作者

Goldbach, Ioana R., Hamza-Lup, Felix G.

论文摘要

随着Z世代的大数据通过社交网络淹没了互联网,基于神经网络的数据处理正在变成重要的基石,显示出快速提取数据模式的巨大潜力。在线课程交付和相关的辅导正在转变为由学习者驱动的可定制的按需服务。除了自动分级外,还具有强大的潜力来开发和部署下一代智能辅导软件代理。自适应,在线辅导代理人表现出“智能”行为,能够从学习者那里学习”,将成为下一个教育超级巨星。在过去的十年中,从患者康复到心理创伤愈合,将基于计算机的补习代理部署在各种扩展的现实环境中。这些代理中的大多数都是由一组条件控制语句和一个大答案/问题对驱动的。本文简要介绍了Z世代对数字信息的成瘾,重点介绍了开发智能对话系统的重要努力,并解释了智能辅导系统的主要组成部分和重要的设计决策。

As generation Z's big data is flooding the Internet through social nets, neural network based data processing is turning an important cornerstone, showing significant potential for fast extraction of data patterns. Online course delivery and associated tutoring are transforming into customizable, on-demand services driven by the learner. Besides automated grading, strong potential exists for the development and deployment of next generation intelligent tutoring software agents. Self-adaptive, online tutoring agents exhibiting "intelligent-like" behavior, being capable "to learn" from the learner, will become the next educational superstars. Over the past decade, computer-based tutoring agents were deployed in a variety of extended reality environments, from patient rehabilitation to psychological trauma healing. Most of these agents are driven by a set of conditional control statements and a large answers/questions pairs dataset. This article provides a brief introduction on Generation Z's addiction to digital information, highlights important efforts for the development of intelligent dialogue systems, and explains the main components and important design decisions for Intelligent Tutoring System.

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