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人工智能:高等教育辅导的通用虚拟工具

Artificial Intelligence: A Universal Virtual Tool to Augment Tutoring in Higher Education.

机构信息

Woxsen University, Hyderabad, Telangana, India.

School of Business Studies, Sharda University, Greater Noida, India.

出版信息

Comput Intell Neurosci. 2022 May 9;2022:1410448. doi: 10.1155/2022/1410448. eCollection 2022.

Abstract

Artificial intelligence is an emerging technology that revolutionizes human lives. Despite the fact that this technology is used in higher education, many professors are unaware of it. In this current scenario, there is a huge need to arise, implement information bridge technology, and enhance communication in the classroom. Through this paper, the authors try to predict the future of higher education with the help of artificial intelligence. This research article throws light on the current education system the problems faced by the subject faculties, students, changing government rules, and regulations in the educational sector. Various arguments and challenges on the implementation of artificial intelligence are prevailing in the educational sector. In this concern, we have built a use case model by using a student assessment data of our students and then built a synthesized using generative adversarial network (GAN). The dataset analyzed, visualized, and fed to different machine learning algorithms such as logistic Regression (LR), linear discriminant analysis (LDA), K-nearest neighbors (KNN), classification and regression trees (CART), naive Bayes (NB), support vector machines (SVM), and finally random forest (RF) algorithm and achieved a maximum accuracy of 58%. This article aims to bridge the gap between human lecturers and the machine. We are also concerned about the psychological emotions of the faculty and the students when artificial intelligence takes control.

摘要

人工智能是一项颠覆人类生活的新兴技术。尽管这项技术被应用于高等教育中,但许多教授对此并不了解。在当前的情况下,迫切需要利用信息桥接技术,并增强课堂沟通。本文作者试图借助人工智能预测高等教育的未来。本研究论文探讨了当前教育系统、学科教师、学生面临的问题、政府对教育部门的规则和条例的改变。在教育领域,人工智能的实施存在着各种争论和挑战。在这方面,我们使用学生评估数据构建了一个用例模型,然后使用生成对抗网络(GAN)构建了一个综合模型。对分析、可视化和输入到不同机器学习算法的数据进行了分析,例如逻辑回归(LR)、线性判别分析(LDA)、K-最近邻(KNN)、分类和回归树(CART)、朴素贝叶斯(NB)、支持向量机(SVM),最后是随机森林(RF)算法,实现了最高 58%的准确率。本文旨在弥合人类讲师和机器之间的差距。我们还关注当人工智能接管时,教师和学生的心理情绪。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f41d/9110123/6cc168f9ba34/CIN2022-1410448.001.jpg

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