scholarly journals From the Teaching machines to the Machine learning. Opportunities and challenges for Artificial Intelligence in education

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Valentina Grion ◽  
Graziano Cecchinato
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Opportunities ◽  
Artificial Intelligence In Education ◽  
Teaching Machines

scholarly journals Artificial Intelligence in Education

Seminar.net ◽  
2021 ◽  
Vol 17 (2) ◽  
Author(s):  
Xavier Giró Gràcia ◽  
Juana M. Sancho-Gil
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Digital Technology ◽  
Social Systems ◽  
Machine Learning Algorithms ◽  
Educational Systems ◽  
Negative Effects ◽  
Black Boxes ◽  
Artificial Intelligence In Education

Digital technology is constantly permeating and transforming all social systems, and education is not an exception. In the last decade, the unstoppable development of Artificial Intelligence, based on machine learning algorithms and fuelled by Big Data, has given a new push to the hope of improving learning-based machines, and providing educational systems with ‘effective’ solutions. Educators, educational researchers and policymakers, in general, lack the knowledge and expertise to understand the underlying logic of these new ‘black boxes’, and we do not have sufficient research-based evidence to understand the consequences that an excessive use of screens has in students’ development. This paper first discusses the notions behind what Big Data is and what it means in our current society; how data is the new currency that has driven the use of algorithms in all areas of our society, and specifically in the field of Artificial Intelligence; and the concept of ‘black boxes’, and its possible impact on education. Then, it discusses the underlying educational discourses, pointing out the need to analyse not only their contributions but also their possible negative effects. It finishes with considerations and a proposed agenda for further studying this phenomenon.

scholarly journals POSSIBILITIES OF APPLICATION MACHINE LEARNING METHODS IN SOLVING ANALYTICAL PROBLEMS IN METALLURGICAL INDUSTRY

2021 ◽  
Vol 9 (3) ◽  
pp. 61-65
Author(s):  
Diana Yusupova ◽  
Sergey Muzalev
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Metallurgical Industry ◽  
Learning Opportunities ◽  
High Tech ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
Non Linear ◽  
A Company

Background. Machine learning is a promising field for organization in the age of development of high-tech methods of management and organization of the company. As a rule, this term is used in relation to artificial intelligence, namely, machines that could learn independently. Thus, the main goal of this work is to assess the prospects for using these methods for solving various problems in a corporation. Methods. The article introduces the main methods of machine learning, their analysis, linear and non-linear learning methods are given, their use in practice is indicated, and the key advantages of using a trained artificial intelligence in a company are identified. Result. As a result, the author proposes ways of using machine learning methods in a firm, analyzes their advantages and disadvantages, identifies the problems of implementing artificial intelligence learning opportunities in practice.

scholarly journals A Common Framework for Artificial Intelligence in Higher Education (AAI-HE Model)

2021 ◽  
Vol 14 (11) ◽  
pp. 94
Author(s):  
Thiti Jantakun ◽  
Kitsadaporn Jantakun ◽  
Thada Jantakoon
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Machine Learning ◽  
Deep Learning ◽  
Research Result ◽  
Research Process ◽  
Purposive Sampling ◽  
Artificial Intelligence In Education ◽  
Common Framework ◽  
Two Stages

This research aims to 1) Develop a common framework for artificial intelligence in higher education (AAI-HE model) and 2) Assess the AAI-HE model. The research process is divided into two stages: 1) Develop an AAI-HE model, and 2) Assessment the model. The sample consists of five experts chosen through purposive sampling. The data is analyzed by means and standardized deviations statistically. The research result shows that 1) the AAI-HE model consists of seven key components which are 1.1) User Interactive Components and Technology of AI, 1.2) Components and Technology of AI, 1.3) Roles for Artificial Intelligence in Education 1.4) Machine Learning and Deep Learning 1.5) DSS Modules 1.6) Applications of Artificial Intelligence in Education, and 1.7) AI to enhance campus efficiencies, and 2) The result of the assessment of the AAI-HE model is rated as absolutely appropriate overall.

scholarly journals Artificial intelligence in education and assessment methods

2020 ◽  
Vol 9 (5) ◽  
pp. 1998-2007
Author(s):  
Balqis Al Braiki ◽  
Saad Harous ◽  
Nazar Zaki ◽  
Fady Alnajjar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Daily Life ◽  
Machine Learning Techniques ◽  
Educational Context ◽  
Course Assessment ◽  
Teaching Community ◽  
Learning Techniques ◽  
Artificial Intelligence In Education ◽  
Better Than

Today, artificial intelligence has proliferated to reach almost every wing of daily life, perhaps one of the most sensitive of these being education. While teaching, insofar as it involves training human minds, is still mostly a form of art rather than a regular science, the taking up of this elitist job by computers has triggered much debate and controversy, involving the teaching community as much as the select corporate AI giants who strive to create computers capable of teaching better than humans. This paper surveys the most relevant studies carried out in this field to date. First, it introduces AI and describes the different AI applications in field of education and course assessment. It then goes on to list the most common topics in the educational context that have been resolved through AI and machine learning techniques, and finally, some of the most promising future lines of research are discussed. 

The Future of Artificial Intelligence in Education

2021 ◽  
pp. 187-194
Author(s):  
Fati Tahiru ◽  
Samuel Agbesi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Counseling Students ◽  
Learning Management Systems ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
The Future ◽  
Artificial Intelligence In Education ◽  
Use Of The Internet

The key accelerating factor in the increased growth of AI is the availability of historic datasets, and this has influenced the adoption of artificial intelligence and machine learning in education. This is possible because data can be accessed through the use of various learning management systems (LMS) and the increased use of the internet. Over the years, research on the use of AI and ML in education has improved appreciably, and studies have also indicated its success. Machine learning algorithms have successfully been implemented in institutions for predicting students' performance, recommending courses, counseling students, among others. This chapter discussed the use of AI and ML-assisted systems in education, the importance of AI in education, and the future of AI in education to provide information to educators on the AI transformation in education.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

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