Computers and Artificial Intelligence in Future Education

Computers and Artificial Intelligence in Future Education

Michael Voskoglou
Copyright: © 2021 |Pages: 27
DOI: 10.4018/978-1-7998-7638-0.ch028
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This chapter focuses on the role computers and artificial intelligence could play for future education in our modern society of knowledge and globalization. The rapid industrial and technological development of the last 150 years has caused radical changes to the traditional human society. As a result, formal education at all levels, from elementary to tertiary, faces the great challenge of preparing students for the forthcoming era of a new but not yet well-known industrial revolution, characterized by the internet of things and energy and the cyber-physical systems controlled through it. It is concluded that it is unlikely for computers and other “clever” AI machines to replace teachers in the future, because all these devices were created and programmed by humans. It is therefore logical to accept that they will never be able to achieve the quality and independence of human thought. However, it is certain that the role of the teacher will dramatically change in future classrooms.
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The rapid industrial and technological development of the last 150 years caused radical changes to our lives and behaviours, transforming the traditional and mainly agrarian human society of the last centuries to a modern society of knowledge and globalization. Machines especially designed for massive industrial production, computers, robots and various other “clever” mechanisms and methods of Artificial Intelligence (AI) have already replaced humans in an increasing number of routine jobs. This continuous development of new technologies could create many new, yet unforeseen jobs in the future. As a result, formal education, from elementary school to university, is faced with the great challenge of preparing students for a new way of life in a rather uncertain future of the forthcoming era of a new, but not yet explicitly known, industrial revolution, as the outcomes have not yet been fully determined.

The objective of the present work is to express some thoughts about this challenge and the difficulties connected to it. In no case, however, can this chapter be considered as an attempt to fully analyse the topic mentioned above, because such an effort requires hundreds of pages, as most of the subjects related to education need to be integrated. The focus here is turned mainly to the role computers and AI could play in future education and the risks associated with this perspective.

The rest of the chapter is organised as follows: In the Background Section the traditional learning theories and teaching methods are exposed and a connection is made between the past industrial revolutions and the forthcoming new one, which could be characterised as the era of the Internet of Things and Energy (IoT & E) and the Cyber-Physical Systems (CPS) controlled through this type of advanced Internet. The Main Focus of the chapter discusses the role of computers and Computational Thinking (CT) in modern education, the recent developments and perspectives of introducing methods and mechanisms of AI to education and in particular of Case-Based Reasoning (CBR), Bayesian Reasoning and Fuzzy Logic (FL). Future directions of research and final conclusions follow, and the chapter closes with a list of references and additional readings, as well as a summary of the key terms and definitions contained therein.

Key Terms in this Chapter

Internet of Energy (IoE): This refers to the upgrading and automating of electricity infrastructures for energy producers and distributors. The IoE allows energy production and distribution to function more efficiently and cleanly with less waste. It is connected to the IoT. Large energy consumers, such as heaters, washing machines and boilers could be switched on when there is sufficient energy in the grid.

Case-Based Reasoning (CBR): CBR is the process of solving problems based on previously solved similar problems (past cases). The use of computers enables a CBR system to build a continuously increasing “library” of past cases and to retrieve them for solving new problems.

Computational Thinking (CT): The term CT, coined by Jeannette Wing in 2006, describes solving problems, designing systems, and understanding human behaviour based on the principles of computer science. CT includes analysing and organising data, automated problem solving and using it to solve similar problems. Nowadays, CT has become necessary to solve complex technological problems. If sufficient background knowledge is available and the necessary new knowledge is acquired through critical thinking, CT may help to solve the problem. CT is actually a hybrid of several other modes of thinking, like abstract, logical, algorithmic, constructive and modelling thinking, which summarises all previous modes for solving the corresponding problem.

Fuzzy Logic (FL): An infinite-valued on the real interval [0, 1] logic, defined with the help of the concept of the Zadeh’s fuzzy set, that extends and completes the traditional bivalent logic and has found nowadays applications to almost all sectors of the human activity.

Artificial Intelligence (AI): AI is a branch of Computer Science that focuses on the creation of intelligent machines which mimic human reasoning and behavior.

Bayesian Reasoning: The Bayes’ theorem calculates the conditional probability P(A/B) of an event A to happen when the event B has already happened, with the help of the inverse in time conditional probability P(B/A), the prior probability P(A) and the posterior probability P(B). Since the changes of the value of P(A) result to different values of P(A/B), Bayesian Reasoning defines a multi-valued logic treating the existing due to the imprecision of the values of P(A) uncertainty in a way analogous to fuzzy logic. Therefore, Bayesian Reasoning could be considered as an interface between bivalent and fuzzy logic. Recent researches acknowledge the important role of Bayesian reasoning to everyday life and AI applications and for the whole science in general.

Cyber-Physical Systems (CPS): Systems controlled through the Internet by computer programs, such as autonomous automobiles, autonomous control systems, distance medicine and robots.

Internet of Things and Energy (IoT): This is a system of interrelated mechanical and digital devices that interact via the internet without requiring human-to-human or human-to-computer interaction ( ). Products which use IoT technology are typically in the field of “smart home” like lighting, heating, security systems, cameras, appliances. Amazon’s Alexa is another example of the IoT, providing services such as music, mail orders or switching smart home devices on and off in response to spoken commands.

Industrial Revolutions (IRs): A revolution is generally defined as a rapid and massive series of changes that lead to a radical transformation of human society. The first IR (1IR) began at the end of the 18 th century and was characterized by the replacement of manual labour based on steam and water power. The second IR (2IR) began in the mid-19 th century, used the power of electricity and was characterized by the mass production of goods. The third IR (3IR) started during the 1940’s and is characterized as the era of automation, in which computers replaced humans as means of control. The upcoming fourth IR (4IR), although not yet explicitly known, could be described as the era of the Internet of Things and Energy and Cyber Physical Systems. Some social thinkers consider the 1IR and 2IR as the 1IR, which makes the upcoming 4IR to be considered as the 3IR.

Social Robots: Social robots are AI devices to interact with humans and other robots. They may understand speech and facial expressions, and are used at home, and in customer service, education, etc. Examples of educational applications include the Tico robot, which was developed to improve children’s motivation in the classroom, and the Bandit robot, which was developed to teach social behaviour to autistic children, etc.

Flipped Learning (FL): FL is a mixed process that involves both online and face-to-face teaching and requires turning around the didactic processes to which we are accustomed. The students acquire new knowledge outside the classroom through the use of digital platforms and technological tools. On the other hand, the homework is done in the classroom under the supervision of a teacher in order to promote the adequacy of learning and student autonomy and increase the time spent to practicing, problem solving and deepening of content.

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