Artificial Intelligence in Education: Current Insights and Future Perspectives

Artificial Intelligence in Education: Current Insights and Future Perspectives

Nil Goksel (Anadolu University, Turkey) and Aras Bozkurt (Anadolu University, Turkey)
Copyright: © 2019 |Pages: 13
DOI: 10.4018/978-1-5225-8431-5.ch014

Abstract

Though only a dream a while ago, artificial intelligence (AI) has become a reality, being now part of our routines and penetrating every aspect of our lives, including education. It is still a field in its infancy, but as time progresses, we will witness how AI evolves and explore its untapped potential. Against this background, this chapter examines current insights and future perspectives of AI in various contexts, such as natural language processing (NLP), machine learning, and deep learning. For this purpose, social network analysis (SNA) is used as a guide for the interpretation of the key concepts in AI research from an educational perspective. The research identified three broad themes: (1) adaptive learning, personalization and learning styles, (2) expert systems and intelligent tutoring systems, and (3) AI as a future component of educational processes.
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General Overview Of Artificial Intelligence (Ai)

Definition of AI

Nabiyev (2010) roughly defines AI as the ability of a computer-controlled device to perform tasks in a human-like manner. As indicated by the author, human-like qualities include mental processes like reasoning, meaning making, generalization, and learning from past experiences. Russell and Norvig (2003) describe the term AI as Machine Intelligence, or Computational Intelligence, that embraces various subfields wherein learning takes place and “specific tasks, such as playing chess, proving mathematical theorems, writing poetry, and diagnosing diseases, can be performed” (p. 2). Nilsson (2014) defines AI as the entirety of an algorithmic construction copying human intelligence. To Nilsson (2014), AI embraces the construction of the information-processing theory of intelligence. In other words, raw data, received from any user, is filtered by a device, made meaningful, and processed before finally becoming cooked data capable of meeting the demands of users.

There have been mind-blowing developments in the evolution of AI and the remarkable role it has played in human lives. Recently, there have been some concrete examples of AI being capable of learning how to think like a human. These examples have even demonstrated that AI-based applications, in some cases, can even function as better as humans. For example, in 2016, Google DeepMind’s AlphaGo defeated one of the world’s most accomplished “Go” players, Lee Se-Dol, a South Korean champion (Sang-Hun, 2016). As the greatest proof of AI’s human-like thinking and skills, the result of this match shows that a true artificially-intelligent system is one that can learn on its own (Adams, 2017).

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Vital Technologies That Support The Visions Of Ai

The below-given figure presents the chronological development and relation between Artificial Intelligence, Machine Learning and Deep Learning from 1950 to 2010 and beyond. As Figure 1 shows, AI, as a broad and advanced term for computer intelligence, started to be discussed between the 1950s and 1980s, which was followed by the introduction of Machine Learning technology between the 1980s and 2010, where learning through algorithms was brought to the agenda, and finally, after 2010, Deep Learning emerged as a breakthrough technique for implementing Machine Learning via neural networks to complete tremendously complex thinking tasks. In this context, the following sections examine the two vital technologies of machine learning and deep learning to better comprehend and explore the world of AI. In addition, Natural Language Processing (NLP) and one of its best examples, intelligent personal assistants, is discussed in detail.

Figure 1.

The relation between artificial intelligence, machine learning and deep learning

978-1-5225-8431-5.ch014.f01
(Copeland 2016)

Key Terms in this Chapter

Natural Language Processing: A subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) language.

Machine Learning: A field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn.

Intelligent Personal Assistant: Software that has been designed to assist people with basic tasks, typically providing information using natural language.

Intelligent Tutoring System: Artificial intelligence-based computer software that provides immediate and customized feedback to students/learners.

Deep Learning: A part of a broader family of machine learning methods based on learning data representations

Artificial Intelligence: The theory governing the development of computer systems that are able to perform tasks which normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

Intelligent Learning Environments: A form of computer software which employs artificial intelligence-based programs for (online) learning activities.

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