scholarly journals Artificial Intelligence in Education

2021 ◽  
Author(s):  
Andrej Flogie ◽  
Boris Aberšek
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Tutoring ◽  
General System ◽  
Knowledge Based Systems ◽  
Tutoring Systems ◽  
Knowledge Based ◽  
Artificial Intelligence In Education ◽  
User Friendly ◽  
Self Learning ◽  
Negative Training

Information technology, through networking, knowledge-based systems and artificial intelligence, interactive multimedia, and other technologies, plays an increasingly important role, which will even increase in the future, in the way that education is taught and delivered to the student. For this reason, we decided to present some ideas for such learning-training environments in education in this chapter. Like many researchers in other countries, we are also developing a user-friendly general system, designed particularly for solving problems. It is based on experience-based intelligent tutoring systems, and intended primarily for executing better lessons and for students’ self-learning. Like all powerful tools, experience-based AI design approaches must be applied carefully. Without a carefully designed experience and extensive testing, these systems could easily result in unwanted outcomes (such as negative training or increased phobia anxiety). Despite the promise of the early efforts, the best approaches to designing these experiences are still topics of research and debate. Any technology as powerful as AI provokes many general social and ethical questions in all of us. Does AI make killing by remote control too consequence-free? Do AI models systematize existing biases? What will AI do when it enters education? We will try to provide an answer to this question in the following chapter.

Artificial Intelligence in Education

2021 ◽  
pp. 256-277
Author(s):  
Rashmi Khazanchi ◽  
Pankaj Khazanchi
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Pedagogical Practices ◽  
Student Centered ◽  
Tutoring Systems ◽  
Artificial Intelligence In Education ◽  
K 12 ◽  
Insight Into

Current educational developments in theories and practices advocate a more personalized, student-centered approach to teach 21st-century skills. However, the existing pedagogical practices cannot provide optimal student engagement as they follow a ‘one size fits all' approach. How can we provide high-quality adaptive instructions at a personalized level? Intelligent tutoring systems with embedded artificial intelligence can assist both students and teachers in providing personalized support. This chapter highlights the role of artificial intelligence in the development of intelligent tutoring systems and how these are providing personalized instructions to students with and without disabilities. This chapter gives insight into the challenges and barriers posed by the integration of intelligent tutoring systems in K-12 classrooms.

Constructing Intelligent Tutoring Systems Based on a Multiagent Architecture

2008 ◽  
pp. 228-259 ◽  
Author(s):  
Ig Bittencourt ◽  
Evandro de Barros Costa ◽  
Baldoíno Fonseca dos Santos Neto ◽  
João Guilherme Maia de Menezes ◽  
Jairo Simão Santana Melo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Tutoring Systems ◽  
Reference Model ◽  
Intelligent Tutoring ◽  
Tutoring Systems ◽  
Education Community ◽  
Artificial Intelligence In Education ◽  
Relevant Topic ◽  
Pedagogical Models

Tools to make the development of intelligent tutoring systems (ITS) easier and more efficient are a relevant topic within the artificial intelligence in education community. This chapter presents a set of tools for constructing multiagent-based ITS, and describes a methodology for guiding the development of ITS. The main goal is to make multiagent-based ITS development more efficient and useful for both developers and authors. This has been done to support development of tutors based on Mathema’s environment as a reference model. Basically, in order to create a particular ITS, authors have to consider three main steps concerned with domain, student, and pedagogical models. A case study is presented to demonstrate the effectiveness of the proposed approach. Results of this case study show that this proposal makes the process of building the considered ITS easier and more efficient.

A Survey of Semantic Web Based Architectures for Adaptive Intelligent Tutoring System

2012 ◽  
pp. 239-256
Author(s):  
Suraiya Jabin ◽  
K. Mustafa
Keyword(s):  
Artificial Intelligence ◽  
Semantic Web ◽  
Intelligent Tutoring ◽  
Main Idea ◽  
Semantic Web Technologies ◽  
Web Based ◽  
Tutoring Systems ◽  
Web Technologies ◽  
Artificial Intelligence In Education ◽  
Web Based Education

Most recently, IT-enabled education has become a very important branch of educational technology. Education is becoming more dynamic, networked, and increasingly electronic. Today’s is a world of Internet social networks, blogs, digital audio and video content, et cetera. A few clear advantages of Web-based education are classroom independence and availability of authoring tools for developing Web-based courseware, cheap and efficient storage and distribution of course materials, hyperlinks to suggested readings, and digital libraries. However, there are several challenges in improving Web-based education, such as providing for more adaptivity and intelligence. The main idea is to incorporate Semantic Web technologies and resources to the design of artificial intelligence in education (AIED) systems aiming to update their architectures to provide more adaptability, robustness, and richer learning environments. The construction of such systems is highly complex and faces several challenges in terms of software engineering and artificial intelligence aspects. This chapter addresses state of the art Semantic Web methods and tools used for modeling and designing intelligent tutoring systems (ITS). Also it draws attention of Semantic Web users towards e-learning systems with a hope that the use of Semantic Web technologies in educational systems can help the accomplishment of anytime, anywhere, anybody learning, where most of the web resources are reusable learning objects supported by standard technologies and learning is facilitated by intelligent pedagogical agents, that may be adding the essential instructional ingredients implicitly.

scholarly journals Modeling Human-Like Robustness in an Intelligent Tutoring System

1970 ◽  
Vol 3 (1) ◽  
pp. 2-8
Author(s):  
Hameedullah Kazi ◽  
Peter Haddawy ◽  
Siriwan Suebnukarn
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Medical Problem ◽  
Knowledge Based Systems ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Specific Solution ◽  
Knowledge Based ◽  
Narrow Scope

Intelligent tutoring systems are no different from other knowledge based systems in that they are often plagued by brittleness. Intelligent tutoring systems for problem solving are typically loaded with problem scenarios for which specific solutions are constructed. Solutions presented by students, are compared against these specific solutions, which often leads to a narrow scope of reasoning, where students are confined to reason towards a specific solution. Student solutions that are different from the specific solution entertained by the system are rejected as being incorrect, even though they may be acceptable or close to acceptable. This leads to brittleness in tutoring systems in evaluating student solutions and returning appropriate feedback. In this paper we discuss a few human-like attributes in the context of robustness that are desirable in knowledge based systems. We then present a model of reasoning through which a tutoring system for medical problem-based learning, can begin to exhibit human-like robust behavior in evaluating solutions in a broader context using UMLS, and respond with hints that are mindful of the partial correctness of the student solution.

scholarly journals Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021

AI and Ethics ◽  
2021 ◽  
Author(s):  
Muhammad Ali Chaudhry ◽  
Emre Kazim
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Tutoring Systems ◽  
Policy Makers ◽  
Tutoring Systems ◽  
Research And Practice ◽  
The Past ◽  
Artificial Intelligence In Education ◽  
Potential Impact ◽  
High Level ◽  
Contextualized Learning

AbstractIn the past few decades, technology has completely transformed the world around us. Indeed, experts believe that the next big digital transformation in how we live, communicate, work, trade and learn will be driven by Artificial Intelligence (AI) [83]. This paper presents a high-level industrial and academic overview of AI in Education (AIEd). It presents the focus of latest research in AIEd on reducing teachers’ workload, contextualized learning for students, revolutionizing assessments and developments in intelligent tutoring systems. It also discusses the ethical dimension of AIEd and the potential impact of the Covid-19 pandemic on the future of AIEd’s research and practice. The intended readership of this article is policy makers and institutional leaders who are looking for an introductory state of play in AIEd.

scholarly journals Review of Artificial Intelligence

2020 ◽  
pp. 143-171
Author(s):  
K. P. V. Sai Aakarsh ◽  
Adwin Manhar
Keyword(s):  
Artificial Intelligence ◽  
Computational Intelligence ◽  
Medical Diagnosis ◽  
Computer Application ◽  
Knowledge Based Systems ◽  
Human Race ◽  
Knowledge Based ◽  
Ordinary Person ◽  
Digital Computers

Over many centuries, tools of increasing sophistication have been developed to serve the human race Digital computers are, in many respects, just another tool. They can perform the same sort of numerical and symbolic manipulations that an ordinary person can, but faster and more reliably. This paper represents review of artificial intelligence algorithms applying in computer application and software. Include knowledge-based systems; computational intelligence, which leads to Artificial intelligence, is the science of mimicking human mental faculties in a computer. That assists Physician to make dissection in medical diagnosis.

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