scholarly journals Artificial Intelligence and Its Role in Education

2021 ◽  
Vol 13 (22) ◽  
pp. 12902
Author(s):  
Sayed Fayaz Ahmad ◽  
Mohd. Khairil Rahmat ◽  
Muhammad Shujaat Mubarik ◽  
Muhammad Mansoor Alam ◽  
Syed Irfan Hyder
Keyword(s):  
Artificial Intelligence ◽  
Significant Role ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Education Sector ◽  
Access To Education ◽  
Tutoring Systems ◽  
Smart Learning ◽  
Modern Methods

The objective of this study is to explore the role of artificial intelligence applications (AIA) in education. AI applications provide the solution in many ways to the exponential rise of modern-day challenges, which create difficulties in access to education and learning. They play a significant role in forming social robots (SR), smart learning (SL), and intelligent tutoring systems (ITS) to name a few. The review indicates that the education sector should also embrace the modern methods of teaching and the necessary technology. Looking into the flow, the education sector organizations need to adopt AI technologies as a necessity of the day and education. The study needs to be tested statistically for better understanding and to make the findings more generalized in the future.

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.

Artificial Tutoring Systems: What Computers Can and Can't Know

1997 ◽  
Vol 16 (2) ◽  
pp. 107-124 ◽  
Author(s):  
Theodore W. Frick
Keyword(s):  
Intelligent Tutoring Systems ◽  
Clinical Evidence ◽  
Intelligent Tutoring ◽  
Computer Systems ◽  
Classroom Teachers ◽  
Language Understanding ◽  
Tutoring Systems ◽  
Natural Systems ◽  
Teachers And Students

After more than four decades, development of artificially intelligent tutoring systems has been constrained by two interrelated problems: knowledge representation and natural language understanding. G. S. Maccia's epistemology of intelligent natural systems implies that computer systems will need to develop qualitative intelligence before these problems can be solved. Recent research on how human nervous systems develop provides evidence for the significance of qualitative intelligence. Qualitative intelligence is required for understanding of culturally bound meanings of signs used in communication among intelligent natural systems. S. I. Greenspan provides neurological and clinical evidence that emotion and sensation are vital to the growth of mind—capabilities that computer systems do not currently possess. Therefore, we must view computers in education as media through which a multitude of teachers can convey their messages. This does not mean that the role of classroom teachers is diminished. Teachers and students can be empowered by these additional learning resources.

scholarly journals Methods and Standards for Research on Explainable Artificial Intelligence: Lessons from Intelligent Tutoring Systems

2021 ◽  
Author(s):  
Robert Hoffman ◽  
William Clancey
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Tutoring Systems ◽  
Explainable Artificial Intelligence ◽  
Explainable Ai

We reflect on the progress in the area of Explainable AI (XAI) Program relative to previous work in the area of intelligent tutoring systems (ITS). A great deal was learned about explanation—and many challenges uncovered—in research that is directly relevant to XAI. We suggest opportunities for future XAI research deriving from ITS methods, as well as the challenges shared by both ITS and XAI in using AI to assist people in solving difficult problems effectively and efficiently.

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.

scholarly journals ONLINE FUNCTIONAL LITERACY, INTELLIGENT TUTORING SYSTEMS AND SCIENCE EDUCATION

2015 ◽  
Vol 14 (2) ◽  
pp. 162-171
Author(s):  
Kosta Dolenc ◽  
Boris Aberšek ◽  
Metka Kordigel Aberšek
Keyword(s):  
Elementary School ◽  
Reading Comprehension ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Educational Process ◽  
Tutoring Systems ◽  
8Th Grade ◽  
On Line ◽  
Print Reading

We live in a time of transition from print reading (off-line) to screen reading (on-line), where the role of the book and other literature is being taken over by different types of electronic devices (computers, tablets, smart phones). In the lives of young people, there is less and less printed media, because it is being pushed out by electronic media. Most written media that is still used is thus bound to the classroom. However, in recent years schools have also become more like e-schools. It is almost impossible to find a school that does not use e-material in its educational process. Research indicates that there are differences in reading comprehension when reading off-line and on-line. In a study in which 78 students from the 8th grade of elementary school participated at the course Technology and science (n=77; 53.2% female), it was shown that in order to overcome this difference, individualised and adaptive Intelligent Tutoring Systems (ITS) can be used. The evaluation of the results also indicates that, for such a form of ITS, there is still plenty of space for optimisation, which is a permanent method of improvement and upgrade in such systems. Key words: reading comprehension, Technology and science, ITS, elementary school.

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