scholarly journals Artificial Intelligence in Education: A Bibliometric Study

2021 ◽  
pp. 822-837
Author(s):  
Tarik Talan
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Tutoring Systems ◽  
Web Of Science ◽  
State University ◽  
Arizona State University ◽  
Tutoring Systems ◽  
University Of Memphis ◽  
Artificial Intelligence In Education ◽  
The Usa ◽  
The Subject

The aim of this study is to examine the studies in the literature on the use of artificial intelligence in education in terms of its bibliometric properties. The Web of Science (WoS) database was used to collect the data. Various keywords were used to search the literature, and a total of 2,686 publications on the subject published between 2001-2021 were found. The inquiry revealed that most of the studies were carried out in the USA. According to the results, it was seen that the most frequently published journals were Computers Education and International Journal of Emerging Technologies in Learning. The study showed that the institutions of the authors were in the first place as Carnegie Mellon University, University of Memphis and Arizona State University as the most productive organizations due to the number of their publications, while Vanlehn, K. and Chen, C. –M. were the most effective and productive researchers. As a result of the analysis, it was determined that the co-authorship network structure was predominantly USA, Taiwan and United Kingdom. In addition, when the keywords mentioned together were mapped, it was seen that the words artificial intelligence, intelligent tutoring systems, machine learning, deep learning and higher education were used more frequently.

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.

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.

Multiagent Based Selection of Tutor-Subject-Student Paradigm in an Intelligent Tutoring System

2010 ◽  
Vol 6 (1) ◽  
pp. 46-70 ◽  
Author(s):  
Kiran Mishra ◽  
R.B. Mishra
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Teaching Strategy ◽  
Intelligent Tutoring System ◽  
Selection Index ◽  
Tutoring Systems ◽  
Analytical Reasoning ◽  
The Subject ◽  
Effective Models ◽  
Selection Of

Intelligent tutoring systems (ITS) aim at development of two main interconnected modules: pedagogical module and student module .The pedagogical module concerns with the design of a teaching strategy which combines the interest of the student, tutor’s capability and characteristics of subject. Very few effective models have been developed which combine the cognitive, psychological and behavioral components of tutor, student and the characteristics of a subject in ITS. We have developed a tutor-subject-student (TSS) paradigm for the selection of a tutor for a particular subject. A selection index of a tutor is calculated based upon his performance profile, preference, desire, intention, capability and trust. An aptitude of a student is determined based upon his answering to the seven types of subject topic categories such as Analytical, Reasoning, Descriptive, Analytical Reasoning, Analytical Descriptive, Reasoning Descriptive and Analytical Reasoning Descriptive. The selection of a tutor is performed for a particular type of topic in the subject on the basis of a student’s aptitude.

scholarly journals Machine learning challenges and impact: an interview with Thomas Dietterich

2017 ◽  
Vol 5 (1) ◽  
pp. 54-58 ◽  
Author(s):  
Zhi-Hua Zhou
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Driving Force ◽  
Professor Emeritus ◽  
State University ◽  
Learning Society ◽  
Learning Challenges ◽  
The World ◽  
The Usa ◽  
Oregon State University

Abstract Machine learning is the driving force of the hot artificial intelligence (AI) wave. In an interview with NSR, Prof. Thomas Dietterich, the distinguished professor emeritus of computer science at Oregon State University in the USA, the former president of Association of Advancement of Artificial Intelligence (AAAI, the most prestigious association in the field of artificial intelligence) and the founding president of the International Machine Learning Society, talked about exciting recent advances and technical challenges of machine learning, as well as its big impact on the world.

scholarly journals A Bibliometric Review of Publications on Oxidative Stress and Chemobrain: 1990–2019

Antioxidants ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 439
Author(s):  
Taylor McElroy ◽  
Antiño R. Allen
Keyword(s):  
Breast Cancer ◽  
Oxidative Stress ◽  
Central Nervous System ◽  
Web Of Science ◽  
Breast Cancer Patients ◽  
Chemotherapy Treatment ◽  
The Usa ◽  
Bibliometric Review ◽  
The Subject ◽  
The Web

Oxidative stress is considered one of the possible mechanisms behind chemobrain or the cognitive dysfunction persistent after chemotherapy treatment. Breast cancer patients have reported chemobrain symptoms since the 1990s. In this present bibliometric review, we employed the VOSviewer tool to describe the existing landscape on literature concerning oxidative stress, breast cancer chemotherapies, and chemobrain. As of 2019, 8799 papers were listed in the Web of Science database, with more than 900 papers published each year. As expected, terms relating to oxidative stress, mitochondria, breast cancer, and antioxidants have occurred very often in the literature throughout the years. In recent years, there has been an increase in the occurrence of terms related to nanomedicine. Only within the last decade do the keywords ‘brain’, ‘blood-brain barrier’, and ‘central nervous system’ appear, reflecting an increased interest in chemobrain. China has become the most prolific producer of oxidative stress and chemotherapy related papers in the last decade followed by the USA and India. In conclusion, the subject of oxidative stress as a mechanism behind chemotherapies’ toxicities is an active area of research.

scholarly journals Domain-General Tutor Authoring with Apprentice Learner Models

2020 ◽  
Author(s):  
Christopher J. MacLellan ◽  
Kenneth R. Koedinger
Keyword(s):  
Artificial Intelligence ◽  
Experimental Design ◽  
Prior Knowledge ◽  
Computational Models ◽  
Tree Model ◽  
Tutoring Systems ◽  
Interactive Machine Learning ◽  
Expert Model ◽  
Wide Range ◽  
Artificial Intelligence In Education

Abstract Intelligent tutoring systems are effective for improving students’ learning outcomes (Pane et al. 2013; Koedinger and Anderson, International Journal of Artificial Intelligence in Education, 8, 1–14, 1997; Bowen et al. Journal of Policy Analysis and Management, 1, 94–111 2013). However, constructing tutoring systems that are pedagogically effective has been widely recognized as a challenging problem (Murray 2003; Murray, International Journal of Artificial Intelligence in Education, 10, 98–129, 1999). In this paper, we explore the use of computational models of apprentice learning, or computer models that learn interactively from examples and feedback, for authoring expert-models via demonstrations and feedback (Matsuda et al. International Journal of Artificial Intelligence in Education, 25(1), 1–34 2014) across a wide range of domains. To support these investigations, we present the Apprentice Learner Architecture, which posits the types of knowledge, performance, and learning components needed for apprentice learning. We use this architecture to create two models: the Decision Tree model, which non-incrementally learns skills, and the Trestle model, which instead learns incrementally. Both models draw on the same small set of prior knowledge (six operators and three types of relational knowledge) to support expert model authoring. Despite their limited prior knowledge, we demonstrate their use for efficiently authoring a novel experimental design tutor and show that they are capable of learning an expert model for seven additional tutoring systems that teach a wide range of knowledge types (associations, categories, and skills) across multiple domains (language, math, engineering, and science). This work shows that apprentice learner models are efficient for authoring tutors that would be difficult to build with existing non-programmer authoring approaches (e.g., experimental design or stoichiometry tutors). Further, we show that these models can be applied to author tutors across eight tutor domains even though they only have a small, fixed set of prior knowledge. This work lays the foundation for new interactive machine-learning based authoring paradigms that empower teachers and other non-programmers to build pedagogically effective educational technologies at scale.

Bibliographies on Health Promotion Programs for Business and Industry

1982 ◽  
Vol 3 (3) ◽  
pp. 211-247
Author(s):  
Francine Martin ◽  
Suzanne Dandoy ◽  
Bradford Kirkman-Liff ◽  
Stacy Chaconas
Keyword(s):  
Health Promotion ◽  
New Ventures ◽  
The United States ◽  
State University ◽  
Arizona State University ◽  
Health Services Administration ◽  
Health Promotion Programs ◽  
Rapid Pace ◽  
The Subject ◽  
Made In

Health promotion programs have been developing at a rapid pace throughout the United States. Business and industry have been major targets for and supporters of these new ventures. This intense interest in health promotion programs has produced a need for a systematic review of past experience. The Center for Health Services Administration at Arizona State University prepared two comprehensive bibliographies of references on occupational health promotion programs. The annotated bibliography includes ninety references that were deemed most relevant to the subject at the time the searches were made in Spring of 1982. The second bibliography, which is not annotated, is supplemental and provides eighty-eight additional related references.

scholarly journals Report on the Thirtieth International Florida Artificial Intelligence Research Society Conference (FLAIRS-30)

AI Magazine ◽  
2017 ◽  
Vol 38 (3) ◽  
pp. 70-71
Author(s):  
Vasile Rus ◽  
Zdravko Markov ◽  
Ingrid Russell
Keyword(s):  
Artificial Intelligence ◽  
Research Laboratory ◽  
Research Society ◽  
State University ◽  
Army Research Laboratory ◽  
Artificial Intelligence Research ◽  
University Of Memphis ◽  
Beach Resort ◽  
The University

The 30th International Florida Artificial Intelligence Research Society Conference (FLAIRS-30) was held May 22–24, 2017, at the Hilton Marco Island Beach Resort and Spa in Marco Island, Florida, USA. The conference events included invited speakers, special tracks, and presentations of papers, posters, and awards. The conference chair was Ingrid Russell from the University of Hartford. The program cochairs were Vasile Rus from The University of Memphis and Zdravko Markov from Central Connecticut State University. The special tracks were coordinated by Keith Brawner from the Army Research Laboratory.

scholarly journals Out of the Laboratory and into the Classroom: The Future of Artificial Intelligence in Education

2020 ◽  
Author(s):  
Daniel Schiff
Keyword(s):  
Artificial Intelligence ◽  
Peer Review ◽  
Autonomous Vehicles ◽  
Educational Systems ◽  
Public Attention ◽  
Policy And Practice ◽  
Socially Responsible ◽  
Tutoring Systems ◽  
The Status ◽  
Artificial Intelligence In Education

[This is a post-peer-review, pre-copy edit version of an article in AI & Society. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00146-020-01033-8 or https://rdcu.be/b6avX.] Like previous educational technologies, artificial intelligence in education (AIEd) threatens to disrupt the status quo, with proponents highlighting the potential for efficiency and democratization, and skeptics warning of industrialization and alienation. However, unlike frequently discussed applications of AI in autonomous vehicles, military and cybersecurity concerns, and healthcare, AI’s impacts on education policy and practice have not yet captured the public attention. This paper therefore evaluates the status of AIEd, with special attention to intelligent tutoring systems and anthropomorphized artificial educational agents. I discuss AIEd’s purported capacities, including the abilities to simulate teachers, provide robust student differentiation, and even foster socioemotional engagement. Next, in order to situate developmental pathways for AIEd going forward, I contrast sociotechnical possibilities and risks through two idealized futures. Finally, I consider a recent proposal to use peer review as a gatekeeping strategy to prevent harmful research. This proposal serves as a jumping off point for recommendations to AIEd stakeholders towards improving their engagement with socially responsible research and implementation of AI in educational systems.

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