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

Policy Makers ◽

Tutoring Systems ◽

Research And Practice ◽

The Past ◽

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.

Advances in Early Childhood and K-12 Education - Handbook of Research on Critical Issues in Special Education for School Rehabilitation Practices ◽

Artificial Intelligence in Education

10.4018/978-1-7998-7630-4.ch014 ◽

2021 ◽

pp. 256-277

Author(s):

Rashmi Khazanchi ◽

Pankaj Khazanchi

Keyword(s):

Artificial Intelligence ◽

Intelligent Tutoring ◽

Pedagogical Practices ◽

Student Centered ◽

Tutoring Systems ◽

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

Agent-Based Tutoring Systems by Cognitive and Affective Modeling ◽

10.4018/978-1-59904-768-3.ch011 ◽

2008 ◽

pp. 228-259 ◽

Cited By ~ 4

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 ◽

Reference Model ◽

Intelligent Tutoring ◽

Tutoring Systems ◽

Education Community ◽

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.

Artificial Intelligence in Education: A Bibliometric Study

International Journal of Research in Education and Science ◽

10.46328/ijres.2409 ◽

2021 ◽

pp. 822-837

Author(s):

Tarik Talan

Keyword(s):

Artificial Intelligence ◽

Web Of Science ◽

State University ◽

Arizona State University ◽

Tutoring Systems ◽

University Of Memphis ◽

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.

ASPECTS REGARDING THE USE OF ARTIFICIAL INTELLIGENCE IN IMPROVING DEFENSE RESOURCE MANAGEMENT

STRATEGIES XXI - Command and Staff College ◽

10.53477/2668-2028-21-42 ◽

2021 ◽

Vol 17 (1) ◽

pp. 323-330

Author(s):

Gabriela-Florina NICOARĂ ◽

Gergonia-Cristiana BOGĂȚEANU

Keyword(s):

Artificial Intelligence ◽

Resource Management ◽

Technological Progress ◽

Digital Systems ◽

Constructive Approach ◽

The Past ◽

A Priority ◽

High Level

Abstract: Regarding the society evolution dominated by a high-level technology, we consider this article a constructive approach. The aim of the paper is to highlight a few activities/places/spots in which competences of humans/soldiers interfere with different elements of the artificial intelligence. We deem that the technological progress in the past few years has been impressive. Nowadays, thousands of activities that were mostly or exclusively executed by people can be done faster and often with greater precision using digital systems. In this instance and considering the achievement of functional compatibility between Romanian Army and forces from NATO as being a priority, the development of the technology based on artificial intelligence is vital within the defense resource management.

Artificial Intelligence In Education

Domain-General Tutor Authoring with Apprentice Learner Models

International Journal of Artificial Intelligence in Education ◽

10.1007/s40593-020-00214-2 ◽

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 ◽

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.

Artificial Intelligence In Education

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

10.31235/osf.io/uqvzd ◽

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 ◽

[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.

Review of Intelligent Tutoring Systems: At the Crossroads of Artificial Intelligence and Education.

Contemporary Psychology ◽

10.1037/031336 ◽

1991 ◽

Vol 36 (12) ◽

pp. 1114-1114

Author(s):

No authorship indicated

Keyword(s):

Artificial Intelligence ◽

Intelligent Tutoring ◽

Tutoring Systems

Handbook of Research on Learning in the Age of Transhumanism - Advances in Educational Technologies and Instructional Design ◽

Artificial Intelligence in Education

10.4018/978-1-5225-8431-5.ch014 ◽

2019 ◽

pp. 224-236 ◽

Cited By ~ 7

Author(s):

Nil Goksel ◽

Aras Bozkurt

Keyword(s):

Artificial Intelligence ◽

Learning Styles ◽

Language Processing ◽

Adaptive Learning ◽

Future Perspectives ◽

Tutoring Systems ◽

Educational Processes ◽

Key Concepts ◽

Educational Perspective

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.

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

10.22541/au.162317004.45114437/v1 ◽

2021 ◽

Author(s):

Robert Hoffman ◽

William Clancey

Keyword(s):

Artificial Intelligence ◽

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.

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

Multidisciplinary Computational Intelligence Techniques ◽

10.4018/978-1-4666-1830-5.ch015 ◽

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 ◽

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.