Artificial Intelligence in Education

2019 ◽  
pp. 224-236 ◽  
Author(s):  
Nil Goksel ◽  
Aras Bozkurt
Keyword(s):  
Artificial Intelligence ◽  
Learning Styles ◽  
Language Processing ◽  
Adaptive Learning ◽  
Future Perspectives ◽  
Tutoring Systems ◽  
Educational Processes ◽  
Key Concepts ◽  
Artificial Intelligence In Education ◽  
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.

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.

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.

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.

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 A Review on Artificial Intelligence in Education

2021 ◽  
Vol 10 (3) ◽  
pp. 206
Author(s):  
Jiahui Huang ◽  
Salmiza Saleh ◽  
Yufei Liu
Keyword(s):  
Artificial Intelligence ◽  
Education Reform ◽  
Adaptive Learning ◽  
Teaching And Learning ◽  
Rapid Development ◽  
Virtual Classroom ◽  
Teaching Evaluation ◽  
Innovative Technologies ◽  
Artificial Intelligence In Education ◽  
Positive Meaning

The emergence of innovative technologies has an impact on the methods of teaching and learning. With the rapid development of artificial intelligence (AI) technology in recent years, using AI in education has become more and more apparent. This article first outlines the application of AI in the field of education, such as adaptive learning, teaching evaluation, virtual classroom, etc. And then analyzes its impact on teaching and learning, which has a positive meaning for improving teachers' teaching level and students' learning quality. Finally, it puts forward the challenges that AI applications may face in education in the future and provides references for AI to promote education reform.   Received: 16 January 2021 / Accepted: 24 March 2021 / Published: 10 May 2021

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.

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 Systematic review of research on artificial intelligence applications in higher education – where are the educators?

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Olaf Zawacki-Richter ◽  
Victoria I. Marín ◽  
Melissa Bond ◽  
Franziska Gouverneur
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Systematic Review ◽  
Adaptive Systems ◽  
Teaching And Learning ◽  
Quantitative Methods ◽  
Empirical Studies ◽  
Tutoring Systems ◽  
Administrative Services ◽  
Artificial Intelligence In Education

Abstract According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.

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