Relevant bibliographies by topics / Intelligent tutoring systems. Artificial intelligence. Computer-assisted instruction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters

Academic literature on the topic 'Intelligent tutoring systems. Artificial intelligence. Computer-assisted instruction'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Intelligent tutoring systems. Artificial intelligence. Computer-assisted instruction"

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Duchastel, Philippe, and Jacques Imbeau. "Intelligent Computer-assisted Instruction (ICAI): Flexible Learning through Better Student-Computer Interaction." Journal of Information Technology 3, no. 2 (1988): 102–5. http://dx.doi.org/10.1177/026839628800300206.

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Intelligent computer-assisted instruction (ICAI) uses artificial intelligence techniques to imitate in computer form the power of human tutorial processes. Its major technical features are the use of a modularized knowledge base instead of CAI's textual scripts, and the ability to interpret the student's statements and questions expressed in natural English. These features make ICAI systems extremely flexible and allow the student much greater learner control of the interaction than is traditionally possible in CAI. Other major elements of an ICAI system are an extensive model of the student a
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Lamia, Mahnane, and Hafidi Mohamed. "A Problem Solving Using Intelligent Social Network." International Journal of Web-Based Learning and Teaching Technologies 14, no. 3 (2019): 28–38. http://dx.doi.org/10.4018/ijwltt.2019070103.

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Nowadays, students are becoming familiar with the computer technology at a very early age. Moreover, the wide availability of the internet gives a new perspective to distance education making e-learning environments crucial to the future of education. Intelligent tutoring systems (ITSs) provide sophisticated tutoring systems using artificial intelligence techniques in computer programs to facilitate instruction. ITSs support problem solving by providing adaptive scaffolding in terms of feedback, guidance, problem selection and other types of help. Little attention has been devoted thus far to
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Khan, Tariq, and Yau Jim Yip. "CBT II—Case-based computer-aided instruction: survey of principles, applications and issues." Knowledge Engineering Review 10, no. 3 (1995): 235–68. http://dx.doi.org/10.1017/s0269888900007475.

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AbstractIn this paper we consider the role case-based reasoning has had in development of computer-aided instruction systems. We survey several case-based teaching systems, each of which is representative of a basic pedagogical principle that motivated its development. Firstly, 15 pedagogical principles are presented that were identified from the analysis of case-based teaching systems. We present some background to the principles, and indicate which systems they are incorporated in. Next, the teaching systems themselves are described, with emphasis on how case-based reasoning has been applied
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Tafazoli, Dara, Elena Gómez María, and Cristina A. Huertas Abril. "Intelligent Language Tutoring System." International Journal of Information and Communication Technology Education 15, no. 3 (2019): 60–74. http://dx.doi.org/10.4018/ijicte.2019070105.

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Intelligent computer-assisted language learning (ICALL) is a multidisciplinary area of research that combines natural language processing (NLP), intelligent tutoring system (ITS), second language acquisition (SLA), and foreign language teaching and learning (FLTL). Intelligent tutoring systems (ITS) are able to provide a personalized approach to learning by assuming the role of a real teacher/expert who adapts and steers the learning process according to the specific needs of each learner. This article reviews and discusses the issues surrounding the development and use of ITSs for language le
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Teng, Zhonghong, and Shukai Cai. "Application of Computer-Aided Instruction (CAI) in the Physical Education: Survey Analysis of Chinese Universities." Journal of Healthcare Engineering 2021 (September 24, 2021): 1–6. http://dx.doi.org/10.1155/2021/1328982.

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With the advent of artificial intelligence, computer-aided instruction has developed into intelligent computer-aided instruction. Computer-aided instruction not only overcomes many weaknesses of traditional physical education teaching but also significantly increases the teaching effect and efficiency. This article first studies and analyzes the characteristics of computer-aided instruction systems. Then, we use statistical methods and logical analysis to randomly select two classes of undergraduate students as the experimental and control group, respectively. In the control group, the traditi
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Zhu, Lijin. "Computer Vision-Driven Evaluation System for Assisted Decision-Making in Sports Training." Wireless Communications and Mobile Computing 2021 (August 26, 2021): 1–7. http://dx.doi.org/10.1155/2021/1865538.

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Computer vision has become a fast-developing technology in the field of artificial intelligence, and its application fields are also expanding, thanks to the rapid development of deep learning. It will be of great practical value if it is combined with sports. When a traditional exercise assistance system is introduced into sports training, the athlete’s training information can be obtained by monitoring the exercise process through sensors and other equipment, which can assist the athlete in retrospectively analyzing the technical actions. However, the traditional system must be equipped with
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Caeiro Rodríguez, Manuel, Fernando Ariel Mikic-Fonte, Jorge Fontenla-González, Roberto Pérez-Rodríguez, Juan Carlos Burguillo-Rial, and Martín Llamas-Nistal. "Adding TQ-BOT into a Third-party Learning Management System." Informática na educação: teoria & prática 14, no. 1 (2011). http://dx.doi.org/10.22456/1982-1654.21903.

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Intelligent Tutoring Systems are computer programs that aim at providing personalized instruction to students. In recent years, artificial intelligence conversational robots, usually known as chatterbots, have become very popular in the Internet. In this paper we show how chatterbots can be integrated in e-Learning Systems. To perform such an integration the Service Oriented Architecture paradigm is adopted and e-learning standardization initiatives are considered. A middleware is provided to enable the integration and reuse of chatterbots by e-Learning systems enabling a tight control of thei
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Jia, Jiyou, and Yunfan He. "The design, implementation and pilot application of an intelligent online proctoring system for online exams." Interactive Technology and Smart Education ahead-of-print, ahead-of-print (2021). http://dx.doi.org/10.1108/itse-12-2020-0246.

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Purpose The purpose of this study is to design and implement an intelligent online proctoring system (IOPS) by using the advantage of artificial intelligence technology in order to monitor the online exam, which is urgently needed in online learning settings worldwide. As a pilot application, the authors used this system in an authentic university online exam and checked the proctoring result. Design/methodology/approach The IOPS adopts the B/S (Browser/Server) architecture. The server side is implemented with programming language C and Python and stores the identification data of all examinee
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Viccari, Rosa M., Demetrio A. Ovalle, and Jovani A. Jimenez. "ALLEGRO: Teaching/Learning Multi-Agent Environment using Instructional Planning and Cases- Based Reasoning (CBR)." CLEI Electronic Journal 10, no. 1 (2007). http://dx.doi.org/10.19153/cleiej.10.1.4.

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 This paper presents a description of the environments of individualized learning (Based on the Intelligent Tutoring Systems, ITS), the Computer Supported Collaborative Learning (CSCL), Multi-Agent Systems (MAS) and the artificial intelligence techniques called: Instruc- tional Planning (IP) and Case-Based Reasoning (CBR). Finally ALLEGRO is presented, a MAS environment of support to the teaching/learning process that includes all previous artificial in- telligence elements.
 
 
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Dissertations / Theses on the topic "Intelligent tutoring systems. Artificial intelligence. Computer-assisted instruction"

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Razzaq, Leena M. "Tutorial dialog in an equation solving intelligent tutoring system." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0107104-155853.

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Thesis (M.S.)--Worcester Polytechnic Institute.<br>Keywords: cognitive model; model-tracing; intelligent tutoring system; tutoring; artificial intelligence. Includes bibliographical references (p. 55-57).
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Csizmadia, Vilmos. "Constructing an authoring tool for intelligent tutoring systems with hierarchical domain models." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-1222103-161814.

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Vasandani, Vijay. "Intelligent tutoring for diagnostic problem solving in complex dynamic systems." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/24934.

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Hiesel, Jeanne C. "An approach to the analysis and design of an intelligent tutoring systems using an object-oriented methodology /." Online version of thesis, 1993. http://hdl.handle.net/1850/11716.

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Palitawanont, Nanta. "An Investigation into the Effectiveness of Intelligent Tutoring on Learning of College Level Statistics." Thesis, University of North Texas, 1989. https://digital.library.unt.edu/ark:/67531/metadc331166/.

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The present research incorporated the content of basic statistics into the Artificial Intelligence Physics Tutor (ARPHY), which was used as the expert system shell, and investigated the effects of the Artificial Intelligent Statistics Tutor (ARSTAT) as a supplement to learning statistics at the college level. Two classes of an introductory educational statistics course in the Department of Educational Foundations, University of North Texas, were used in the study. The daytime class was used as the experimental group and the evening class was used as the control group. The experimental group's
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Jarvis, Matthew P. "Applying machine learning techniques to rule generation in intelligent tutoring systems." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0429104-112724.

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Thesis (M.S.)--Worcester Polytechnic Institute.<br>Keywords: Intelligent Tutoring Systems; Model Tracing; Machine Learning; Artificial Intelligence; Programming by Demonstration. Includes bibliographical references.
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Brawner, Keith. "Modeling Learner Mood in Realtime through Biosensors for Intelligent Tutoring Improvements." Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5774.

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Computer-based instructors, just like their human counterparts, should monitor the emotional and cognitive states of their students in order to adapt instructional technique. Doing so requires a model of student state to be available at run time, but this has historically been difficult. Because people are different, generalized models have not been able to be validated. As a person's cognitive and affective state vary over time of day and seasonally, individualized models have had differing difficulties. The simultaneous creation and execution of an individualized model, in real time, rep
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Stanley, Berdenia Walker. "Hierarchical multiway partitioning strategy with hardware emulator architecture intelligence." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/13360.

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Wu, Binghui H. "Artificial teaching assistant : a framework for intelligent tutoring systems with abstract knowledge /." Diss., 1998. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:9831814.

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Zielke, Marjorie Ann. "Emergent pedagogical agents as assistive technology in creative, collaborative and expansive projects /." 2007. http://proquest.umi.com/pqdweb?did=1296112901&sid=1&Fmt=2&clientId=10361&RQT=309&VName=PQD.

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Books on the topic "Intelligent tutoring systems. Artificial intelligence. Computer-assisted instruction"

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Nkambou, Roger, Jacqueline Bourdeau, and Riichiro Mizoguchi. Advances in intelligent tutoring systems. Springer, 2010.

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World Conference on Artificial Intelligence in Education (7th 1995 August 16-19 Washington, D.C.). Artificial intelligence in education, 1995. Association for the Advancement of Computing in Education, 1995.

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IFIP, TC 3. Working Conference on Artificial Intelligence Tools in Education (1987 Frascati Italy). Artificial intelligence tools in education: Proceedings of the IFIP TC3 Working Conference on Artificial Intelligence Tools in Education, Frascati, Italy, 26-28 May, 1987. North-Holland, 1988.

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Paolo, Ercoli, Lewis R. 1940-, and IFIP Technical Committee 3, Education., eds. Artificial intelligence tools in education: Proceedings of the IFIP TC 3 Working Conference on Artificial Intelligence Tools in Education, Frascati, Italy, 26-28 May 1987. North-Holland, 1988.

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Li, Minghua. Ren gong zhi neng ji qi jiao yu ying yong. Ke xue chu ban she, 2008.

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Jiao yu ji shu yu ren gong zhi neng. Ji lin ta xue chu ban she, 2009.

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Artificial intelligence and tutoring systems: Computational and cognitive approaches to the communication of knowledge. Morgan Kaufmann Publishers, 1987.

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AAAI Fall Symposium (2000 North Falmouth, Mass.). Building dialogue systems for tutorial applications: Papers from the 2000 AAAI Fall Symposium, November 3-5, North Falmouth, Massachusetts. AAAI Press, 2000.

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World Conference on Artificial Intelligence in Education (1997 Kobe, Japan). Artificial intelligence in education, 1997: Knowledge and media in learning systems : proceedings of AI-ED 97, World Conference on Artificial Intelligence in Education, Kobe, Japan. IOS Press, 1997.

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Susan, Bull, Kay Judy, Mitrovic Antonija, and SpringerLink (Online service), eds. Artificial Intelligence in Education: 15th International Conference, AIED 2011, Auckland, New Zealand, June 28 – July 2011. Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Book chapters on the topic "Intelligent tutoring systems. Artificial intelligence. Computer-assisted instruction"

1

Bailey, Liston William. "New Technology for the Classroom." In Educational Technology and the New World of Persistent Learning. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6361-7.ch001.

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New developments in educational technologies are making it possible for teachers to bring technology into the classroom more than ever before. This chapter provides an overview of educational technologies related to mobile devices, artificial intelligence, intelligent tutoring systems, and robotics. As developments in computer technologies advance over time educators will need to cultivate better understanding and skills for using technology with a well conceptualized instructional design. Concepts are offered here to further discussions on how new technologies will support persistent and personalized learning in the not too distant future. Educational leaders must begin to think about on how to incorporate different technologies as well as potential resource constraints that school systems need to consider in order to achieve broader educational technology integration goals.
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