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Articles de revues sur le sujet « Intelligent tutoring systems »

Auteur : Grafiati

Publié le 4 juin 2021

Mis à jour le 25 juillet 2025

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1

Bradáč, Vladimír, and Kateřina Kostolányová. "Intelligent Tutoring Systems." Journal of Intelligent Systems 26, no. 4 (2017): 717–27. http://dx.doi.org/10.1515/jisys-2015-0144.

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AbstractThe importance of intelligent tutoring systems has rapidly increased in past decades. There has been an exponential growth in the number of ends users that can be addressed as well as in technological development of the environments, which makes it more sophisticated and easily implementable. In the introduction, the paper offers a brief overview of intelligent tutoring systems. It then focuses on two types that have been designed for education of students in the tertiary sector. The systems use elements of adaptivity in order to accommodate as many users as possible. They serve both a

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Anderson, J. R., C. F. Boyle, and B. J. Reiser. "Intelligent Tutoring Systems." Science 228, no. 4698 (1985): 456–62. http://dx.doi.org/10.1126/science.228.4698.456.

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ROSS, P. "Intelligent tutoring systems." Journal of Computer Assisted Learning 3, no. 4 (1987): 194–203. http://dx.doi.org/10.1111/j.1365-2729.1987.tb00331.x.

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Maher, Mary Lou. "Intelligent tutoring systems." Artificial Intelligence in Engineering 2, no. 1 (1987): 50. http://dx.doi.org/10.1016/0954-1810(87)90075-6.

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Auguste, Donna. "Intelligent tutoring systems." Artificial Intelligence 26, no. 2 (1985): 233–38. http://dx.doi.org/10.1016/0004-3702(85)90033-5.

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Stefik, Mark. "Intelligent tutoring systems." Artificial Intelligence 26, no. 2 (1985): 238–45. http://dx.doi.org/10.1016/0004-3702(85)90034-7.

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Ilić, Jelena, Mirjana Ivanović, and Aleksandra Klašnja-Milićević. "The impact of intelligent tutoring systems and artificial intelligence on students' motivation and achievement in STEM education: A systematic review." Journal of Educational Studies in Mathematics and Computer Science 1, no. 2 (2024): 5–18. https://doi.org/10.5937/jesmac2402005i.

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In recent years, STEM education, which encompasses science, technology, engineering, and mathematics, has experienced significant and dynamic progress. These advancements are characterised by the proliferation of scientific knowledge and the development of cutting-edge educational resources that rely on artificial intelligence technology, among other factors. A promising avenue for advancing formal education is presented by intelligent tutoring systems, which offer intelligent instruction and feedback, thereby facilitating a more personalized and practical learning experience. This study explo

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Del Soldato, Teresa. "Intelligent Tutoring Systems 92." AI Communications 5, no. 4 (1992): 215–16. http://dx.doi.org/10.3233/aic-1992-5411.

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Woolf, Beverly Park. "Intelligent multimedia tutoring systems." Communications of the ACM 39, no. 4 (1996): 30–31. http://dx.doi.org/10.1145/227210.227217.

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Nkambou, Roger, and Froduald Kabanza. "Designing intelligent tutoring systems." ACM SIGCUE Outlook 27, no. 2 (2001): 46–60. http://dx.doi.org/10.1145/381234.381246.

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Yazdani, M. "Intelligent tutoring systems survey." Artificial Intelligence Review 1, no. 1 (1986): 43–52. http://dx.doi.org/10.1007/bf01988527.

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Son, Taekwon. "Artificial Intelligence in Mathematics Education: A Systematic Literature Review on Intelligent Tutoring Systems." Korean Society of Educational Studies in Mathematics - Journal of Educational Research in Mathematics 34, no. 2 (2024): 187–208. http://dx.doi.org/10.29275/jerm.2024.34.2.187.

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This study synthesized 63 empirical studies integrating intelligent tutoring systems in mathematics education from 2003 to 2023 and systematically examined trends, effects, and limitations. As a result, research interest in intelligent teaching systems has continued to increase, but most of the research has been conducted in the fields of education and engineering. The intelligent tutoring systems were mainly applied at the elementary school level, focusing on numbers and arithmetic, algebra, and geometry. Most studies reported the positive effects of intelligent teaching systems, and six limi

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Petrovica, Sintija. "Tutoring Process in Emotionally Intelligent Tutoring Systems." International Journal of Technology and Educational Marketing 4, no. 1 (2014): 72–85. http://dx.doi.org/10.4018/ijtem.2014010106.

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Research has shown that emotions can influence learning in situations when students have to analyze, reason, make conclusions, apply acquired knowledge, answer questions, solve tasks, and provide explanations. A number of research groups inspired by the close relationship between emotions and learning have been working to develop emotionally intelligent tutoring systems. Despite the research carried out so far, a problem how to adapt tutoring not only to a student's knowledge state but also to his/her emotional state has been disregarded. The paper aims to examine to what extent the tutoring p

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Angelides, Marios C., and Amelia K. Y. Tong. "Implementing Multiple Tutoring Strategies in an Intelligent Tutoring System for Music Learning." Journal of Information Technology 10, no. 1 (1995): 52–62. http://dx.doi.org/10.1177/026839629501000107.

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Variation in tutoring strategies plays an important part in intelligent tutoring systems. The potential for providing an adaptive intelligent tutoring system depends on having a range of tutoring strategies to select from. In order to react effectively to the student's needs, an intelligent tutoring system has to be able to choose intelligently among the strategies and determine which strategy is best for an individual student at a particular moment. This paper describes, through the discussion pertaining to the implementation of SONATA, a music theory tutoring system, how an intelligent tutor

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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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VanLEHN, KURT. "The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems." Educational Psychologist 46, no. 4 (2011): 197–221. http://dx.doi.org/10.1080/00461520.2011.611369.

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Kulik, James A., and J. D. Fletcher. "Effectiveness of Intelligent Tutoring Systems." Review of Educational Research 86, no. 1 (2016): 42–78. http://dx.doi.org/10.3102/0034654315581420.

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Wu, Albert K. W., and M. C. Lee. "Intelligent tutoring systems as design." Computers in Human Behavior 14, no. 2 (1998): 209–20. http://dx.doi.org/10.1016/s0747-5632(98)00002-8.

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Blandford, A. E. "Intelligent tutoring systems: Lessons learned." Computers & Education 14, no. 6 (1990): 544–45. http://dx.doi.org/10.1016/0360-1315(90)90114-m.

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YAZDANI, MASOUD. "Intelligent tutoring systems: An overview." Expert Systems 3, no. 3 (1986): 154–63. http://dx.doi.org/10.1111/j.1468-0394.1986.tb00488.x.

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Johnson, W. Lewis. "Intelligent tutoring systems: Lessons learned." Artificial Intelligence 48, no. 1 (1991): 125–34. http://dx.doi.org/10.1016/0004-3702(91)90085-x.

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Baker, Ryan S. "Stupid Tutoring Systems, Intelligent Humans." International Journal of Artificial Intelligence in Education 26, no. 2 (2016): 600–614. http://dx.doi.org/10.1007/s40593-016-0105-0.

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Yu, Yan, and Jian Hua Wang. "The Study on the Key Technologies in Multiple Agent-Based Intelligent Tutoring System." Advanced Materials Research 846-847 (November 2013): 1889–92. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1889.

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Network teaching system has changed the way of teaching, with the artificial intelligence technology, Currently, many traditional network teaching system has been unable to meet the needs of the public.After studying the defects in current teaching systems and combining features of multi-agent systems and their application theories in intelligent teaching systems, this paper, integrating the concept of multi-agent systems, discusses the key technologies in multiple agent-based intelligent tutoring systems based on multiple agent-based intelligent tutoring system models. To achieve true intelli

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Gloria J. Martínez Bejarano. "Intelligent Tutoring Systems as A Solution to Emotional and Academic Challenges in Higher Education: Analysis of a Survey from Instituto Politécnico Nacional." Journal of Information Systems Engineering and Management 10, no. 6s (2025): 493–500. https://doi.org/10.52783/jisem.v10i6s.747.

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The use of artificial intelligence in education has opened new possibilities for personalizing teaching and providing comprehensive support to students. This study investigated the emotional and tutoring needs of students at the Instituto Politécnico Nacional, employing data mining techniques to analyze survey results. The methodology allowed the identification of key patterns related to academic motivations, emotional needs, and access to tutoring, offering a comprehensive view of the challenges faced by students. The findings reveal that 47.7% of students experience emotional problems, 70.4%

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Pappas, Marios, and Athanasios Drigas. "Incorporation of Artificial Intelligence Tutoring Techniques in Mathematics." International Journal of Engineering Pedagogy (iJEP) 6, no. 4 (2016): 12. http://dx.doi.org/10.3991/ijep.v6i4.6063.

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Intelligent Tutoring Systems incorporate Artificial Intelligence techniques, in order to imitate a human tutor. These expert systems are able to assess student’s proficiency, to provide solved examples and exercises for practice in each topic, as well as to provide immediate and personalized feedback to learners. The present study is a systematic review that evaluates the contribution of the Intelligent Tutoring Systems developed so far, to Mathematics Education, representing some of the most representative studies of the last decade.

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NIELSEN, RODNEY D., WAYNE WARD, and JAMES H. MARTIN. "Recognizing entailment in intelligent tutoring systems." Natural Language Engineering 15, no. 4 (2009): 479–501. http://dx.doi.org/10.1017/s135132490999012x.

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AbstractThis paper describes a new method for recognizing whether a student's response to an automated tutor's question entails that they understand the concepts being taught. We demonstrate the need for a finer-grained analysis of answers than is supported by current tutoring systems or entailment databases and describe a new representation for reference answers that addresses these issues, breaking them into detailed facets and annotating their entailment relationships to the student's answer more precisely. Human annotation at this detailed level still results in substantial interannotator

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Inoue, Yukiko. "Methodological Issues in the Evaluation of Intelligent Tutoring Systems." Journal of Educational Technology Systems 29, no. 3 (2001): 251–58. http://dx.doi.org/10.2190/b5vf-qk3f-pccd-64lb.

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Drill and practice, simulation, and tutorial are the main formats of computer-assisted instruction, yet tutorial format is particularly suitable to conceptual school subjects. When a knowledge module is added to the tutorial format, it is termed an intelligent tutoring system (ITS). Although ITS evaluation studies greatly influence interest in and support for the future ITS work, valid evaluation methodologies are lacking because ITS is fairly new. Consequently, this article examined the ITS evaluation studies, with the focus on the methodological issue. The critical component in ITS is the kn

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Glaze, Andrew, Patricia Moyer-Packenham, and Max Longhurst. "Teachers' Conceptions of Mathematics and the Use of Intelligent Tutoring Systems, Calculators, Dynamic Geometry Software and Desmos in the Classroom." Journal of Computers in Mathematics and Science Teaching 40, no. 3 (2021): 201–27. https://doi.org/10.70725/601390aategx.

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The purpose of this mixed-methods study was to investigate the relationship between teachers’ conceptions of mathematics and their use of intelligent tutoring systems for mathematics instruction. Intelligent tutoring systems are adaptive computer programs which administer mathematics instruction to students based on their cognitive state. A conception is a mixture of beliefs and knowledge. The participants in this study were 93 junior high school mathematics teachers from three school districts in the Midwest. Data were gathered using a two-part online survey. The first part of the survey cont

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Psotka, Joseph, Heinz Mandl, and Alan Lesgold. "Learning Issues for Intelligent Tutoring Systems." American Journal of Psychology 102, no. 4 (1989): 581. http://dx.doi.org/10.2307/1423312.

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Whitaker, Elizabeth T., and Ronald D. Bonnell. "Plan recognition in intelligent tutoring systems." Intelligent Tutoring Media 1, no. 2 (1990): 73–82. http://dx.doi.org/10.1080/14626269009409091.

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Yang, Feng-Jen. "The ideology of intelligent tutoring systems." ACM Inroads 1, no. 4 (2010): 63–65. http://dx.doi.org/10.1145/1869746.1869765.

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Elsom-Cook, Mark. "Student modelling in intelligent tutoring systems." Artificial Intelligence Review 7, no. 3-4 (1993): 227–40. http://dx.doi.org/10.1007/bf00849556.

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Gross, Sebastian, Bassam Mokbel, Barbara Hammer, and Niels Pinkwart. "Learning Feedback in Intelligent Tutoring Systems." KI - Künstliche Intelligenz 29, no. 4 (2015): 413–18. http://dx.doi.org/10.1007/s13218-015-0367-y.

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Sharples, Mike. "Intelligent tutoring systems: Evolutions in design." Computers & Education 20, no. 2 (1993): 209–10. http://dx.doi.org/10.1016/0360-1315(93)90091-v.

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Talhi, Said, Mahieddine Djoudi ., and Mohamed Batouche . "Authoring Groupware For Intelligent Tutoring Systems." Information Technology Journal 5, no. 5 (2006): 860–67. http://dx.doi.org/10.3923/itj.2006.860.867.

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Gray, Wayne D., Bart Burns, and Lael Schooler. "The Usability of Intelligent Tutoring Systems." Proceedings of the Human Factors Society Annual Meeting 33, no. 19 (1989): 1343–47. http://dx.doi.org/10.1177/154193128903301923.

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Grace, the NYNEX COBOL tutor, is being built in a corporate environment following the philosophy of iterative design and test. Grace and the student interact in a mixed-initiative dialogue. Grace's side of the dialogue is controlled by a simulation based upon the ACT* theory of cognitive skill acquisition (Anderson, 1983, 1987b). This simulation is theory-driven and largely, but not completely, embodied in a production system architecture. The student-tutor dialogue is mediated by an interface whose design is empirically driven and embodied in a multi-media system of windows, text, hypertext,

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Yuce, Ali, A. Mohammed Abubakar, and Mustafa Ilkan. "Intelligent tutoring systems and learning performance." Online Information Review 43, no. 4 (2019): 600–616. http://dx.doi.org/10.1108/oir-11-2017-0340.

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Purpose Intelligent tutoring systems (ITS) are a supplemental educational tool that offers great benefits to students and teachers. The systems are designed to focus on an individual’s characteristics, needs and preferences in an effort to improve student outcomes. Despite the potential benefits of such systems, little work has been done to investigate the impact of ITS on users. To provide a more nuanced understanding of the effectiveness of ITS, the purpose of this paper is to explore the role of several ITS parameters (i.e. knowledge, system, service quality and task–technology fit (TTF)) i

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G, Manju. "Rule-based Cognitive Modelling for Multimodal Intelligent Tutoring Systems." International Journal of Psychosocial Rehabilitation 24, no. 1 (2020): 1754–60. http://dx.doi.org/10.37200/ijpr/v24i1/pr200275.

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Frick, Theodore W. "Artificial Tutoring Systems: What Computers Can and Can't Know." Journal of Educational Computing Research 16, no. 2 (1997): 107–24. http://dx.doi.org/10.2190/4cwm-6jf2-t2dn-qg8l.

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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 communi

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Lee, C. H., J. E. Biegel, and C. M. Dixon. "Student Performance Evaluation for a Simulation Based Intelligent Expert Tutoring System." Proceedings of the Human Factors Society Annual Meeting 32, no. 18 (1988): 1212–16. http://dx.doi.org/10.1177/154193128803201805.

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Intelligent tutoring systems offer an exciting new way to train people in areas of complex domains. A simulation-based training system provides the student with the opportunity to manipulate a system without the consequences of real life mistakes. The intelligence required in the tutoring system is focused on the tutor's ability to teach the student efficient, strategic responses. This tutoring demands that the tutor is aware of the student's current ability, specific fault areas, and preferred method of tutoring. Instructional decisions are made by assessing the student's performance. The uti

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Ksemidov, B. S., and K. K. Abgaryan. "Adaptive Intelligent Tutoring System." Моделирование и анализ данных 14, no. 2 (2024): 152–65. http://dx.doi.org/10.17759/mda.2024140211.

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<p>The goal of the work is to create a modern adaptive intelligent system using current machine learning technologies to automate a significant part of the teacher&rsquo;s work. Existing intelligent systems, the purpose of which is to train students to work in various subject areas, currently have a set of various disadvantages, for example, the need to prepare educational material in a given format, which is sometimes a very labor-intensive task. In addition, in such systems there is a need to assess knowledge to correct the training plan for students, which requires various practic

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Phillips, Fred, and Benny G. Johnson. "Online Homework versus Intelligent Tutoring Systems: Pedagogical Support for Transaction Analysis and Recording." Issues in Accounting Education 26, no. 1 (2011): 87–97. http://dx.doi.org/10.2308/iace.2011.26.1.87.

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ABSTRACT: Prior research demonstrates that students learn more from homework practice when using online homework or intelligent tutoring systems than a paper-and-pencil format. However, no accounting education research directly compares the learning effects of online homework systems with the learning effects of intelligent tutoring systems. This paper presents a quasi-experiment that compares the two systems and finds that students’ transaction analysis performance increased at a significantly faster rate when they used an intelligent tutoring system rather than an online homework system. Imp

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Smith, Philip J., Elliot Soloway, and John Carroll. "Special Session: Intelligent Tutoring and Help Systems." Proceedings of the Human Factors Society Annual Meeting 31, no. 3 (1987): 280. http://dx.doi.org/10.1177/154193128703100301.

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In recent years, considerable effort has been focused on the development of computational models of expert human performance. One class of expertise that has been studied is that of human tutors. The resultant intelligent tutoring systems are intended to provide the user with the “instructional advantage that a sophisticated human tutor can provide,” (Anderson, Boyle and Reiser, 1985). This line of research is of interest to the human factors community for two reasons: 1. Intelligent tutoring systems offer potential tools for use in training and educational programs, a long-standing area of in

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Phobun, Pipatsarun, and Jiracha Vicheanpanya. "Adaptive intelligent tutoring systems for e-learning systems." Procedia - Social and Behavioral Sciences 2, no. 2 (2010): 4064–69. http://dx.doi.org/10.1016/j.sbspro.2010.03.641.

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Ma, Sihan. "Comparison of Different AIED Models and Evaluation Methods." Highlights in Science, Engineering and Technology 72 (December 15, 2023): 401–8. http://dx.doi.org/10.54097/yh47p531.

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In the educational field, although machine which attempts to learn AI is still in their early stages, the approach has yet to show remarkable results when facing complex challenges without obvious cut-off points, such as grading students’ papers or exploring enormous and complicated data collections. AI can also be used to create virtual learning environments, intelligent testing systems, and automated grading systems. AI in educational fields refers to the application of AI technology to enhance and support the studying processes, such as tracking students’ behavior and constructing models th

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Xu, Wei, Ke Zhao, Yatao Li, and Zhenzhen Yi. "FUDAOWANG." International Journal of Distance Education Technologies 10, no. 3 (2012): 67–90. http://dx.doi.org/10.4018/jdet.2012070105.

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Determining how to provide good tutoring functions is an important research direction of intelligent tutoring systems. In this study, the authors develop an intelligent tutoring system with good tutoring functions, called “FUDAOWANG.” The research domain that FUDAOWANG treats is junior middle school mathematics, which belongs to the objective mature domain. Its characteristic is that the knowledge employed is the mature knowledge accepted by most people. FUDAOWANG uses automatic reasoning technology about objective mature problems to realize its intelligence. Based on the results of the automa

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Huang, Mu-Jung. "Intelligent Diagnosing and Learning Agents for Intelligent Tutoring Systems." Journal of Computer Information Systems 40, no. 1 (1999): 45–50. http://dx.doi.org/10.1080/08874417.1999.11647422.

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Anohina, Alla. "Advances in Intelligent Tutoring Systems: Problem-solving Modes and Model of Hints." International Journal of Computers Communications & Control 2, no. 1 (2007): 48. http://dx.doi.org/10.15837/ijccc.2007.1.2336.

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The paper focuses on the issues of providing an adaptive support for learners in intelligent tutoring systems when learners solve practical problems. The results of the analysis of support policies of learners in the existing intelligent tutoring systems are given and the revealed problems are emphasized. The concept and the architectural parts of an intelligent tutoring system are defined. The approach which provides greater adaptive abilities of systems of such kind offering two modes of problem-solving and using a two-layer model of hints is described. It is being implemented in the intelli

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Kaklauskas, Artûras, Ruslanas Ditkevičius, and Leonarda Gargasaite. "INTELLIGENT TUTORING SYSTEM FOR REAL ESTATE MANAGEMENT." International Journal of Strategic Property Management 10, no. 2 (2006): 113–30. http://dx.doi.org/10.3846/1648715x.2006.9637548.

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The review on the worldwide intelligent tutoring systems and their application possibilities is presented in the paper. The intelligent tutoring system for real estate management developed by the authors is described. This system is applied in Vilnius Gediminas Technical University, Department of Construction Economics and Property Management. Besides the common components ‐ student model, domain model, pedagogical model and graphical interface, the new developed system has testing model, decision support subsystem and database of computer learning systems. Domain model includes knowledge with

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Gu, Chloe. "The application of intelligent tutoring systems and social robots in autism treatment." Applied and Computational Engineering 19, no. 1 (2023): 184–89. http://dx.doi.org/10.54254/2755-2721/19/20231030.

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This study examines the utilization of intelligent tutoring systems (ITS) and social robots as innovative technologies to augment the treatment of autism. In the context of artificial intelligence, these interactive systems offer personalized and engaging interventions, holding great promise for individuals on the autism spectrum. While the research in this field is still evolving and lacks maturity, early findings indicate increased engagement and potential for improved learning outcomes among autistic children. Intelligent tutoring systems, capable of adapting to individual learners, exhibit

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