Статті в журналах: "Intelligent tutoring systems"

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Bradáč, Vladimír, and Kateřina Kostolányová. "Intelligent Tutoring Systems." Journal of Intelligent Systems 26, no. 4 (September 26, 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 as a support of presence lessons and, primarily, as the main educational environment for students in the distance form of studies – e-learning. The systems are described from the point of view of their functionalities and typical features that show their differences. The authors conclude with an attempt to choose the best features of each system, which would lead to creation of an even more sophisticated intelligent tutoring system for e-learning.

2

Anderson, J. R., C. F. Boyle, and B. J. Reiser. "Intelligent Tutoring Systems." Science 228, no. 4698 (April 26, 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 (December 1987): 194–203. http://dx.doi.org/10.1111/j.1365-2729.1987.tb00331.x.

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4

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

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5

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

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7

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 (April 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 (March 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 (March 1986): 43–52. http://dx.doi.org/10.1007/bf01988527.

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Petrovica, Sintija. "Tutoring Process in Emotionally Intelligent Tutoring Systems." International Journal of Technology and Educational Marketing 4, no. 1 (January 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 process and tutoring strategies are adapted to students' emotional and knowledge states in these systems. It also presents a study on how to influence student's emotions looking from the pedagogical point of view and provides general guidelines for selection of tutoring strategies to influence and regulate student's emotions.

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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 (March 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 tutoring system can be developed to support multiple tutoring strategies during the course of interaction. SONATA has been implemented using a hypertext tool, HyperCard II. 1.

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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 (July 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 learning and teaching. First, the authors look at ICALL history: its evolution from CALL. Second, issues in ICALL research and integration will be discussed. Third, they will explain how artificial intelligence (AI) techniques are being implemented in language education as ITS and intelligent language tutoring systems (ITLS). Finally, the successful integration and development of ITLS will be explained in detail.

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Kulik, James A., and J. D. Fletcher. "Effectiveness of Intelligent Tutoring Systems." Review of Educational Research 86, no. 1 (March 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 (May 1998): 209–20. http://dx.doi.org/10.1016/s0747-5632(98)00002-8.

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16

Blandford, A. E. "Intelligent tutoring systems: Lessons learned." Computers & Education 14, no. 6 (January 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 (July 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 (February 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 (February 22, 2016): 600–614. http://dx.doi.org/10.1007/s40593-016-0105-0.

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20

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

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21

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 intelligent teaching system, simulate real teaching environments, offer customized teaching services, and really realize students' independent learning and collaborative learning.

22

G, Manju. "Rule-based Cognitive Modelling for Multimodal Intelligent Tutoring Systems." International Journal of Psychosocial Rehabilitation 24, no. 1 (January 20, 2020): 1754–60. http://dx.doi.org/10.37200/ijpr/v24i1/pr200275.

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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 (September 16, 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 agreement (86.2%), with a kappa statistic of 0.728. We also present our current efforts to automatically assess student answers, which involves training machine learning classifiers on features extracted from dependency parses of the reference answer and student's response and features derived from domain-independent lexical statistics. Our system's performance, as high as 75.5% accuracy within domain and 68.8% out of domain, is very encouraging and confirms the approach is feasible. Another significant contribution of this work is that it represents a significant step in the direction of providing domain-independent semantic assessment of answers. No prior work in the area of tutoring or educational assessment has attempted to build such domain-independent systems. They have virtually all required hundreds of examples of learner answers for each new question in order to train aspects of their systems or to hand-craft information extraction templates.

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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 (November 24, 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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Inoue, Yukiko. "Methodological Issues in the Evaluation of Intelligent Tutoring Systems." Journal of Educational Technology Systems 29, no. 3 (March 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 knowledge component that has not been evaluated adequately to verify the knowledge. Definitely summative evaluation is more difficult than formative evaluation since it involves the comparison of ITS with human tutors using traditional teaching methods across extensive subject domains.

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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 (March 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 communication among intelligent natural systems. S. I. Greenspan provides neurological and clinical evidence that emotion and sensation are vital to the growth of mind—capabilities that computer systems do not currently possess. Therefore, we must view computers in education as media through which a multitude of teachers can convey their messages. This does not mean that the role of classroom teachers is diminished. Teachers and students can be empowered by these additional learning resources.

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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 (January 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 (December 2010): 63–65. http://dx.doi.org/10.1145/1869746.1869765.

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30

Elsom-Cook, Mark. "Student modelling in intelligent tutoring systems." Artificial Intelligence Review 7, no. 3-4 (August 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 (May 5, 2015): 413–18. http://dx.doi.org/10.1007/s13218-015-0367-y.

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32

Sharples, Mike. "Intelligent tutoring systems: Evolutions in design." Computers & Education 20, no. 2 (March 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 (August 15, 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 (October 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, mouse gestures, menus, node selections, typing-in, and so. Construction of the simulation and the tutor interface are being tested and revised through a series of user trials. The trials are conducted at one of the sites at which the tutor will be used. Students participating in the trial are from the same population as our target audience.

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Yuce, Ali, A. Mohammed Abubakar, and Mustafa Ilkan. "Intelligent tutoring systems and learning performance." Online Information Review 43, no. 4 (August 12, 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)) in motivating, satisfying and helping students to improve their learning performance. Design/methodology/approach Data were obtained from students who used ITS, and a structural equation modeling was deployed to analyze the data. Findings Data analysis revealed that the quality of knowledge, system and service directly impacted satisfaction and improved TTF for ITS. It was found that TTF and student satisfaction with ITS did not generate higher learning performance. However, student satisfaction with ITS did improve learning motivation and resulted in superior learning performance. Data suggest this is due to students receiving constant and constructive feedback while simultaneously collaborating with their peers and teachers. Originality/value This study verifies that there was a need to assess the benefits of ITS. Based on the study’s findings, theoretical and practical implications are proposed.

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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 (October 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 utility of an intelligent tutoring system depends on its capacity to evaluate the student's performance. Performance assessment then has significant impact on the employment of such a system. The parameters used for performance assessment of a complex task depend on the objective of the tutoring system. We present a description of a generic intelligent tutoring system which will remove the human instructor from the training loop.

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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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38

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 that can accurately hypothesize students’ achievements. It can include the use of AI-powered tutoring systems, personalized learning platforms, and data analysis tools that can help teachers and administrators better understand student needs and progress. This paper mainly concentrates on the field of artificial intelligence tutoring and summarizes the methods by which intelligent tutors assess student performance by comparing some student models and the input, output, and model forms of methods for evaluating fine-grained interactions of intelligent tutors. This article also provides basic information and new perspectives for studying which methods to use to model and evaluate tutorial learning.

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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 (September 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 interest to human factors researchers and practitioners; 2. There are many human factors and human performance issues that should be addressed in the design of such tutoring systems. The speakers in this special session will provide an overview of research issues in the design of intelligent tutoring systems. Relevant conceptual issues and approaches will be highlighted in the context of a variety of application areas. Included will be a discussion of the “use of intelligent system monitors that allow users to integrate the time and effort spent on learning with actual use of a system”, (Carroll and McKendree, 1987).

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Xu, Wei, Ke Zhao, Yatao Li, and Zhenzhen Yi. "FUDAOWANG." International Journal of Distance Education Technologies 10, no. 3 (July 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 automatic reasoning, FUDAOWANG synthetically applies the problem-based tutoring and advanced education concepts to achieve the tutoring functions of stepwise prompt, detailed answers, rethinking after solution, consolidated exercise, etc. The evaluation of FUDAOWANG shows that it is helpful to students in improving learning achievements and cultivating good learning habits.

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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 (February 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. Implications for accounting instructors and researchers are discussed.

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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 (January 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 intelligent tutoring system for the Minimax algorithm at present. In accordance with the proposed approach the learner solves problems in the mode which is the most appropriate for him/her and receives the most suitable hint.

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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 (June 30, 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 the supplemental audio and video material for 63 modules being taught in Vilnius Gediminas Technical University. Student model enables to adapt to a learner needs and knowledge level. Decision support subsystem is used for all components of intelligent tutoring system giving them different level of intelligence. Database of computer learning systems enables using the following web‐based learning systems: construction, real estate, facilities management, international trade, ethics, innovation, sustainable development, building refurbishment, etc. Tutor and testing model provide a model of the teaching process and support transition to a new knowledge state. Graphic interface is used to create an effective system‐user dialogue.

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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 (October 23, 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 promise for delivering personalized and effective interventions. Social robots, with their interactive functionalities, provide a distinct avenue for fostering social communication and skill development. Moreover, these technologies have the potential to deliver cost-effective intervention methods, thus enhancing accessibility for children with autism and their families. However, further research, development, and integration into existing therapeutic approaches are imperative to fully actualize their potential. By harnessing the power of intelligent tutoring systems and social robots, it is possible to pave the way for more effective, personalized, and engaging autism treatment, positively impacting the lives of individuals on the autism spectrum.

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Dermeval, Diego, and Ig Ibert Bittencourt. "Co-designing Gamified Intelligent Tutoring Systems with Teachers." Revista Brasileira de Informática na Educação 28 (February 16, 2020): 73–91. http://dx.doi.org/10.5753/rbie.2020.28.0.73.

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Researchers are increasingly interested in Gamified Intelligent Tutoring Systems (ITSs) to provide adaptive instruction and to enhance engagement of students. However, although teachers are demanding to be active users of gamified ITS, they have been not considered as first-class citizens in the design of these kinds of systems. In order to contribute to the active and customized use of gamified ITS by teachers, three technical problems should be considered. First, designing ITS is very complex (i.e., considering different theories, components, and stakeholders) and including gamification may significantly increase such complexity and variability. Second, gamified ITS features can be used depending on several elements (e.g., educational level, knowledge domain, gamification and ITS theories, etc). Thus, it is imperative to take advantage of theories and practices from both topics to reduce the design space of these systems. Third, in order to effectively aid teachers to actively use such systems, it is needed to provide a simple and usable solution for them. To target these problems, in this paper, we present a solution for authoring gamified ITS by teachers that makes use of an ontology-based feature model (OntoSPL) to deal with the variability at runtime and takes advantage of an ontology (GaTO) that connects gamified ITS theories and design practices to constrain the variability space for designing these systems. Our main results indicate that teachers have a high acceptance level (i.e., ease of use, usability, and low complexity) in the design of gamified ITS using the authoring solution, customizing their own gamified tutors in less than five minutes. These results indicate a promising way to explore the use of authoring tools, ontologies, and software engineering to take advantage of both artificial intelligence techniques (mainly for aiding adaptation for students) as well as on the human intelligence of teachers to co-design gamified intelligent tutoring systems.

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Wang, Jian Hua, Yan Yu, and Jun Jie Guo. "The Study on the Multiple Agent-Based Independent and Collaborative Intelligent Tutoring System Model." Advanced Materials Research 846-847 (November 2013): 1885–88. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1885.

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Over the years, the traditional computer-assisted teaching can not meet the needs of university teaching, the traditional teaching system software, most of the existence of low intelligence, lack of teaching strategies and other shortcomings.Multi-AGENT technology and intelligent tutoring systems is the current research focus in computer intelligence education. Integrating multi-Agent features and multi-Agent application theories in ITS, this paper proposes a multiple Agent-based intelligent network tutoring system design model, detailedly analyzes the functions of each layer in the system, and presents system database category design and system model features.

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Sokolnicki, Tomas. "Towards knowledge-based tutors: a survey and appraisal of Intelligent Tutoring Systems." Knowledge Engineering Review 6, no. 2 (June 1991): 59–95. http://dx.doi.org/10.1017/s0269888900005622.

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AbstractIntelligent tutoring systems can be seen as a next step for computer-based training systems, but also as an important by-product of knowledge-based expert systems. This paper surveys the development and progress in the area, with a special emphasis on the potential for an emerging engineering discipline as opposed to a mere crafting of systems. Major components in intelligent tutoring systems as realized so far are discussed, and key issues for successful future development identified. Knowledge representation, student modelling, planning, natural language issues, explanations and learning are discussed in more depth as being the cornerstones of both tutoring systems and artificial intelligence. Examples from specific implementations are used to illustrate key points. In the concluding discussion we present our attempt at dealing with some of the problems facing the area. In the project Knowledge-Linker, we aim at extending the functionality of a knowledge-based system with tutoring capabilities, and suggest one way of explicitly dealing with teaching strategies.

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Bernacki, Jarosław. "Recommending learning material in Intelligent Tutoring Systems." Annales Universitatis Mariae Curie-Sklodowska, sectio AI – Informatica 16, no. 1 (October 4, 2016): 1. http://dx.doi.org/10.17951/ai.2016.16.1.1.

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<p>Nowadays, intelligent e-learning systems which can adapt to learner's needs and preferences, became very popular. Many studies have demonstrated that such systems can increase the eects of learning. However, providing adaptability requires consideration of many factors. The main problems concern user modeling and personalization, collaborative learning, determining and modifying learning senarios, analyzing learner's learning styles. Determining the optimal learning scenario adapted to students' needs is very important part of an e-learning system. According to psychological research, learning path should follow the students' needs, such as (i.a.): content, level of diculty or presentation version. Optimal learning path can allow for easier and faster gaining of knowledge. In this paper an overview of methods for recommending learning material is presented. Moreover, a method for determining a learning scenario in Intelligent Tutoring Systems is proposed. For this purpose, an Analytic Hierarchy Process (AHP) method is used.</p>

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Rus, Vasile, Sidney D’Mello, Xiangen Hu, and Arthur Graesser. "Recent Advances in Conversational Intelligent Tutoring Systems." AI Magazine 34, no. 3 (September 15, 2013): 42–54. http://dx.doi.org/10.1609/aimag.v34i3.2485.

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We report recent advances in intelligent tutoring systems with conversational dialogue. We highlight progress in terms of macro and microadaptivity. Macroadaptivity refers to a system’s capability to select appropriate instructional tasks for the learner to work on. Microadaptivity refers to a system’s capability to adapt its scaffolding while the learner is working on a particular task. The advances in macro and microadaptivity that are presented here were made possible by the use of learning progressions, deeper dialogue and natural language processing techniques, and by the use of affect-enabled components. Learning progressions and deeper dialogue and natural language processing techniques are key features of DeepTutor, the first intelligent tutoring system based on learning progressions. These improvements extend the bandwidth of possibilities for tailoring instruction to each individual student which is needed for maximizing engagement and ultimately learning.

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Sun, Yu, Zhi Ping Li, and Yao Wen Xia. "Emotional Interaction Agents in Intelligent Tutoring Systems." Applied Mechanics and Materials 347-350 (August 2013): 2682–87. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2682.

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A model of emotional interaction agents in intelligent tutoring systems is presented in this paper, and the functionalities of the key components of the agents are described. To improve the emotional interaction between learners and the system, a kind of emotional interaction agents which can deduce users emotional statues, provide helps needed, and mark emotional difficulty of the learned pedagogical units, is introduced and discussed in detail in order to improve pedagogical effects.

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