6,302 research outputs found

    Intelligent tutoring systems for systems engineering methodologies

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    The general goal is to provide the technology required to build systems that can provide intelligent tutoring in IDEF (Integrated Computer Aided Manufacturing Definition Method) modeling. The following subject areas are covered: intelligent tutoring systems for systems analysis methodologies; IDEF tutor architecture and components; developing cognitive skills for IDEF modeling; experimental software; and PC based prototype

    Development of a personal-computer-based intelligent tutoring system

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    A large number of Intelligent Tutoring Systems (ITSs) have been built since they were first proposed in the early 1970's. Research conducted on the use of the best of these systems has demonstrated their effectiveness in tutoring in selected domains. A prototype ITS for tutoring students in the use of CLIPS language: CLIPSIT (CLIPS Intelligent Tutor) was developed. For an ITS to be widely accepted, not only must it be effective, flexible, and very responsive, it must also be capable of functioning on readily available computers. While most ITSs have been developed on powerful workstations, CLIPSIT is designed for use on the IBM PC/XT/AT personal computer family (and their clones). There are many issues to consider when developing an ITS on a personal computer such as the teaching strategy, user interface, knowledge representation, and program design methodology. Based on experiences in developing CLIPSIT, results on how to address some of these issues are reported and approaches are suggested for maintaining a powerful learning environment while delivering robust performance within the speed and memory constraints of the personal computer

    Knowledge Engineering for Intelligent Tutoring

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    Building a mapping between items and their related knowledge components, while difficult and time consuming, is central to the task of developing intelligent tutoring systems (ITS). Improving performance on this task by creating a semi-automatic skill encoder would facilitate ITS development. The goal of this project is to explore text classification techniques to reduce the time required to correctly tag items. This work has received favorable peer-review and was accepted for publication at the 8th Annual Intelligent Tutoring System Conference

    Artificial intelligent based teaching and learning approaches: A comprehensive review

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    The goal of this study is to investigate the potential effects that Artificial intelligence (AI) could have on education. The narrative and framework for investigating AI that emerged from the preliminary research served as the basis for the study’s emphasis, which was narrowed down to the use of AI and its effects on administration, instruction, and student learning. According to the findings, artificial intelligence has seen widespread adoption and use in education, particularly by educational institutions and in various contexts and applications. The development of AI began with computers and technologies related to computers; it then progressed to web-based and online intelligent education systems; and finally, it applied embedded computer systems in conjunction with other technologies, humanoid robots, and web-based chatbots to execute instructor tasks and functions either independently or in partnership with instructors. By utilizing these platforms, educators have been able to accomplish a variety of administrative tasks. In addition, because the systems rely on machine learning and flexibility, the curriculum and content have been modified to match the needs of students. This has led to improved learning outcomes in the form of higher uptake and retention rates

    A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms

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    Abstract The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.</jats:p
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