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A Day in the Life of Airman Basic Smith
Published in C.A.P. Smith, Kenneth W. Kisiel, Jeffrey G. Morrison, Working Through Synthetic Worlds, 2009
Peter Garretson, Nathan T. Denny
Intelligent tutoring systems GBL, as envisioned in our vignette, is the product of the fusion of many different technologies, including intelligent tutoring systems and automatic scenario construction. Intelligent tutoring systems (ITS) are knowledge-based systems that attempt to mimic some of the practices of real tutors in facilitating the process of learning. Many ITS products are integrated with the pedagogical content and interact with the user through standard point-and-click interfaces. As human tutors are particularly effective and they are definitely not point-and-click objects, there is considerable interest in making interaction with ITSs more natural. As conversation is a common mode of interaction between real tutors and their students, it follows that a conversational dialog between the student and the ITS would be an alternative to point-and-click interfaces. Conversational interfaces, with pedagogical agents which manifest themselves with humanoid avatars not only deepen the immersion experienced by the student but also provide a mechanism that can encourage the student to think deeper about the material being learned. Rather then engaging the function of recall when selecting a multiple choice answer, a free form conversational dialog requires the student to perform both recall and synthesis.
AI-Based Predictive Models for Adaptive Learning Systems
Published in Prathamesh Churi, Shubham Joshi, Mohamed Elhoseny, Amina Omrane, Artificial Intelligence in Higher Education, 2023
Prashant Gupta, Trishul Kulkarni, Bhagwan Toksha
An intelligent tutoring system (ITS) is a computer programme that gives tailored instruction on an immediate basis without direct intervention from a human instructor (Steenbergen-Hu & Cooper, 2014). These are computer learning environments capable of helping learners to master skills and knowledge by employing intelligent algorithms that are adaptable at a finer level and represent complex principles of learning by instance. The work done towards development in ITS over the last two decades is focused on two basic pedagogical problems, the first of which is to arrange sophisticated instructional guidance on an individual basis, which is better in terms of results achieved with traditional computer-based instructional systems. The second issue is to proceed with current models on the intellective processes involved with teaching-learning. The research in ITS has leveraged advancements in AI, thereby pushing boundaries of its capabilities with grounded usage scenarios (Kokku et al., 2018). Intelligent Tutoring Systems’ (ITSs’) cognitive capabilities have been derived from the use of AI techniques. These techniques were put into work with interacting components (Sedlmeier, 2001) such as: The knowledge base, which indicates the centric part of an instructional process;The learner model, indicating the existing state of learner’s knowledge;The pedagogical module, indicative of the most suitable instructional approaches that depended upon the learner model assessment; andThe user interface, enabling an effective conversation in between the transacting parties—the ITS and a learner. These components were thoroughly assessed in terms of their granularity, interoperability, reliability and generality, cumulatively indicative of its functionality. ITSs thus can be classified into a) course content curation and delivery ITSs, and b) learner performance monitoring and automated diagnostic feedback offering ITSs. However, issues such as the role of theoretical frameworks in the design, implementation, and validation of an ITS still remain unaddressed (Curilem et al., 2007; Guan et al., 2020; Hwang, 2003).
A Multimodal Human-Computer Interaction for Smart Learning System
Published in International Journal of Human–Computer Interaction, 2023
Tareq Mahmod Alzubi, Jafar A. Alzubi, Ashish Singh, Omar A. Alzubi, Murali Subramanian
Deep learning (LeCun et al., 2015) is a new advanced technology that achieved relatively successful development in image recognition and retrieval, language information processing, and speech recognition (Le et al., 2021). The model automatically creates personalized learning experiences for students based on their individual needs and preferences. It also creates more engaging and interactive educational experiences, which improve student learning performance. Thus, the work moved towards multimodal HCI (Raptis et al., 2021). The proposed multimodal HCI consider an educational client-server environment in which multimodal input and output combine different modes of input and output, such as touch, voice, and gestures, to create a more natural and intuitive way for students to interact with educational material. The main feature of the proposed model includes multimodality, which improves the accuracy and reliability of the multimodal HCI system and the output information. It also enhances the teaching communication methodology with multiple animated outputs. The proposed model uses deep learning multi-layer CNN algorithm is used to develop a smart learning education system. It analyzes and interprets data from various input modalities and extracts important features from educational content such as videos, images, and text, which can then be used to develop intelligent tutoring systems.
Adaptive driving simulation-based training: framework, status, and needs
Published in Theoretical Issues in Ergonomics Science, 2020
Maryam Zahabi, Junho Park, Ashiq Mohammed Abdul Razak, Anthony D. McDonald
The intelligent tutoring system (ITS), a major subcategory of adaptive training (VanLehn 2011), is a computer system that aims to provide immediate and customized instruction or feedback . The ITS is a well-established framework that has been applied in many domains. However, there are three critical differences between the ITS and our proposed ADST framework which are described in details below and are summarized in Table 1.