Designing intelligent computer‐based simulations: A pragmatic approach

This paper examines the design of intelligent multimedia simulations. A case study is presented which uses an approach based in part on intelligent tutoring system design to integrate formative assessment into the learning of clinical decision‐making skills for nursing students. The approach advocated uses a modular design with an integrated intelligent agent within a multimedia simulation. The application was created using an object‐orientated programming language for the multimedia interface (Delphi) and a logic‐based interpreted language (Prolog) to create an expert assessment system. Domain knowledge is also encoded in a Windows help file reducing some of the complexity of the expert system. This approach offers a method for simplifying the production of an intelligent simulation system. The problems developing intelligent tutoring systems are examined and an argument is made for a practical approach to developing intelligent multimedia simulation systems

Developing a Multimedia-Based Intelligent Tutoring System

Multimedia systems lack the goal-oriented teaching approach of intelligent tutoring systems. If multimedia is integrated into intelligent tutoring systems then the combination of easy-to-access, highly-structured audiovisual information with goal-oriented intelligent tutoring increases the pedagogical effectiveness of such a system. This paper illustrates how multimedia can be incorporated into an intelligent tutoring system in the implementation of such a system which offers tutoring on zoology

Towards a synthesis of multimedia and intelligent tutoring systems : a dissertation presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University

Multimedia is being used in almost every field. This study is about the use of multimedia in the area of intelligent tutoring systems. This project studies the advantages and disadvantages of interactive multimedia and intelligent tutoring systems, and analyses the ways of combining these technologies in search of an interesting, learnable, flexible, compelling and technology-enhanced educational tool. Educational packages need to be evaluated for effectiveness. When it comes to computer-based instruction, technical concerns such as multimedia effects are taken seriously and there is not enough emphasis on its educational value. There is not much concern about the appropriateness of the instruction method to the computer medium. This research proposes a framework for evaluating educational packages which include a number of issues. Several pieces of educational software were evaluated using this framework and Diagnosis for crop protection, a multimedia software package that aids in teaching the process of diagnosing crop problems, was selected for modification, as a practical application of the theoretical work. We studied different multimedia system development models and methodologies. We also analysed the cognitive issues and intelligent features that enhance the learnability. Finally, the appropriate intelligent features and other factors that could enhance Diagnosis for crop protection to be a more 'active knowledge constructing' environment have been identified. The current version of Diagnosis for crop protection was represented using an appropriate methodology and the proposed changes were described in detail

The implementation of a multimedia learning environment for graduate civil engineers

This paper examines the underpinning theory, design and implementation of a computer‐based Multimedia Learning Environment (MLE) for graduate civil engineers. The MLE brings together multimedia technology and Intelligent Tutoring Systems techniques for the purpose of developing in the graduate engineer the skills and understanding needed to produce initial design proposals for real‐world dam spillway design problems

Editor’s Note

The International Journal of Interactive Multimedia and Artificial Intelligence provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence and Interactive Multimedia techniques. The research works presented in this issue are based on various topics of interest, among which are included: 3D image reconstruction, Persian texts, usability evaluation methods, user experience, oriented matroids, flexible job-shop scheduling, business and social behavior, mobile computing and mobile devices, intelligent tutoring systems and geography optimization

An interactive multimedia learning environment for VLSI built with COSMOS

This paper presents Bigger Bits, an interactive multimedia learning environment that teaches students about VLSI within the context of computer electronics. The system was built with COSMOS (Content Oriented semantic Modelling Overlay Scheme), which is a modelling scheme that we developed for enabling the semantic content of multimedia to be used within interactive systems

Space Communication Artificial Intelligence for Link Evaluation Terminal (SCAILET)

A software application to assist end-users of the high burst rate (HBR) link evaluation terminal (LET) for satellite communications is being developed. The HBR LET system developed at NASA Lewis Research Center is an element of the Advanced Communications Technology Satellite (ACTS) Project. The HBR LET is divided into seven major subsystems, each with its own expert. Programming scripts, test procedures defined by design engineers, set up the HBR LET system. These programming scripts are cryptic, hard to maintain and require a steep learning curve. These scripts were developed by the system engineers who will not be available for the end-users of the system. To increase end-user productivity a friendly interface needs to be added to the system. One possible solution is to provide the user with adequate documentation to perform the needed tasks. With the complexity of this system the vast amount of documentation needed would be overwhelming and the information would be hard to retrieve. With limited resources, maintenance is another reason for not using this form of documentation. An advanced form of interaction is being explored using current computer techniques. This application, which incorporates a combination of multimedia and artificial intelligence (AI) techniques to provided end-users with an intelligent interface to the HBR LET system, is comprised of an intelligent assistant, intelligent tutoring, and hypermedia documentation. The intelligent assistant and tutoring systems address the critical programming needs of the end-user

Adaptive hypermedia for education and training

Adaptive hypermedia (AH) is an alternative to the traditional, one-size-fits-all approach in the development of hypermedia systems. AH systems build a model of the goals, preferences, and knowledge of each individual user; this model is used throughout the interaction with the user to adapt to the needs of that particular user (Brusilovsky, 1996b). For example, a student in an adaptive educational hypermedia system will be given a presentation that is adapted specifically to his or her knowledge of the subject (De Bra & Calvi, 1998; Hothi, Hall, & Sly, 2000) as well as a suggested set of the most relevant links to proceed further (Brusilovsky, Eklund, & Schwarz, 1998; Kavcic, 2004). An adaptive electronic encyclopedia will personalize the content of an article to augment the user's existing knowledge and interests (Bontcheva & Wilks, 2005; Milosavljevic, 1997). A museum guide will adapt the presentation about every visited object to the user's individual path through the museum (Oberlander et al., 1998; Stock et al., 2007). Adaptive hypermedia belongs to the class of user-adaptive systems (Schneider-Hufschmidt, Kühme, & Malinowski, 1993). A distinctive feature of an adaptive system is an explicit user model that represents user knowledge, goals, and interests, as well as other features that enable the system to adapt to different users with their own specific set of goals. An adaptive system collects data for the user model from various sources that can include implicitly observing user interaction and explicitly requesting direct input from the user. The user model is applied to provide an adaptation effect, that is, tailor interaction to different users in the same context. In different kinds of adaptive systems, adaptation effects could vary greatly. In AH systems, it is limited to three major adaptation technologies: adaptive content selection, adaptive navigation support, and adaptive presentation. The first of these three technologies comes from the fields of adaptive information retrieval (IR) and intelligent tutoring systems (ITS). When the user searches for information, the system adaptively selects and prioritizes the most relevant items (Brajnik, Guida, & Tasso, 1987; Brusilovsky, 1992b)