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This article presents systems that use speech technology, to emulate the one-on-one interaction a student can get from a virtual instructor. A web-based learning tool, the Learn IN Context (LINC+) system, designed and used in a real mixed-mode learning context for a computer (C++ language) programming course taught at the Université de Moncton (Canada) is described here. It integrates an Internet Voice Searching and Navigating (IVSN) system that helps learners to search and navigate both the web and their desktop environment through voice commands and dictation. LINC+ also incorporates an Automatic User Profile Building and Training (AUPB&T) module that allows users to increase speech recognition performance without having to go through the long and fastidious manual training process. The findings show that the majority of learners seem to be satisfied with this new media, and confirm that it does not negatively affect their cognitive load.

Web-based learning is rapidly becoming the preferred way to quickly, efficiently, and economically create and deliver training or educational content through various communication media. This chapter presents systems that use speech technology to emulate the one-on-one interaction a student can get from a virtual instructor. A Web-based learning tool, the Learn IN Context (LINC+) system, designed and used in a real mixed-mode learning context for a computer (C++ language) programming course taught at the Université de Moncton (Canada) is described here. It integrates an Internet Voice Searching and Navigating (IVSN) system that helps learners to search and navigate both the web and their desktop environment through voice commands and dictation. LINC+ also incorporates an Automatic User Profile Building and Training (AUPB&T) module that allows users to increase speech recognition performance without having to go through the long and fastidious manual training process. New Automated Service Agents based on the Artificial Intelligence Markup Language (AIML) are used to provide naturalness to the dialogs between users and machines. The portability of the e-learning system across a mobile platform is also investigated. The findings show that when the learning material is delivered in the form of a collaborative and voice-enabled presentation, the majority of learners seem to be satisfied with this new media, and confirm that it does not negatively affect their cognitive load.

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This paper presents improvements in a dialogue interpreter sub-system for an application that allows the user to interact by speech with a Radio-Frequency IDentification (RFID) network working in a highly noisy environment. A new dialog framework is proposed in order to give the human operators the ability to communicate with the system in a more natural fashion. This is achieved by the implementation of the Artificial Intelligence Markup Language combined with an ontology-based pattern generation and root semantical analysis algorithms, which allows the system to respond to close-to natural language queries by means of pattern matching.