The Tactical Language and Culture Training System (TLCTS) helps people quickly acquire communicative skills in foreign languages and cultures.  More than 40,000 learners worldwide have used TLCTS courses.  TLCTS utilizes artificial intelligence technologies during the authoring process, and at run time to process learner speech, engage in dialog, and evaluate and assess learner performance. This paper describes the architecture of TLCTS and the artificial intelligence technologies that it employs, and presents results from multiple evaluation studies that demonstrate the benefits of learning foreign language and culture using this approach.

CSIEC (Computer Simulation in Educational Communication), is not only an intelligent web-based human-computer dialogue system with natural language for English instruction, but also a learning assessment system for learners and teachers. Its multiple functions—including grammar-based gap filling exercises, scenario show, free chatting and chatting on a given topic—can satisfy the various requirements for students with different backgrounds and learning abilities. After a brief explanation of the conception of our dialogue system, as well as a survey of related works, we will illustrate the system structure, and describe its pedagogical functions with the underlying AI techniques in detail such as NLP and rule-based reasoning. We will summarize the free Internet usage within a six month period and its integration into English classes in universities and middle schools. The evaluation findings about the class integration show that the chatting function has been improved and frequently utilized by the users, and the application of the CSIEC system on English instruction can motivate the learners to practice English and enhance their learning process. Finally, we will conclude with potential improvements.

Can we devise educational systems that provide individualized instruction tailored to the needs of the individual learners, as many good teachers do? Intelligent Tutoring Systems is the interdisciplinary field that investigates this question by integrating research in Artificial Intelligence, Cognitive Science and Education. Research in this field has successfully delivered techniques and systems that provide adaptive support for student problem solving in variety of domains. There are, however, other educational activities that can benefit from individualized computer-based support, such as studying examples, exploring interactive simulations and playing educational games. Providing individualized support for these activities rises unique challenges, because it requires that an ITS can model and adapt to student behaviors, skills and mental states often not as structured and well-defined as those involved in traditional problem solving. I will present a variety of projects that illustrate some of these challenges, our proposed solutions, and future opportunities.

I discuss (ontologies_and_ontological_knowledge_bases / formal_methods_and_theories) duality and its category theory extensions as a step toward a solution to Knowledge-Based Systems Theory. In particular I focus on the example of the design of elements of ontologies and ontological knowledge bases of next three electronic courses: Foundations of Research Activities, Virtual Modeling of Complex Systems and Introduction to String Theory.

Using robots as part of any curriculum requires careful management of the significant complexity that physical embodiment introduces. Students need to be made aware of this complexity without being overwhelmed by it, and navigating students through this complexity is the biggest challenge faced by an instructor.  Achieving this requires a framework that allows complexity to be introduced in stages, as students' abilities improve. Such a framework should also be flexible enough to provide a range of application environments that can grow with student  sophistication, and be able to quickly change between applications.  It should be portable and maintainable, and require a minimum of overhead to manage in a classroom. Finally, the framework should provide repeatability and control for evaluating the students' work, as well as for performing research. In this paper, we discuss the advantages of a mixed reality approach to applying robotics to education in order to accomplish these challenges.  We introduce a framework for managing mixed reality in the classroom, and discuss our experiences with using this framework for teaching robotics and AI.

The field of artificial intelligence needs to attract new researchers to the field to continue current explorations and look for novel approaches to tomorrow's problems. One approach involves providing students with learning tools that excite their imagination and help them obtain an appreciation for what artificial intelligence can do. The tools described here are used in an undergraduate course at Sam Houston State University. They include heuristic-driven search in a potential game's terrain map, reinforcement learning in a tank battle game, and game tree search techniques in tic-tac-toe.

Computer Science has a bad reputation among non-CS majors. This paper describes three assignments from a gentle introduction to programming course for non-majors that uses robots and simple game programming as a hook to get students interested in the subject. In each of the assignments presented, what might be considered a trivial twist to an instructor was a key factor in making an otherwise standard project into something that is more engaging.

In this paper, we examine Robot Defense, a computer game that serves as a pedagogical platform for students to explore methods typically covered in an Introductory Artificial Intelligence course. Robot Defense is the synergistic outcome of two NSF funded Course, Curriculum, and Laboratory Improvement (CCLI) projects and was first presented in (Wallace, Russell and Markov 2008). The primary contribution of this paper is to discuss the implementation of the Robot Defense platform and the outcome of its first use in the classroom.

We present a tutorial introduction to the area of preference handling - one of the core issues in the design of any system that automates or supports decision making. The main goal of this tutorial is to provide a framework, or perspective, within which current work on preference handling -representation, reasoning, and elicitation - can be understood. Our intention is not to provide a technical description of the diverse methods used, but rather, to provide a general perspective on the problem and its varied solutions and to highlight central ideas and techniques.

Early work in AI focused on the notion of a goal--an explicit target that must be achieved--and this paradigm is still dominant in AI problem solving. But as application domains become more complex and realistic, it is apparent that the dichotomic notion of a goal, while adequate for certain puzzles, is too crude in general. The problem is that in many contemporary application domains, for example, information retrieval from large databases or the web, or planning in complex domains, the user has little knowledge about the set of possible solutions or feasible items, and what she or he typically seeks is the best that's out there. But since the user does not know what is the best achievable plan or the best available document or product, he or she typically cannot characterize it or its properties specifically. As a result, the user will end up either asking for an unachievable goal, getting no solution in response, or asking for too little, obtaining a solution that can be substantially improved. Of course, the user can gradually adjust the stated goals. This, however, is not a very appealing mode of interaction because the space of alternative solutions in such applications can be combinatorially huge, or even infinite. Moreover, such incremental goal refinement is simply infeasible when the goal must be supplied offline, as in the case of autonomous agents (whether on the web or on Mars).