Articles AI MAGAZINEA rtificial intelligence affects growth and productivity in many sectors (for example, transportation, communication, commerce, and finance). However, one painful exception is education; specifically, very few AI-based learning systems are consistently used in classrooms or homes. Yet the potential exists for AI to have a large impact on education: As described by the articles on education in this and the previous AI Magazine issue, AI-based instructional software now routinely tailors learning to individual needs, connects learners together, provides access to digital materials, supports decentralized learning, and engages students in meaningful ways. As a society we have great expectations for the educational establishment (for example, train employees, support scientific and artistic development, transmit culture, and so on) and yet, no matter how much is achieved, society continues to expect even more from education. The current environment of fixed classrooms, lectures, and static printed textbooks is clearly not capable of serving a digital society or flexibly adapting for the future. Classrooms and textbooks are especially inappropriate for people who use mobile and digital technology every day. For example, digital natives learn and work at twitch speed, through parallel processing, and connected to others (Beavis 2010). For digital natives, information is instantly available, change is constant, distance and time do not matter, and multimedia is omnipresent. No wonder schools and classrooms are boring! AI Grand Challenges for Education
As artificial intelligence (AI) systems and behavior models in military simulations become increasingly complex, it has been difficult for users to understand the activities of computer-controlled entities. Prototype explanation systems have been added to simulators, but designers have not heeded the lessons learned from work in explaining expert system behavior. These new explanation systems are not modular and not portable; they are tied to a particular AI system. In this paper, we present a modular and generic architecture for explaining the behavior of simulated entities. We describe its application to the Virtual Humans, a simulation designed to teach soft skills such as negotiation and cultural awareness.
Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Although the representation of physical environments and behaviors will continue to play an important role in simulation-based training, an emerging challenge is the representation of virtual humans with rich mental models (e.g., including emotions, trust) that interact through conversational as well as physical behaviors. The motivation for such simulations is training soft skills such as leadership, cultural awareness, and negotiation, where the majority of actions are conversational, and the problem solving involves consideration of the emotions, attitudes, and desires of others.The educational power of such simulations can be enhanced by the integration of an intelligent tutoring system to support learners' understanding of the effect of their actions on virtual humans and how they might improve their performance. In this paper, we discuss our efforts to build such virtual humans, along with an accompanying intelligent tutor, for the domain of negotiation and cultural awareness.
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