For those who haven't heard of it, EAAI is a symposium that is held in conjunction with AAAI. The symposium provides a venue for researchers and educators to discuss pedagogical issues and share resources related to AI and education. This year, the symposium featured a range of activities, including two invited talks, paper presentations, poster presentations, panels, and workshops. Several main themes of discussion at the symposium included the introduction of AI concepts in early courses, active learning, and massive open online courses (MOOCs) and flipped classrooms. With the emergence of "big data" as a buzzword in the mainstream media, new students are often interested in learning about this area but may not have the math or computing skills to support their interests.

Past Research in Expert Systems at ETSU Artificial intelligence research at East Texas State University (ETSU) began in the fall of 1983 with the development of a knowledge-based expert system to solve configuration problems. The intention was to develop a generic system that could be transferred from one problem domain to another. The problem domains selected on which the system was to be tested were the configuration of Hewlett-Packard Model 29 computer systems and the generation of degree plans for graduate students in the Computer Science Department at ETSU. The configurator is based on a semantic network that utilizes frames as a method of representing knowledge. Frames are used as nodes in the network and can contain facts, rules, and links to other nodes.

It included an invited talk, paper presentations, model AI assignments, a teaching and mentoring workshop, a best educational video award, and a robotics track. The symposium was established in response to growing community interest in sharing best practices for (1) how to teach AI and (2) how AI can serve as a motivating problem for teaching concepts in other areas of computer science, especially in introductory computer science courses. EAAI encourages the sharing of innovative educational approaches that convey or leverage AI and its many subfields, including robotics, machine learning, natural language, and computer vision. EAAI follows the successful 2008 Spring Symposium on "Using AI to Motivate Greater Participation in Computer Science" and the 2008 AAAI AI Education Colloquium. Fifty-five attendees formally registered for the event, but many other AAAI attendees also visited one or more EAAI events.

Articulating thought in computerbased media is a powerful means for humans to develop their understanding of phenomena. We have created DynaLearn, an intelligent learning environment that allows learners to acquire conceptual knowledge by constructing and simulating qualitative models of how systems behave. DynaLearn uses diagrammatic representations for learners to express their ideas. The environment is equipped with semantic technology components that are capable of generating knowledge-based feedback and virtual characters that enhance the interaction with learners. Teachers have created course material, and successful evaluation studies have been performed.

Don was one of the pioneers of our field, whose early research built the foundation for the area that would later come to be labeled "knowledge based systems" (and still later "expert systems"). Don received a B.S. in Electrical Engineering from Iowa State University in 1958, and an M.S. in Electrical Engineering from the University of California, Berkeley in 1964. He then entered the Ph.D. program at Stanford's newly created Cotiputer Science Department. While at Berkeley he met a young professor named Ed Feigenbaum, and when Feigenbaum moved to Stanford in 1965 Don became Ed's first Ph.D. student. Ed recalls: "In mid-1965 the DENDRAL project began in earnest, and Don was its first (and at the time its only) Ph.D. student.

The Dialogue on Dialogues workshop was organized as a satellite event at the Interspeech 2006 conference in Pittsburgh, Pennsylvania, and it was held on September 17, 2006, immediately before the main conference. It was planned and coordinated by Michael McTear (University of Ulster, UK), Kristiina Jokinen (University of Helsinki, Finland), and James A. Larson (Portland State University, USA). The one-day workshop involved more than 40 participants from Europe, the United States, Australia, and Japan. One of the motivations for furthering the systems' interaction capabilities is to improve the AI Magazine Volume 28 Number 2 (2007) ( AAAI) However, relatively little work has so far been devoted to defining the criteria according to which we could evaluate such systems in terms of increased naturalness and usability. It is often felt that statistical speech-based research is not fully appreciated in the dialogue community, while dialogue modeling in the speech community seems too simple in terms of the advanced architectures and functionalities under investigation in the dialogue community.

This article summarizes a workshop entitled "Detecting, Repairing, and Preventing Human-Machine Miscommunication," held on 4 August 1996 in Portland, Oregon. The author presents the significant issues raised during the four specific workshop sessions. Research related to achieving robust interaction is an important subarea in AI. Early work concerned the correction of spelling or grammatical errors in a user's utterance so that the system could more easily match them against a fixed linguistic model; work has also been done in the area of speech recognition, attempting to find the best fit of a sound signal to legal sequences of linguistic objects. All these approaches have assumed that the system's model is always correct.

Of all computational systems, robots are unique in their ability to afford embodied interaction using the wider range of human communicative cues. Research on human communication provides strong evidence that embodied cues, when used effectively, elicit social, cognitive, and task outcomes such as improved learning, rapport, motivation, persuasion, and collaborative task performance. While this connection between embodied cues and key outcomes provides a unique opportunity for design, taking advantage of it requires a deeper understanding of how robots might use these cues effectively and the limitations in the extent to which they might achieve such outcomes through embodied interaction. This article aims to underline this opportunity by providing an overview of key embodied cues and outcomes in human communication and describing a research program that explores how robots might generate high-level social, cognitive, and task outcomes such as learning, rapport, and persuasion using embodied cues such as verbal, vocal, and nonverbal cues. Such representations vary from physical artifacts (such as tangible interfaces) to biological forms (such as humanlike agents and robots) and offer templates for understanding and interacting with complex computational systems (Ullmer and Ishii 2000, Cassell 2001, Breazeal 2003).

On October 8-10, 2009, an interdisciplinary group met in Beverley, Massachusetts, to evaluate the state of the art in the computational modeling of narrative. Three important findings emerged: (1) current work in computational modeling is described by three different levels of representation; (2) there is a paucity of studies at the highest, most abstract level aimed at inferring the meaning or message of the narrative; and (3) there is a need to establish a standard data bank of annotated narratives, analogous to the Penn Treebank. We use them to entertain, communicate, convince, and explain. One workshop participant noted that "as far as I know, every society in the world has stories, which suggests they have a psychological basis, that stories do something for you." To truly understand and explain human intelligence, reasoning, and beliefs, we need to understand why narrative is universal and explain the function it serves. Computational modeling is a natural method for investigating narrative. As a complex cognitive phenomenon, narrative touches on many areas that have traditionally been of interest to artificial intelligence researchers: its different facets draw on our capacities for natural language understanding and generation, commonsense reasoning, analogical reasoning, planning, physical perception (through imagination), and social cognition. Successful modeling will undoubtedly require researchers from these many perspectives and more, using a multitude of different techniques from the AI toolkit, ranging from, for example, detailed symbolic knowledge representation to largescale statistical analyses. The relevance of AI to narrative, and vice versa, is compelling.

This editorial introduction presents an overview of the robotic resources available to AI educators and provides context for the articles in this special issue. We set the stage by addressing the tradeoffs among a number of established and emerging hardware and software platforms, curricular topics, and robot contests used to motivate and teach undergraduate AI. Yet it is only recently that physically embodied agents have become a viable tool in the undergraduate AI classroom. Examples of the flurry of activity in this area include competitions and exhibitions, the growing options for lowcost robot hardware and software, and a number of recent workshops and symposia. This special issue of AI Magazine grew out of the 2004 AAAI spring symposium on Accessible, Hands-on AI and Robotics Education.