Recent years have witnessed the birth of a new paradigm for learning environments: animated pedagogical agents. These lifelike autonomous characters cohabit learning environments with students to create rich, face-to-face learning interactions. This opens up exciting new possibilities; for example, agents can demonstrate complex tasks, employ locomotion and gesture to focus students'attention on the most salient aspect of the task at hand, and convey emotional responses to the tutorial situation. Animated pedagogical agents offer great promise for broadening the bandwidth of tutorial communication and increasing learning environments' ability to engage and motivate students. This article sets forth the motivations behind animated pedagogical agents, describes the key capabilities they offer, and discusses the technical issues they raise. The discussion is illustrated with descriptions of a number of animated agents that represent the current state of the art.

This research investigates the meaning of ‘human-computer relationship’ and presents techniques for constructing, maintaining, and evaluating such relationships, based on research in social psychology, sociolinguistics, communication and other social sciences. Contexts in which relationships are particularly important are described, together with specific benefits (like trust) and task outcomes (like improved learning) known to be associated with relationship quality. We especially consider the problem of designing for longterm interaction, and define relational agents as computational artifacts designed to establish and maintain long-term social-emotional relationships with their users. We construct the first such agent, and evaluate it in a controlled experiment with 101 users who were asked to interact daily with an exercise adoption system for a month. Compared to an equivalent task-oriented agent without any deliberate social-emotional or relationshipbuilding skills, the relational agent was respected more, liked more, and trusted more, even after four weeks of interaction. Additionally, users expressed a significantly greater desire to continue working with the relational agent after the termination of the study. We conclude by discussing future directions for this research together with ethical and other ramifications of this work for HCI designers.

This paper suggests a framework for understanding the roles that children can play in the technology design process, particularly in regards to designing technologies that support learning. Each role, user, tester, informant, and design partner has been defined based upon a review of the literature and my lab’s own research experiences. This discussion does not suggest that any one role is appropriate for all research or development needs. Instead, by understanding this framework the reader may be able to make more informed decisions about the design processes they choose to use with children in creating new technologies. This paper will present for each role a historical overview, research and development methods, as well as the strengths, challenges, and unique contributions associated with children in the design process.

how to design the roots, stem, and leaves of plants to survive in 8 different environ-ments through a computer-based multimedia lesson. They learned by interacting with an animated pedagogical agent who spoke to them (Group PA) or received identical graphics and explanations as on-screen text without a pedagogical agent (Group No PA). Group PA outperformed Group No PA on transfer tests and interest ratings but not on retention tests. To investigate further the basis for this personal agent effect, we varied the interactivity of the agent-based lesson (Experiment 3) and found an

What kinds of social relationships can people have with computers? Are there activities that computers can engage in that actively draw people into relationships with them? What are the potential benefits to the people who participate in these human-computer relationships? To address these questions this work introduces a theory of Relational Agents, which are computational artifacts designed to build and maintain long-term, social-emotional relationships with their users. These can be purely software humanoid animated agents--as developed in this work--but they can also be non-humanoid or embodied in various physical forms, from robots, to pets, to jewelry, clothing, hand-helds, and other interactive devices. Central to the notion of relationship is that it is a persistent construct, spanning multiple interactions; thus, Relational Agents are explicitly designed to remember past history and manage future expectations in their interactions with users. Finally, relationships are fundamentally social and emotional, and detailed knowledge of human social psychology--with a particular emphasis on the role of affect--must be incorporated into these agents if they are to effectively leverage the mechanisms of human social cognition in order to build relationships in the most natural manner possible. People build

Given the importance of credibility in computing products, the research on computer credibility is relatively small. To enhance knowledge about computers and credibility, we define key terms relating to computer credibility, synthesize the literature in this domain, and propose three new conceptual frameworks for better understanding the elements of computer credibility. To promote further research, we then offer two perspectives on what computer users evaluate when assessing credibility. We conclude by presenting a stt of credibility-related terms that can serve in future research and evaluation endeavors.

this paper, we investigate a new style for presenting information. We introduce the notion of presentation teams which---rather than addressing the user directly---convey information in the style of performances to be observed by the user. The paper is organized as follows. First, we report on our experience with two single animated presentation agents and explain how to evaluate their success. After that, we move to presentation teams and discuss their potential benefits for presentation tasks. In section 2, we describe the basic steps of our approach to the automated generation of performances with multiple characters. This approach has been applied to two different in: J. Cassell, S. Prevost, J. Sullivan, and E. Churchill: Embodied Conversational

Predicting when a person might be frustrated can provide an intelligent system with important information about when to initiate interaction. For example, an automated Learning Companion or Intelligent Tutoring System might use this information to intervene, providing support to the learner who is likely to otherwise quit, while leaving engaged learners free to discover things without interruption. This paper presents the first automated method that assesses, using multiple channels of affect-related information, whether a learner is about to click on a button saying ‘‘I’m frustrated.’’ The new method was tested on data gathered from 24 participants using an automated Learning Companion. Their indication of frustration was automatically predicted from the collected data with 79% accuracy (chance 58%). The new assessment method is based on Gaussian process classification and Bayesian inference. Its performance suggests that non-verbal channels carrying affective cues can help provide important information to a system for formulating a more intelligent response.

Abstract. Mixed-initiative problem solving lies at the heart of knowledge-based learning environments. While learners are actively engaged in problem-solving activities, learning environments should monitor their progress and provide them with feedback in a manner that contributes to achieving the twin goals of learning effectiveness and learning efficiency. Mixed-initiative interactions are particularly critical for constructivist learning environments in which learners participate in active problem solving. We have recently begun to see the emergence of believable agents with lifelike qualities. Featured prominently in constructivist learning environments, lifelike pedagogical agents could couple key feedback functionalities with a strong visual presence by observing learners ’ progress and providing them with visually contextualized advice during mixed-initiative problem solving. For the past three years, we have been engaged in a large-scale research program on lifelike pedagogical agents and their role in constructivist learning environments. In the resulting computational framework, lifelike pedagogical agents are specified by (1) a behavior space containing animated and vocal behaviors, (2) a design-centered context model that maintains constructivist problem representations, multimodal advisory contexts, and evolving problem-solving tasks, and (3) a behavior sequencing engine that in realtime dynamically selects and assembles agents ’ actions to create pedagogically effective, lifelike behaviors. To empirically investigate this framework, it has been instantiated in a full-scale implementation of a lifelike pedagogical agent for DESIGN-A-PLANT, a learning environment developed for the domain of botanical anatomy and physiology for middle school students. Experience with focus group studies conducted with middle school students interacting with the implemented agent suggests that lifelike pedagogical agents hold much promise for mixed-initiative learning. Key words: Lifelike agents, pedagogicalagents, animated agents, knowledge-basedlearning environments, mixed-initiative interaction, intelligent tutoring systems, intelligent multimedia presentation,

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