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.

The ability to recognize affective states of a person... This paper argues that next-generation human-computer interaction (HCI) designs need to include the essence of emotional intelligence -- the ability to recognize a user's affective states -- in order to become more human-like, more effective, and more efficient. Affective arousal modulates all nonverbal communicative cues (facial expressions, body movements, and vocal and physiological reactions). In a face-to-face interaction, humans detect and interpret those interactive signals of their communicator with little or no effort. Yet design and development of an automated system that accomplishes these tasks is rather difficult. This paper surveys the past work in solving these problems by a computer and provides a set of recommendations for developing the first part of an intelligent multimodal HCI -- an automatic personalized analyzer of a user's nonverbal affective feedback.

Following Langer (1992), this article reviews a series of experimental studies that demonstrate that individuals mindlessly apply social rules and expectations to computers. The first set of studies illustrates how individuals overuse human social categories, applying gender stereotypes to computers and ethnically identifying with computer agents. The second set demonstrates that people exhibit overlearned social behaviors such as politeness and reciprocity toward computers. In the third set of studies, premature cognitive commitments are demonstrated: A specialist television set is perceived as providing better content than a generalist television set. A final series of studies demonstrates the depth of social responses with respect to computer “personality. ” Alternative explanations for these findings, such as anthropomorphism and intentional social responses, cannot explain the results. We conclude with an agenda for future research. Computer users approach the personal computer in many different ways. Experienced word processors move smoothly from keyboard to mouse to menu, mixing prose and commands to the computer automatically; the distinction between the hand and the tool blurs (Heidegger, 1977; Winograd & Flores, 1987). Novices cautiously strike each key, fearing that one false move will initiate an uncontrollable series of unwanted events. Game players view computers as *Correspondence concerning this article should be addressed to Clifford Nass, Department of

Building trust with users is crucial in a wide range of applications, such as financial transactions, and some minimal degree of trust is required in all applications to even initiate and maintain an interaction with a user. Humans use a variety of relational conversational strategies, including small talk, to establish trusting relationships with each other. We argue that such strategies can also be used by interface agents, and that embodied conversational agents are ideally suited for this task given the myriad cues available to them for signaling trustworthiness. We describe a model of social dialogue, an implementation in an embodied conversation agent, and an experiment in which social dialogue was demonstrated to have an effect on trust, for users with a disposition to be extroverts.

Use of technology often has unpleasant side effects, which may include strong, negative emotional states that arise during interaction with computers. Frustration, confusion, anger, anxiety and similar emotional states can affect not only the interaction itself, but also productivity, learning, social relationships, and overall well-being. This paper suggests a new solution to this problem: designing human-computer interaction systems to actively support users in their ability to manage and recover from negative emotional states. An interactive affect-support agent was designed and built to test the proposed solution in a situation where users were feeling frustration. The agent's text-only interaction used components of active listening, empathy, and sympathy, in an effort to support users in their ability to recover from frustration. The agent's effectiveness was evaluated against two control conditions, which were also text-based interactions: (1) users' emotions were ignored, and (...

‘‘This evidence leads us to wonder whether intimacy is as much a ‘negotiated collusion ’ as it is a state of ‘true oneness’’’ (Brown and Rogers, 1991) Abstract. Building a collaborative trusting relationship with users is crucial in a wide range of applications, such as advice-giving or financial transactions, and some minimal degree of cooperativeness is required in all applications to even initiate and maintain an interaction with a user. Despite the importance of this aspect of human–human relationships, few intelligent systems have tried to build user models of trust, credibility, or other similar interpersonal variables, or to influence these variables during interaction with users. Humans use a variety of kinds of social language, including small talk, to establish collaborative trusting interpersonal relationships. We argue that such strategies can also be used by intelligent agents, and that embodied conversational agents are ideally suited for this task given the myriad multimodal cues available to them for managing conversation. In this article we describe a model of the relationship between social language and interpersonal relationships, a new kind of discourse planner that is capable of generating social language to achieve interpersonal goals, and an actual implementation in an embodied conversational agent. We discuss an evaluation of our system in which the use of social language was demonstrated to have a significant effect on users ’ perceptions of the agent’s knowledgableness and ability to engage users, and on their trust, credibility, and how well they felt the system knew them, for users manifesting particular personality traits.

Automation is often problematic because people fail to rely upon it appropriately. Because people respond to technology socially, trust influences reliance on automation. In particular, trust guides reliance when complexity and unanticipated situations make a complete understanding of the automation impractical. This review considers trust from the organizational, sociological, interpersonal, psychological, and neurological perspectives. It considers how the context, automation characteristics, and cognitive processes affect the appropriateness of trust. The context in which the automation is used influences automation performance and provides a goal-oriented perspective to assess automation characteristics along a dimension of attributional abstraction. These characteristics can influence trust through analytic, analogical, and affective processes. The challenges of extrapolating the concept of trust in people to trust in automation are discussed. A conceptual model integrates research regarding trust in automation and describes the dynamics of trust, the role of context, and the influence of display characteristics. Actual or potential applications of this research include improved designs of systems that require people to manage imperfect automation.

Abstract. Lifelike animated agents for knowledge-based learning environments can provide timely, customized advice to support learners ’ problem-solving activities. By drawing on a rich repertoire of emotive behaviors to exhibit contextually appropriate facial expressions and emotive gestures, these agents could exploit the visual channel to more effectively communicate with learners. To address these issues, this article proposes the emotive-kinesthetic behavior sequencing framework for dynamically sequencing lifelike pedagogical agents ’ full-body emotive expression. By exploiting a rich behavior space populated with emotive behaviors and structured by pedagogical speech act categories, a behavior sequencing engine operates in realtime to select and assemble contextually appropriate expressive behaviors. This framework has been implemented in a lifelike pedagogical agent, COSMO, who exhibits full-body emotive behaviors in response to learners ' problem-solving activities.

how to have a conversation with living, breathing human users with a wink, a nod, and a sidelong glance. Animals and humans all manifest social qualities and skills. Dogs recognize dominance and submission, stand corrected by their superiors, demonstrate consistent personalities, and so forth. On the other hand, only humans communicate through language and carry on conversations with one another. The skills involved in human conversation have developed in such a way as to exploit all the special characteristics of the human body. We make complex representational

In this study, we investigated people's relationships with AIBO, a robotic pet, through 6,438 spontaneous postings in online AIBO discussion forums. Results showed that AIBO psychologically engaged this group of participants, particularly by drawing forth conceptions of technological essences (75%), life-like essences (49%), mental states (60%), and social rapport (59%). However, participants seldom attributed moral standing to AIBO (e.g., that AIBO deserves respect, has rights, or can be held morally accountable for action). Our discussion focuses on how robotic pets (now and in the future) may (a) challenge traditional boundaries (e.g. between who or what can possess feelings), (b) extend our conceptions of self, companionship, and community, and (c) begin to replace interactions with live pets. We also discuss a concern that people in general, and children in particular, may fall prey to accepting robotic pets without the moral responsibilities (and moral developmental outcomes) that real, reciprocal companionship and cooperation involves. This research contributes to a growing literature on the human-robotic relationship.