O impacto social dos agentes pedagógicos animados em ambientes interactivos de aprendizagem

O estudo de caso, aqui apresentado de forma sucinta, foi realizado com a finalidade de se analisar a interacção entre os alunos de uma turma de sexto ano, do Ensino Básico, e os agentes virtuais presentes no software educativo TeLL me More® Kids. Os dados obtidos a partir de diferentes técnicas e instrumentos permitiram tentar compreender a influência dessas personagens, não só no desenvolvimento de apetências e de competências transversais e específicas da área disciplinar de Inglês, mas também noutros domínios tais como a motivação, a emoção, as relações aluno-professor e aluno-aluno, a autonomia, as atitudes e valores e a criatividade. As conclusões resultantes do estudo, implementado no ano lectivo de 2004/2005, convergem com as que têm vindo a ser apresentadas pelos investigadores da área em questão, ou seja, que os agentes pedagógicos animados actuam essencialmente ao nível da emoção e da motivação, o que pode ajudar no desenvolvimento e/ou construção de conhecimento. Por este motivo, julga-se fundamental uma avaliação sistemática dos agentes virtuais, de modo a tentar evitar qualquer impacto social menos positivo, principalmente nas crianças

Exploring Non-verbal Behavior Models for Believable Characters.

Believable characters constitute an important component of interactive stories. It is, therefore, not surprising to see much research focusing on developing algorithms that enhance character believability within interactive experiences, such as games, interactive narrative, and training environments. These efforts target a variety of problems, including portraying and synchronizing gestures with speech, developing animation tools that allow artists to manipulate and blend motions, or embed emotions within virtual character models. There has been very little research, however, devoted to the study of non-verbal behaviors, specifically mannerisms, patterns of movement including postures, gaze, and timing, and how they vary as a function of character attributes. This paper presents a work in progress of a study conducted to (1) identify key character characteristics recognized by animators using an acting model, and (2) formalize non-verbal behaviors patterns that animators use to express these character characteristics

Generating socially appropriate tutorial dialog

Analysis of student-tutor coaching dialogs suggest that good human tutors attend to and attempt to influence the motivational state of learners. Moreover, they are sensitive to the social face of the learner, and seek to mitigate the potential face threat of their comments. This paper describes a dialog generator for pedagogical agents that takes motivation and face threat factors into account. This enables the agent to interact with learners in a socially appropriate fashion, and foster intrinsic motivation on the part of the learner, which in turn may lead to more positive learner affective states

Presenting Arguments as Fictive Dialogue

Presentation of an argument can take many different forms ranging from a monologue to advanced graphics and diagrams. This paper investigates the presentation of one or more arguments in the form of a fictive dialogue. This technique was already employed by Plato, who used fictive conversations between Socrates and his contemporaries to put his arguments forward. Ever since, there have been influential authors – including Desiderius Erasmus, Sir Thomas More and Mark Twain – that have used dialogue in this way. In this paper, we define the notion of a fictive dialogue, motivate it is as a topic for investigation, and present a qualitative and quantitative study of five fictive dialogues by well-known authors. We conclude by indicating how our preliminary and ongoing investigations may inform the development of systems that automatically generate argumentative fictive dialogue

Museum Experience Design: A Modern Storytelling Methodology

In this paper we propose a new direction for design, in the context of the theme “Next Digital Technologies in Arts and Culture”, by employing modern methods based on Interaction Design, Interactive Storytelling and Artificial Intelligence. Focusing on Cultural Heritage, we propose a new paradigm for Museum Experience Design, facilitating on the one hand traditional visual and multimedia communication and, on the other, a new type of interaction with artefacts, in the form of a Storytelling Experience. Museums are increasingly being transformed into hybrid spaces, where virtual (digital) information coexists with tangible artefacts. In this context, “Next Digital Technologies” play a new role, providing methods to increase cultural accessibility and enhance experience. Not only is the goal to convey stories hidden inside artefacts, as well as items or objects connected to them, but it is also to pave the way for the creation of new ones through an interactive museum experience that continues after the museum visit ends. Social sharing, in particular, can greatly increase the value of dissemination

Simulating emotional reactions in medical dramas

Presenting information on emotionally charged topics is a delicate task: if bare facts alone are conveyed, there is a risk of boring the audience, or coming across as cold and unfeeling; on the other hand, emotional presentation can be appropriate when carefully handled, but when overdone or mishandled risks being perceived as patronising or in poor taste. When Natural Language Generation (NLG) systems present emotionally charged information linguistically, by generating scripts for embodied agents, emotional/affective aspects cannot be ignored. It is important to ensure that viewers consider the presentation appropriate and sympathetic. We are investigating the role of affect in communicating medical information in the context of an NLG system that generates short medical dramas enacted by embodied agents. The dramas have both an informational and an educational purpose in that they help patients review their medical histories whilst receiving explanations of less familiar medical terms and demonstrations of their usage. The dramas are also personalised since they are generated from the patients' own medical records. We view generation of natural/appropriate emotional language as a way to engage and maintain the viewers' attention. For our medical setting, we hypothesize that viewers will consider dialogues more natural when they have an enthusiastic and sympathetic emotional tone. Our second hypothesis proposes that such dialogues are also better for engaging the viewers' attention. As well as describing our NLG system for generating natural emotional language in medical dialogue, we present a pilot study with which we investigate our two hypotheses. Our results were not quite as unequivocal as we had hoped. Firstly, our participants did notice whether a character sympathised with the patient and was enthusiastic. This did not, however, lead them to judge such a character as behaving more naturally or the dialogue as being more engaging. However, when pooling data from our two conditions, dialogues with versus dialogues without emotionally appropriate language use, we discovered, somewhat surprisingly, that participants did consider a dialogue more engaging if they believed that the characters showed sympathy towards the patient, were not cold and unfeeling, and were natural (true for the female agent only)

Speech-driven Animation with Meaningful Behaviors

Conversational agents (CAs) play an important role in human computer interaction. Creating believable movements for CAs is challenging, since the movements have to be meaningful and natural, reflecting the coupling between gestures and speech. Studies in the past have mainly relied on rule-based or data-driven approaches. Rule-based methods focus on creating meaningful behaviors conveying the underlying message, but the gestures cannot be easily synchronized with speech. Data-driven approaches, especially speech-driven models, can capture the relationship between speech and gestures. However, they create behaviors disregarding the meaning of the message. This study proposes to bridge the gap between these two approaches overcoming their limitations. The approach builds a dynamic Bayesian network (DBN), where a discrete variable is added to constrain the behaviors on the underlying constraint. The study implements and evaluates the approach with two constraints: discourse functions and prototypical behaviors. By constraining on the discourse functions (e.g., questions), the model learns the characteristic behaviors associated with a given discourse class learning the rules from the data. By constraining on prototypical behaviors (e.g., head nods), the approach can be embedded in a rule-based system as a behavior realizer creating trajectories that are timely synchronized with speech. The study proposes a DBN structure and a training approach that (1) models the cause-effect relationship between the constraint and the gestures, (2) initializes the state configuration models increasing the range of the generated behaviors, and (3) captures the differences in the behaviors across constraints by enforcing sparse transitions between shared and exclusive states per constraint. Objective and subjective evaluations demonstrate the benefits of the proposed approach over an unconstrained model.Comment: 13 pages, 12 figures, 5 table