Improving social presence in human-agent interaction

Pereira, Ana Isabel; Prada, Rui; Paiva, Ana

doi:10.1145/2556288.2557180

Cited by 41 publications

(38 citation statements)

References 29 publications

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“…Research on design and evaluation of robots that interact with groups of users has become very prominent in the past few years in several application domains such as education (Kanda et al, 2007;Al Moubayed et al, 2012;Foster et al, 2012;Gomez et al, 2012;Johansson et al, 2013;Bohus et al, 2014;Pereira et al, 2014). Despite this trend, few authors investigated differences between one single user and a group of users interacting with a robot in the same setting.…”

Section: Multiparty Interactions With Robotsmentioning

confidence: 99%

Narratives with Robots: The Impact of Interaction Context and Individual Differences on Story Recall and Emotional Understanding

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McCoy²,

Lohani

et al. 2017

Front. Robot. AI

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Role-play scenarios have been considered a successful learning space for children to develop their social and emotional abilities. In this paper, we investigate whether socially assistive robots in role-playing settings are as effective with small groups of children as they are with a single child and whether individual factors such as gender, grade level (first vs. second), perception of the robots (peer vs. adult), and empathy level (low vs. high) play a role in these two interaction contexts. We conducted a three-week repeated exposure experiment where 40 children interacted with socially assistive robotic characters that acted out interactive stories around words that contribute to expanding children's emotional vocabulary. Our results showed that although participants who interacted alone with the robots recalled the stories better than participants in the group condition, no significant differences were found in children's emotional interpretation of the narratives. With regard to individual differences, we found that a single child setting appeared more appropriate to first graders than a group setting, empathy level is an important predictor for emotional understanding of the narratives, and children's performance varies depending on their perception of the robots (peer vs. adult) in the two conditions.

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Section: Multiparty Interactions With Robotsmentioning

confidence: 99%

Narratives with Robots: The Impact of Interaction Context and Individual Differences on Story Recall and Emotional Understanding

Leite

McCoy²,

Lohani

et al. 2017

Front. Robot. AI

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show abstract

“…There are several elements that indicate social presence or agency as highlighted in previous studies (Biocca et al 2001;Andre Pereira, Prada, and Paiva 2014;Straub 2016). They include physical embodiment or presence, ability to engage other agents in face-to-face interaction, attention allocation to other agents and perception of a reciprocal allocation from them.…”

Section: Classroom-based Social Interactionmentioning

confidence: 93%

“…Based on the premise that a good teacher should be a good communicator, able to exhibit agency and social presence (Priestley 2015;Straub 2016;Andre Pereira, Prada, and Paiva 2014). There are several elements that indicate social presence or agency as highlighted in previous studies (Biocca et al 2001;Andre Pereira, Prada, and Paiva 2014;Straub 2016).…”

Section: Classroom-based Social Interactionmentioning

confidence: 99%

“…Others are perceived reciprocal message understanding; believable verbal & non-verbal behaviours use of symbolic expressions, ability to recognize other agent(s) and perceive itself as recognized, observation of the rules of social communication (turn-taking, greetings, form of address, etc., leave-taking (Nebeska 2005), social cues (proxemics, gaze, etc.). Simulation of context-related social roles, synchronized and coordinated context-specific or context-related action/behaviour, social memory (Pereira et al, 2014;Straub, 2016, Biocca et al, 2001). …”

Section: Classroom-based Social Interactionmentioning

confidence: 99%

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Why not Robot Teachers: Artificial Intelligence for Addressing Teacher Shortage

Edwards¹,

Cheok²

2017

Preprint

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Global teacher shortage is a serious concern with grave implications for the future of education. This calls for novel ways of addressing teacher roles. The economic benefits of tireless labour inspires the need for teachers who are unlimited by natural human demands, highlighting consideration for the affordances of robotics and Artificial Intelligence in Education (AIED) as currently obtainable in other areas of human life. This however demands designing robotic personalities that can take on independent teacher roles despite strong opinions that robots will not be able to fully replace humans in the classroom of the future. In this paper, we argue for a future classroom with independent robot teachers, highlighting the minimum capabilities required of such personalities in terms of personality, instructional delivery, social interaction and affect. We describe our project on the design of a robot teacher based on these. Possible directions for future system development and studies are highlighted.

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“…social categories, moral values, norms) and to be able to adapt to different situations, including, learning how to deal with new situations. To deal with all this we need to develop computational models that address many different aspects of social behaviour, such as, group dynamics [13], social power [14], cultural behaviour [15], social importance [16], social relationships [17], interaction dynamics [18], social identity [19], personality [20] and social practices [21]. However, although we can find models with good results for particular aspects of social behaviour, it is not common to find models that are able to deal with many of the aspects at the same time.…”

Section: The Case Of Learning Social Skillsmentioning

confidence: 99%

The Importance of Socio-Emotional Agency in Applied Games for Social Learning

Prada

2016

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract.Games have a great potential as learning tools, in particular, because they provide means for players to safely explore and fail, and because they promote personal emotional experiences. To be successful, games must present a good coverage and fidelity of the interaction experience regarding the target learning goals. In the case of learning of social skills, which is one prominent area of application of games, the use of AI characters with socioemotional agency has great potential value. These characters may increase the range of social situations that players can explore (coverage). However, in order to achieve that the AI characters need to be able to present good social behaviour (fidelity). Although, several examples of computational models to achieve this can be found, developing these models remains a challenging research question. Applied Games and LearningGames have been used for other purposes beyond entertainment for several years. For example, as tools to engage people in crowdsourcing tasks, e.g. The use of games for learning is sustained in the strong connection between play and learning, noted in child development studies [7], an idea that was already discussed in the seminal work of John Locke and Jean-Jacques Rousseau in the XVII and XVIII centuries. In turn, learning has been regarded as an important activity in games for promoting fun. Following the Natural Funativity theory [8], which presents fun as a reward for learning, a game that has nothing to teach to a player will become boring and will, therefore, be abandoned.The work of Clark Abt [9], in 1970, fostered the idea of having games directly presented as tools for learning. He coined the term serious games as games that have "an explicit and carefully thought-out educational purpose and are not intended to be played primarily for amusement". Abt argued for the development of new games taking learning goals as their main concern. Since then, serious games have grown as a research field and the term has evolved (commonly, referred now as applied games or games with purpose) to incorporate other serious applications besides education. Education and training are still, nevertheless, regarded as one of the application fields with most potential. Why Applied Games Work for LearningThere are two main reasons why (good) applied games work as learning tools. First, because they enable practice, or, more importantly, because they support proactive exploration and failure. While playing, learners are motivated to explore the range of options presented in the game in the search for specific outcomes. Intended outcomes are not easily achieved, which means that players fail in the process until they achieve a good outcome. Games are good at supporting players recovering from those failures, because they do not imply major consequences in real-life. This means that players can safely fail. They do not fear failure too much and are, therefore, encouraged to explore the different options that the game offers and, for that reason, gain understand...

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Improving social presence in human-agent interaction

Cited by 41 publications

References 29 publications

Narratives with Robots: The Impact of Interaction Context and Individual Differences on Story Recall and Emotional Understanding

Narratives with Robots: The Impact of Interaction Context and Individual Differences on Story Recall and Emotional Understanding

Why not Robot Teachers: Artificial Intelligence for Addressing Teacher Shortage

The Importance of Socio-Emotional Agency in Applied Games for Social Learning

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