This work addresses the challenge of creating virtual agents that are able to portray culturally appropriate behavior when interacting with other agents or humans. Because culture influences how people perceive their social reality it is important to have agent models that explicitly consider social elements, such as existing relational factors. We addressed this necessity by integrating culture into a novel model for simulating human social behavior. With this model, we operationalized a particular dimension of culture-individualism versus collectivism-within the context of an interactive narrative scenario that is part of an agent-based tool for intercultural training. Using this scenario we conducted a cross-cultural study in which participants from a collectivistic country (Portugal) were compared with participants from an individualistic country (the Netherlands) in the way they perceived and Multi-Agent Syst (2016) 30:931-962 interacted with agents whose behavior was either individualistic or collectivistic, according to the configuration of the proposed model. In the obtained results, Portuguese subjects rated the collectivistic agents more positively than the Dutch but both countries had a similarly positive opinion about the individualistic agents. This experiment sheds new light on how people from different countries differ when assessing the social appropriateness of virtual agents, while also raising new research questions on this matter.

Integrating culture into the behavioural models of virtual characters requires knowledge from very different disciplines such as cross-cultural psychology and computer science. If culture-related behavioural differences are simulated with a virtual character system, users might not necessarily understand the intent of the designer. This is, in part, due to the influence of culture on not only users, but also designers. To gain a greater understanding of the instantiation of culture in the behaviour of virtual characters, and on this potential mismatch between designer and user, we have conducted two experiments. In these experiments, we tried to simulate one dimension of culture (Masculinity vs. Femininity) in the behaviour of virtual characters. We created four scenarios in the first experiment and six in the second. In each of these scenarios, the same two characters interact with each other. The verbal and non-verbal behaviour of these characters differs depending on their cultural scripts. In two user perception studies, we investigated how these differences are judged by human participants with different cultural backgrounds. Besides expected differences between participants from Masculine and Feminine countries, we found significant differences in perception between participants from Individualistic and Collectivistic countries. We also found that the user's interpretation of the character's motivation had a significant influence on the perception of the scenarios. Based on our findings, we give recommendations for researchers that aim to design culture-specific behaviours for virtual characters.

Abstract.Creating agents that are capable of emulating similar sociocultural dynamics to those found in human interaction remains as one of the hardest challenges of artificial intelligence. This problem becomes particularly important when considering embodied agents that are meant to interact with humans in a believable and empathic manner. In this article, we introduce a conceptual model for socio-cultural agents, and, based on this model, we present a set of requirements for these agents to be capable of showing appropriate socio-cultural behaviour. Our model differentiates between three levels of instantiation: the interaction level, consisting of elements that may change depending on the people involved, the group level, consisting of elements that may change depending on the group affiliation of the people involved, and the society level, consisting of elements that may change depending on the cultural background of those involved. As such, we are able to have culture alter agents' social relationships rather than directly determining actions, allowing for virtual agents to act more appropriately in any social or cultural context.

Digital support during self-regulated learning can improve metacognitive knowledge and skills in learners. Previous research has predominantly focused on embedding metacognitive support in domain-specific content. In this study, we examine a detached approach where digital metacognitive support is offered in parallel to ongoing domain-specific training via a digital tool. The primary support mechanism was self-explication, where learners are prompted to make, otherwise implicit, metacognition concrete.In a controlled pre-test/post-test quasi-experiment, we compared domain-specific and domain-general support and assessed the effects, use, and learners' perceptions of the tool. The results showed that self-explication is an effective mechanism to support and improve metacognition during self-regulated learning. Furthermore, the results confirm the effectiveness of offering detached metacognitive support. While only domain-specific metacognitive support was found to be effective, quantitative and qualitative analysis warrant further research into domain-general and detached metacognitive support.The results also indicated that, while students with higher metacognition found a lack of relevance of using the tool, students with lower metacognition are less likely to make (structural) use of the available support. A key challenge for future research is thus to adapt metacognitive support to learner needs, and to provide metacognitive support to those who would benefit from it the most. The paper concludes by formulating implications for future research as well as design of digital metacognitive support.

Game-based learning (GBL) is an interactive form of training in which instructional elements are combined with motivational elements within one GBL-environment. Under the right circumstances, GBL can contribute to both learning and motivation. It is, however, unclear which elements in the design of GBL-environments can encourage effective and efficient learning. Metacognition is cognition about cognition: knowing about one's own knowledge and applying that knowledge in practice. While research has found that learners can benefit from metacognitive support within learning environments, it is unclear how to encourage metacognition in GBL-environments to improve learning effectively and efficiently. In this paper, we present a qualitative review of metacognition within GBL. We discuss the objectives, interventions, and effects reported in studies that address metacognition in GBL-environments. The aim of this review is to inform educational designers, researchers, and other professionals who want to address metacognition in GBL, and the review concludes with concrete implications for design and research. What is the significance of this article for the general public?As the role of metacognition in learning effectively and efficiently is clear, a crucial next step in the design and research of GBL-environments is how to promote metacognition in learners. In this paper we present a first comprehensive overview of metacognition in GBL. We identified nine metacognitive intervention types for GBL as well as initial game design principles for encouraging metacognition. As such, the findings from this review aid designers, researchers, and other professionals in the design of technology-enhanced learning environments, in general, and GBL-environments, in particular.

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Emotional synthetic characters are able to evaluate (appraise) events as positive or negative with their emotional states being triggered by several factors. Currently, the vast majority of  models for appraisal in synthetic characters consider factors related to the goals and preferences of the characters. We argue that appraisals that only take into consideration these "personal" factors are incomplete as other more social factors, such as the normative and the social context, including in-group and out-group relations, should be considered as well. Without them, moral emotions such as shame cannot be appraised, limiting the believability of the characters in certain situations. We present a model for the appraisal of characters' actions that evaluates whether actions by in-group and out-group members which conform, or not, to social norms generate different emotions depending on the social relations between the characters. The model was then implemented in an architecture for virtual agents and evaluated with humans. Results suggest that the emotions generated by our model are perceived by the participants, taking into account the social context and that participants experienced very similar emotions, both in type and intensity, to the emotions appraised and generated by the characters.

Abstract. In this paper, we describe a cultural training system based on an interactive storytelling approach and a culturally-adaptive agent architecture, for which a user-defined gesture set was created. 251 full body gestures by 22 users were analyzed to find intuitive gestures for the in-game actions in our system. After the analysis we integrated the gestures in our application using our framework for full body gesture recognition. We further integrated a second interaction type which applies a graphical interface controlled with freehand swiping gestures.