Designing mobile, persuasive, and motivational interactions for sustainable behaviour change remains a challenge. Setting daily step goals on a mobile application can initially stimulate participation, but both the number of steps walked each day and the use of mobile applications decrease over time, even when individuals report high levels of motivation. To address this issue, we propose the integration of tailored gamification as a motivational mechanism, grounded in psychological theories. Self-Determination Theory is used to promote competence, while Regulatory Focus Theory is used to support individual differences and for tailoring motivational interactions. Gamification aims at reinforcing motivation and action-taking. In this article, we explain how we designed a motivational mobile application that incorporates these gamification mechanisms, and we describe how we evaluated its use during 28 days with 37 users. Results suggest a decrease in amotivation levels, and that promotion-focused individuals find gamified elements more motivational than prevention-focused ones.

Evaluating the quality of generated nonverbal behavior remains a major challenge in the development of generative models.While human evaluations are reliable, they are costly and impractical for large-scale or iterative optimization.In this work, we propose an objective evaluation framework based on aggregated ranks across multiple fidelity and diversity metrics, computed from both raw features and learned latent representations.Compared to existing works, our framework emphasizes consistency across multiple metrics, aiming to provide a more holistic assessment.

Storytelling in virtual reality (VR) offers users freedom to explore narratives from any perspective, enhancing immersion but risking that some story elements may be overlooked.This study examines how dynamically increasing virtual crowd density during moments of dramatic tension can be used as a cue to sustain narrative engagement.Participants experience a VR murder mystery where the crowd size grows in sync with the story's tension.This research aims to validate crowd density as an environmental cue for maintaining user attention and emotional involvement in VR narratives, contributing to the development of affective design principles for immersive storytelling systems.

Interactive storytelling in virtual reality is an emerging form of media that fundamentally transforms how stories are experienced.Unlike traditional formats such as film or literature, virtual reality grants users the freedom to look around and explore the environment from any angle, creating an enhanced sense of presence and immersion.However, this increased agency also presents new challenges for narrative design: users may miss key story events because their attention is not directed in the same way as in non-interactive media.Virtual reality narratives require new approaches to staging in order to fully engage users and effectively convey emotional content.This project presents a virtual environment designed to test out cues for interactive storytelling to make the user more engaged with a story.The aim is to study how authors can help viewers engage in the story and feel the emotions conveyed, notably by using various cues in the environment (sounds, light, behaviors).This demo presents the first tested cue, the gradual appearance of a crowd of virtual agents, to encourage viewer engagement during moments of dramatic tension.Participants are presented a story

Research on non-verbal behavior generation for social interactive agents focuses mainly on the believability and synchronization of non-verbal cues with speech. However, existing models, predominantly based on deep learning architectures, often perpetuate biases inherent in the training data. This raises ethical concerns, depending on the intended application of these agents. This paper addresses these issues by first examining the influence of gender on facial non-verbal behaviors. We concentrate on gaze, head movements, and facial expressions. We introduce a classifier capable of discerning the gender of a speaker from their non-verbal cues. This classifier achieves high accuracy on both real behavior data, extracted using state-of-the-art tools, and synthetic data, generated from a model developed in previous work. Building upon this work, we present a new model, FairGenderGen, which integrates a gender discriminator and a gradient reversal layer into our previous behavior generation model. This new model generates facial non-verbal behaviors from speech features, mitigating gender sensitivity in the generated behaviors. Our experiments demonstrate that the classifier, developed in the initial phase, is no longer effective in distinguishing the gender of the speaker from the generated non-verbal behaviors.

The COPALZ model [3] is designed to simulate emotional disorders of a virtual agent representing a patient in a pedagogical scenario for training healthcare professionals. The identification of emotional and expressive pathologies may sometimes require an assessment over multiple interactions with trainees, as behaviors associated with emotional disorders are not systematically observed on patients' behavior in the early stages of the pathology. The aim of this article is to propose an evaluation method for this model, which, in the case of computational models of affects that generate nonverbal behaviors, requires a tailored approach. This task can be difficult as the correspondence between a pathology and observed behaviors is not systematic. Our method focuses on the pedagogical dimension and on the ability of the model to display pathological behaviors identified as relevant for training interactions. The results highlight the ability of the studied model to simulate multiple relevant pedagogical situations and adapt the virtual patient's behaviors to the evolution of the pathology and the patient's mood instability. This method gives interesting perspectives for the evaluation of virtual patients and computational models of affect.

This paper presents a study that assesses the effectiveness of Motivational Interviewing (MI) interventions on the engagement of adults over 50 years to do more physical activity. While MI's four step process may easily be adapted to online devices, modeling the MI approach based on empathy may be more challenging (as opposed to directive interviews in which the user is encouraged to adopt a favorable attitude from the outset). Three devices are compared: a non-directive MI-based website, a non-directive MI-based virtual agent and a directed MI-based one. Our results show that the non-directive virtual agent tends to be perceived as more empathetic and trustworthy than the directed one, and that it significantly raises the participants' self-efficacy to overcome barriers, and positively impacts intrinsic motivation. We discuss mentions several implications for the adaptation of MI to the context of ECA.

In social interactions between humans and Embodied Conversational Agents (ECAs) conversational interruptions may occur. ECAs should be prepared to detect, manage and react to such interruptions in order to keep the interaction smooth, natural and believable. In this paper, we examined nonverbal reactions exhibited by an interruptee during conversational interruptions and we propose a novel technique driven by an evolutionary algorithm to build a computational model for ECAs to manage user's interruptions. We propose a taxonomy of conversational interruptions adapted from social psychology, an annotation schema for semi-automatic detection of user's interruptions and a corpus-based observational analysis of human nonverbal reactions to interruptions. Then we present a methodology for building an ECA behavioral model including the design and realization of an interactive study driven by an evolutionary algorithm, where participants interactively built the most appropriate set of multimodal reactive behaviours for an ECA to display interpersonal attitudes (friendly/hostile) through nonverbal reactions to a conversational interruption.

Transactive Memory System (TMS) is a mental representation of the distribution of knowledge between the members of a team. Can an Embodied Conversational Agent perform as well as a Human when intervening as a leader to support the development of the team’s TMS? And, if yes, are there differences in the way the team perceives their respective interventions? In this paper, a perceptive online study is conducted on how Human leader interventions affect the perception of a team’s TMS. The results are compared to the ones from a previous study evaluating an Embodied Conversational agent leader rather than a human one. Both the agent and the human adopt nonverbal behaviors characterizing 2 leadership styles: Transformational (TFL) and Transactional (TAL). TFL is expected to stimulate team members curiosity and creativity in problem-solving; instead, TAL emphasizes the role of the leader in supervising the team, providing it with feedback when needed. The results show that the intervention from both the agent and the human are perceived to potentially improve the perceived TMS of a team. Another interesting insight is that the TFL style works better when performed by the Human, where both the TAL and TFL style perform well when realized by the agent.

New technologies for autism focus on the training of either social skills or motor skills, but not both. Such a dichotomy omits a wide range of joint action tasks that require the coordination of two persons (e.g. moving a heavy furniture). The training of these physical tasks performed in dyad has great potential to foster inclusiveness while having an impact on both social and motor skills. In this paper, we present the design of a tangible and virtual interactive system for the training of children with Autism Spectrum Disorder (ASD) in performing joint actions. The proposed system is composed of a virtual character projected onto a surface on which a tangible object is magnetized: both the user and the virtual character hold the object, thus simulating a joint action. We report and discuss preliminary results of a field training study, which shows the potential of the interactive system.

We present WoNoWa, a novel multi-modal dataset of small group interactions in collaborative tasks. The dataset is explicitly designed to elicit and to study over time a Transactive Memory System (TMS), a group's emergent state characterizing the group's meta-knowledge about "who knows what". A rich set of automatic features and manual annotations, extracted from the collected audio-visual data, is available on request for research purposes. Features include individual descriptors (e.g., position, Quantity of Motion, speech activity) and group descriptors (e.g., F-formations). Additionally, participants' self-assessments are available. Preliminary results from exploratory analyses show that the WoNoWa design allowed groups to develop a TMS that increased across the tasks. These results encourage the use of the WoNoWa dataset for a better understanding of the relationship between behavioural patterns and TMS, that in turn could help to improve group performance.

Virtual Reality and immersive experiences, which allow players to share the same virtual environment as the characters of a virtual world, have gained more and more interest recently. In order to conceive these immersive virtual worlds, one of the challenges is to give to the characters that populate them the ability to express behaviors that can support the immersion. In this work, we propose a model capable of controlling and simulating a conversational group of social agents in an immersive environment. We describe this model which has been previously validated using a regular screen setting and we present a study for measuring whether users recognized the attitudes expressed by virtual agents through the realtime generated animations of nonverbal behavior in an immersive setting. Results mirrored those of the regular screen setting thus providing further insights for improving players experiences by integrating them into immersive simulated group conversations with characters that express different interpersonal attitudes.

In the everyday world people form small conversing groups where social interaction takes place, and much of the social behavior takes place through managing interpersonal space (i.e., proxemics) and group formation, signaling their attentio to others (i.e., through gaze behavior), and expressing certain attitudes, for example, friendliness, by smiling, getting close through increased engagement and intimacy, and welcoming newcomers. Many real-time interactive systems feature virtual anthropomorphic characters in order to simulate conversing groups and add plausibility and believability to the simulated environments. However, only a few have dealt with autonomous behavior generation, and in those cases, the agents’ exhibited behavior should be evaluated by users in terms of appropriateness, believability, and conveyed meaning (e.g., attitudes). In this article we present an integrated intelligent interactive system for generating believable nonverbal behavior exhibited by virtual agents in small simulated group conversations. The produced behavior supports group formation management and the expression of interpersonal attitudes (friendly vs. unfriendly) both among the agents in the group (i.e., in-group attitude) and towards an approaching user in an avatar-based interaction (out-group attitude). A user study investigating the effects of these attitudes on users’ social presence evaluation and proxemics behavior (with their avatar) in a three-dimensional virtual city environment is presented. We divided the study into two trials according to the task assigned to users, that is, joining a conversing group and reaching a target destination behind the group. Results showed that the out-group attitude had a major impact on social presence evaluations in both trials, whereby friendly groups were perceived as more socially rich. The user’s proxemics behavior depended on both out-group and in-group attitudes expressed by the agents. Implications of these results for the design and implementation of similar intelligent interactive systems for the autonomous generation of agents’ multimodal behavior are briefly discussed.

Virtual worlds are more and more populated with autonomous conversational agents embodying different roles like tutor, guide, or personal assistant. In order to create more engaging and natural interactions, these agents should be endowed with social capabilities such as expressing different social attitudes through their behaviors. In this paper, we present the architecture of a socio-conversational agent composed of communicative components to detect and respond verbally and non-verbally to the user's speech and to convey different social attitudes. This paper presents the main components of this architecture. These descrpitions are illustrated with scenarios of interaction.