An investigation into the efficacy of avatar-based systems for student advice

Student support is an important function in all universities. Most students expect access to support 24/7, but support staff cannot be available at all times of day. This paper addresses this problem, describing the development of an avatar-based system to guide students through the materials provided by a university student employability service. Firstly, students and staff were surveyed to establish the demand for such a system. The system was then constructed. Finally, the system was evaluated by students and staff, which led to a clearer understanding of the optimal role for avatar-based systems and consequent improvements to the system’s functionality

An investigation into the efficacy of avatar-based systems for student advice

Student support is an important function in all universities. Most students expect access to support 24/7, but support staff cannot be available at all times of day. This paper addresses this problem, describing the development of an avatar-based system to guide students through the materials provided by a university student employability service. Firstly, students and staff were surveyed to establish the demand for such a system. The system was then constructed. Finally, the system was evaluated by students and staff, which led to a clearer understanding of the optimal role for avatar-based systems and consequent improvements to the system’s functionality

The Effects of Engaging and Affective Behaviors of Virtual Agents in Group Decision-Making

Virtual agents (VAs) need to exhibit engaged and affective behavior in order to become more effective social actors in our daily lives. However, such behaviors need to conform to social norms, especially in organizational settings. This study examines how different VA behaviors influence subjects' perceptions and actions in group decision-making processes. Participants exposed to VAs demonstrated varying levels of engagement and affective behavior during the group discussions. Engagement refers to the VA's focus on the group task, while affective behavior represents the VA's emotional state. The findings indicate that VA engagement positively influences user behavior, particularly in attention allocation. However, it has minimal impact on subjective perception. Conversely, affective expressions of VAs have a negative impact on subjective perceptions, such as social presence, social influence, and trustworthiness. Interestingly, in 64 discussions for tasks, only seven showed a decline in group scores compared to individual scores, and in six of these cases, the VA exhibited a non-engaged and affective state. We discuss the results and the potential implications for future research on using VAs in group meetings. It provides valuable insights for improving VA behavior as a team member in group decision-making scenarios and guides VA design in organizational contexts.Comment: Under Review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A Framework for Research in Gamified Mobile Guide Applications using Embodied Conversational Agents (ECAs)

Mobile Guides are mobile applications that provide players with local and location-based services (LBS), such as navigation assistance, where and when they need them most. Advances in mobile technologies in recent years have enabled the gamification of these applications, opening up new opportunities to transfer education and culture through game play. However, adding traditional game elements such as PBLs (points, badges, and leaderboards) alone cannot ensure that the intended learning outcomes will be met, as the player’s cognitive resources are shared between the application and the surrounding environment. This distribution of resources prevents players from easily immersing themselves into the educational scenario. Adding artificial conversational characters (ECAs) that simulate the social norms found in real-life human-to-human guide scenarios has the potential to address this problem and improve the player’s experience and learning of cultural narratives [1]. Although significant progress has been made towards creating game-like mobile guides with ECAs ([2], [3]), there is still a lack of a unified framework that enables researchers and practitioners to investigate the potential effects of such applications to players and how to approach the concepts of player experience, cognitive accessibility and usability in this context. This paper presents a theoretically-well supported research framework consisted of four key components: differences in players, different features of the gamified task, aspects of how the ECA looks, sound or behaves and different mobile environments. Furthermore, it provides based on this framework a working definition of what player experience, cognitive accessibility and usability are in the context of game-like mobile guide applications. Finally, a synthesis of the results of six empirical studies conducted within this research framework is discussed and a series of design guidelines for the effective gamification of mobile guide applications using ECAs are presented. Results show that an ECA can positively affect the quality of the player’s experience, but it did not elicit better player retention of cultural narratives and navigation of routes

A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

People increasingly use videos on the Web as a source for learning. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning for the period from 2016 to 2021. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories: (1) audio features, (2) visual features, (3) textual features, (4) instructor behavior, (5) learners activities, (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative research directions: (1) proposals of tools to support video-based learning, (2) studies with controlled experiments, (3) data analysis studies, and (4) proposals of design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learner activities, and interactive features. Text of transcripts, video frames, and images (figures and illustrations) are most frequently used by tools that support learning through videos. The learner activity is heavily explored through log files in data analysis studies, and interactive features have been frequently scrutinized in controlled experiments. We complement our review by contrasting research findings that investigate the impact of video characteristics on the learning effectiveness, report on tasks and technologies used to develop tools that support learning, and summarize trends of design guidelines to produce learning video

Gender stereotypes in virtual agents

Visual, behavioural and verbal cues for gender are often used in designing virtual agents to take advantage of their cultural and stereotypical effects on the users. However, recent studies point towards a more gender-balanced view of stereotypical traits and roles in our society. This thesis is intended as an effort towards a progressive and inclusive approach for gender representations in virtual agents. The contributions are two-fold. First, in an iterative design process, representative male, female and androgynous embodied AI agents were created with few differences in their visual attributes. Second, these agents were then used to evaluate the stereotypical assumptions of gendered traits and roles in AI virtual agents. The results showed that, indeed, gender stereotypes are not as effective as previously assumed, and androgynous agents could represent a middle-ground between gendered stereotypes. The thesis findings are presented in the hope to foster discussions in virtual agent research and the frequent stereotypical use of gender representations

Affective Communication between ECAs and Users in Collaborative Virtual Environments: The REVERIE European Parliament Use Case

This paper discusses the enactment and evaluation of Embodied Conversational Agents (ECA) capable of affective communication in Collaborative Virtual Environments (CVE) for learning. The CVE discussed is a reconstruction of the European Parliament in Brussels developed using the REVERIE (Real and Virtual Engagement In Realistic Immersive Environment) framework. REVERIE is a framework designed to support the creation of CVEs populated by ECAs capable of natural human-like behaviour, physical interaction and engagement. The ECA provides a tour of the virtual parliament and participates in the learning activity as an intervention mechanism to engage students. The ECA is capable of immediacy behaviour (verbal and non-verbal) and interactions to support a dialogic learning scenario. The design of the ECA is grounded on a theoretical framework that addresses the required characteristics of the ECA to successfully support collaborative learning. In this paper, we discuss the Heuristic Evaluation of the REVERIE ECA which revealed a wealth of usability problems that led to the development of a list of design recommendations to improve their usability, including its immediacy behaviours and interactions. An ECA capable of effectively creating rapport should result in more positive experiences for participants and better learning results for students in dialogic learning scenarios. Future work aims to evaluate this hypothesis in real-world scenarios with teachers and students participating in a shared virtual educational experienc

Persistence of the uncanny valley: the influence of repeated interactions and a robot's attitude on its perception

Zlotowski J, Sumioka H, Nishio S, Glas DF, Bartneck C, Ishiguro H. Persistence of the uncanny valley: the influence of repeated interactions and a robot's attitude on its perception. Frontiers in Psychology. 2015;6:883

Real-time generation and adaptation of social companion robot behaviors

Social robots will be part of our future homes. They will assist us in everyday tasks, entertain us, and provide helpful advice. However, the technology still faces challenges that must be overcome to equip the machine with social competencies and make it a socially intelligent and accepted housemate. An essential skill of every social robot is verbal and non-verbal communication. In contrast to voice assistants, smartphones, and smart home technology, which are already part of many people's lives today, social robots have an embodiment that raises expectations towards the machine. Their anthropomorphic or zoomorphic appearance suggests they can communicate naturally with speech, gestures, or facial expressions and understand corresponding human behaviors. In addition, robots also need to consider individual users' preferences: everybody is shaped by their culture, social norms, and life experiences, resulting in different expectations towards communication with a robot. However, robots do not have human intuition - they must be equipped with the corresponding algorithmic solutions to these problems. This thesis investigates the use of reinforcement learning to adapt the robot's verbal and non-verbal communication to the user's needs and preferences. Such non-functional adaptation of the robot's behaviors primarily aims to improve the user experience and the robot's perceived social intelligence. The literature has not yet provided a holistic view of the overall challenge: real-time adaptation requires control over the robot's multimodal behavior generation, an understanding of human feedback, and an algorithmic basis for machine learning. Thus, this thesis develops a conceptual framework for designing real-time non-functional social robot behavior adaptation with reinforcement learning. It provides a higher-level view from the system designer's perspective and guidance from the start to the end. It illustrates the process of modeling, simulating, and evaluating such adaptation processes. Specifically, it guides the integration of human feedback and social signals to equip the machine with social awareness. The conceptual framework is put into practice for several use cases, resulting in technical proofs of concept and research prototypes. They are evaluated in the lab and in in-situ studies. These approaches address typical activities in domestic environments, focussing on the robot's expression of personality, persona, politeness, and humor. Within this scope, the robot adapts its spoken utterances, prosody, and animations based on human explicit or implicit feedback.Soziale Roboter werden Teil unseres zukünftigen Zuhauses sein. Sie werden uns bei alltäglichen Aufgaben unterstützen, uns unterhalten und uns mit hilfreichen Ratschlägen versorgen. Noch gibt es allerdings technische Herausforderungen, die zunächst überwunden werden müssen, um die Maschine mit sozialen Kompetenzen auszustatten und zu einem sozial intelligenten und akzeptierten Mitbewohner zu machen. Eine wesentliche Fähigkeit eines jeden sozialen Roboters ist die verbale und nonverbale Kommunikation. Im Gegensatz zu Sprachassistenten, Smartphones und Smart-Home-Technologien, die bereits heute Teil des Lebens vieler Menschen sind, haben soziale Roboter eine Verkörperung, die Erwartungen an die Maschine weckt. Ihr anthropomorphes oder zoomorphes Aussehen legt nahe, dass sie in der Lage sind, auf natürliche Weise mit Sprache, Gestik oder Mimik zu kommunizieren, aber auch entsprechende menschliche Kommunikation zu verstehen. Darüber hinaus müssen Roboter auch die individuellen Vorlieben der Benutzer berücksichtigen. So ist jeder Mensch von seiner Kultur, sozialen Normen und eigenen Lebenserfahrungen geprägt, was zu unterschiedlichen Erwartungen an die Kommunikation mit einem Roboter führt. Roboter haben jedoch keine menschliche Intuition - sie müssen mit entsprechenden Algorithmen für diese Probleme ausgestattet werden. In dieser Arbeit wird der Einsatz von bestärkendem Lernen untersucht, um die verbale und nonverbale Kommunikation des Roboters an die Bedürfnisse und Vorlieben des Benutzers anzupassen. Eine solche nicht-funktionale Anpassung des Roboterverhaltens zielt in erster Linie darauf ab, das Benutzererlebnis und die wahrgenommene soziale Intelligenz des Roboters zu verbessern. Die Literatur bietet bisher keine ganzheitliche Sicht auf diese Herausforderung: Echtzeitanpassung erfordert die Kontrolle über die multimodale Verhaltenserzeugung des Roboters, ein Verständnis des menschlichen Feedbacks und eine algorithmische Basis für maschinelles Lernen. Daher wird in dieser Arbeit ein konzeptioneller Rahmen für die Gestaltung von nicht-funktionaler Anpassung der Kommunikation sozialer Roboter mit bestärkendem Lernen entwickelt. Er bietet eine übergeordnete Sichtweise aus der Perspektive des Systemdesigners und eine Anleitung vom Anfang bis zum Ende. Er veranschaulicht den Prozess der Modellierung, Simulation und Evaluierung solcher Anpassungsprozesse. Insbesondere wird auf die Integration von menschlichem Feedback und sozialen Signalen eingegangen, um die Maschine mit sozialem Bewusstsein auszustatten. Der konzeptionelle Rahmen wird für mehrere Anwendungsfälle in die Praxis umgesetzt, was zu technischen Konzeptnachweisen und Forschungsprototypen führt, die in Labor- und In-situ-Studien evaluiert werden. Diese Ansätze befassen sich mit typischen Aktivitäten in häuslichen Umgebungen, wobei der Schwerpunkt auf dem Ausdruck der Persönlichkeit, dem Persona, der Höflichkeit und dem Humor des Roboters liegt. In diesem Rahmen passt der Roboter seine Sprache, Prosodie, und Animationen auf Basis expliziten oder impliziten menschlichen Feedbacks an