The research and development (R&D) of intelligent virtual agents (IVAs) is inherently complex.We aim to manage this complexity by releasing a major update to the Virtual Human Toolkit, combining the best aspects of academic and commercial approaches into a principled R&D platform that emphasizes interoperability, extendability, re-use, and support for multiple hardware targets.We demonstrate the current status of the VHToolkit on Windows desktop and Quest 3 AR/VR headset.

Agent-based training systems can enhance people's social skills. The effective development of these systems needs a comprehensive architecture that outlines their components and relationships. Such an architecture can pinpoint improvement areas and future outlooks. This paper presents ARTES: a general architecture illustrating how components of agent-based social training systems work together. We studied existing systems and architectures for training and tutoring to design ARTES and identify its essential components and interaction characteristics. ARTES comprises two core components: the agent simulation of social situations, and educational elements to provide guided learning. We link ARTES's crucial components to four primary learning theories (behaviourism, cognitivism, social cognitive theory, and constructivism) to illustrate the role of agent simulation and tutoring elements in establishing desired learning outcomes. Furthermore, we map ARTES's components against eight architectures, 43 systems and three tools to indicate the components' relevance, completeness, generalisation, and deployment potential across contexts. In addition to ARTES, the paper also contributes by identifying future improvements and research directions, such as the agent's thinking, tutoring methods, knowledge transfer, and ethical implications. We believe ARTES can help bridge the gap between virtual human simulations and impactful educational learning, offering training system developers desirable features like understandability and adaptability.

As the demand for socially intelligent Virtual Humans (VHs) increases, so follows the demand for effective and efficient cross-discipline collaboration that is required to bring these VHs “to life”. One avenue for increasing cross-discipline fluency is the aggregation and organization of seemingly disparate areas of research and development (e.g., graphics and emotion models) that are essential to the field of VH research. Our initial investigation (1) identifies and catalogues research streams concentrated in three multidisciplinary VH topic clusters within the domain of social intelligence, Emotion, Social Behavior, and The Face, (2) brings to the forefront key themes and prolific authors within each topic cluster, and (3) provides evidence that a full scoping review is warranted to further map the field, aggregate research findings, and identify gaps in the research. To enable collaboration, we provide full access to the refined VH cluster datasets, key word and author word clouds, as well as interactive evidence maps.

The research and development (R&D) of intelligent virtual agents (IVAs) is inherently complex. We aim to manage this complexity by combining the best aspects of academic and commercial approaches into a principled R&D platform that emphasizes interoperability, ex-tendability, re-use, and support for multiple hardware targets. This IVA platform, the Virtual Human Toolkit 2.0, is a re-architecture of our earlier work and combines a modular message passing architecture with that of a microservices architecture. This paper discusses our approach, design decisions, lessons learned, and current status of this ongoing effort. We illustrate the strengths of the architecture, how best to use commodity AI cloud services in one's own work, and how to port legacy stand-alone software to a web service.

A major impediment to the success of virtual agents is the inability of non-technical experts to easily author content. To address this barrier we present VHMason, a multimodal authoring tool designed to help creative authors build embodied conversational agents. We introduce the novel aspects of this authoring tool and explore a use case of the creation of an agent-led educational experience implemented at Children's Hospital Los Angeles (CHLA).

Rapidly creating educational content that is effective, engaging, and low-cost is a challenge. We present Canvas, a tool for educators that addresses this challenge by enabling the generation of educational video, led by an intelligent virtual agent, that combines rapid nonverbal behavior generation techniques with end-user facing authoring tools. With Canvas, educators can easily produce compelling educational videos with a minimum of investment by leveraging existing content provided by the tool (e.g., characters and environments) while incorporating their own custom content (e.g., images and video clips). Canvas has been delivered to the Smithsonian Science Education Center and is currently being evaluated internally before wider release. We discuss the system, feature set, design process, and lessons learned.

We present one of the first applications of virtual humans in Augmented Reality (AR), which allows young adults with Autism Spectrum Disorder (ASD) the opportunity to practice job interviews. It uses the Magic Leap's AR hardware sensors to provide users with immediate feedback on six different metrics, including eye gaze, blink rate and head orientation. The system provides two characters, with three conversational modes each. Ported from an existing desktop application, the main development lessons learned were: 1) provide users with navigation instructions in the user interface, 2) avoid dark colors as they are rendered transparently, 3) use dynamic gaze so characters maintain eye contact with the user, 4) use hardware sensors like eye gaze to provide user feedback, and 5) use surface detection to place characters dynamically in the world.

We present a versatile system for training job interviewing skills that focuses specifically on segments of the population facing increased challenges during the job application process. In particular, we target those with Autism Spectrum Disorder (ADS), veterans transitioning to civilian life, and former convicts integrating back into society. The system itself follows the SAIBA framework and contains several interviewer characters, who each represent a different type of vocational field, (e.g. service industry, retail, office, etc.) Each interviewer can be set to one of three conversational modes, which not only affects what they say and how they say it, but also their supporting body language. This approach offers varying difficulties, allowing users to start practicing with interviewers who are more encouraging and accommodating before moving on to personalities that are more direct and indifferent. Finally, the user can place the interviewers in different environmental settings (e.g. conference room, restaurant, executive office, etc.), allowing for many different combinations in which to practice.

We present the SimSensei system, a fully automatic virtual agent that conducts interviews to assess indicators of psychological distress. We emphasize on the perception part of the system, a multimodal framework which captures and analyzes user state for both behavioral understanding and interactional purposes.

We present the SimSensei system, a fully automatic virtual agent that conducts interviews to assess indicators of psychological distress. With this demo, we focus our attention on the perception part of the system, a multimodal framework which captures and analyzes user state behavior for both behavioral understanding and interactional purposes. We will demonstrate real-time user state sensing as a part of the SimSensei architecture and discuss how this technology enabled automatic analysis of behaviors related to psychological distress.