A Review of the Practical Applications of Pedagogic Conversational Agents to Be Used in School and University Classrooms

Pedagogic Conversational Agents (PCAs) can be defined as autonomous characters that cohabit learning environments with students to create rich learning interactions. Currently, there are many agents reported in the literature of this fast-evolving field. In this paper, several designs of PCAs used as instructors, students, or companions are reviewed using a taxonomy to analyze the possibilities that PCAs can bring into the classrooms. Finally, a discussion as to whether this technology could become the future of education depending on the design trends identified is open for any educational technology practitioner, researcher, teacher, or manager involved in 21st century education.

Pedagogical Agents: Back to the Future

Back in the 1990s we started work on pedagogical agents, a new user interface paradigm for interactive learning environments. Pedagogical agents are autonomous characters that inhabit learning environments and can engage with learners in rich, face-to-face interactions. Building on this work, in 2000 we, together with our colleague, Jeff Rickel, published an article on pedagogical agents that surveyed this new paradigm and discussed its potential. We made the case that pedagogical agents that interact with learners in natural, life-like ways can help learning environments achieve improved learning outcomes. This article has been widely cited, and was a winner of the 2017 IFAAMAS Award for Influential Papers in Autonomous Agents and Multiagent Systems (IFAAMAS, 2017). On the occasion of receiving the IFAAMAS award, and after twenty years of work on pedagogical agents, we decided to take another look at the future of the field. We’ll start by revisiting our predictions for pedagogical agents back in 2000, and examine which of those predictions panned out. Then, informed what we have learned since then, we will take another look at emerging trends and the future of pedagogical agents. Advances in natural language dialogue, affective computing, machine learning, virtual environments, and robotics are making possible even more lifelike and effective pedagogical agents, with potentially profound effects on the way people learn.

Using Several Types of Virtual Characters in Sports - a Literature Survey

This article discusses the development and application of virtual environments (VEs) in the domain of exercise as well as research in recreational and high-performance sports. A special focus is put on the use of virtual characters (VCs). For its elaboration, the following criteria parameters were chosen: scene content and the role of the VC, output device, kind of additional feedback, level of expertise of the tested participants, kind of user’s movement (reaction), kind of the visualization of the user’s body, kind of study and kind of evaluation. We explored the role of VCs embodying virtual opponents, teammates, or coaches in sports. We divided these VCs in passive and autonomous characters. Passive VCs are not affected by the user, whereas autonomous VCs adapt autonomously to the user’s movements and positions. We identified 44 sport related VEs, thereof 22 each in the domain of recreational sports and high-performance sports: of the identified 44 VEs, 19 VEs are without VC, 20 VEs with passive VCs, and 5 VEs with autonomous VCs. We categorized studies examining expert athletes in high-performance sports as well as studies analyzing novices, beginners or advanced athletes in recreational sports. Nevertheless, all identified systems are suitable for athletes of recreational and high-performance level

The Ninth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE): A Report

The Ninth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) was held October 14–18, 2013, at Northeastern University in Boston, Massachusetts. The mission of the AIIDE conference is to provide a forum for researchers and game developers to discuss ways that AI can enhance games and other forms of interactive entertainment. In addition to presentations on adapting standard AI techniques such as search, planning and machine learning for use within games, key topic areas include creating realistic autonomous characters, interactive narrative, procedural content generation, and integrating AI into game design and production tools.