Self- and Co-Regulating Anthropomorphic Pedagogical Agents

想象一下，一个软件代理学习如何根据您自己的习惯和优势，以及基于之前成千上万名其他成功学生的学习分析模式，为您制定个性化的教育和培训策略。一个值得信赖的朋友、伙伴和盟友，可以帮助你确定目标，并根据你自己的学习风格，与你自己最深层次的价值观、个人、社会和其他标准保持一致，制定出实现这些目标的最有可能的途径。这项研究旨在建立和测试一套这样的智能代理，称为拟人化代理，它模仿人类的特征，将帮助学习者完成他们的调节任务。此外，这些代理人自我调节他们自己的拟人化特征，如主动性和主题能力，以便激发高度的信任、共享的观点和实体性-从而增加学习者将他们视为教育过程中合格合作伙伴的意愿。

Imagine a software agent that learns how to develop personalized education and training strategies for you based on your own habits and strengths, and based on the learning analytics patterns of thousands of other successful students that have come before. A trusted friend, partner, and ally that can help identify goals, and map out the most likely path to achieve those goals, based on your own learning styles, consistent with your own deepest values, personal, social, and other standards. This research aims to build and test a suite of such intelligent agents, called anthropomorphic agents, that mimic human-like traits and will assist learners in their regulatory tasks. Further these agents self-regulate their own anthropomorphic traits, such as initiative and subject matter competency, in order to inspire a high level of trust, shared perspective, and entitativity - thus increasing the learners' willingness to view them as competent partners in the educational process.