Self- and Co-Regulating Anthropomorphic Pedagogical Agents

想象一下，一个软件代理，它可以学习如何根据你自己的习惯和优势，并根据成千上万其他成功学生的学习分析模式，为你制定个性化的教育和培训策略。一个值得信赖的朋友，合作伙伴和盟友，可以帮助确定目标，并根据自己的学习风格，符合自己最深层次的价值观，个人，社会和其他标准，制定出实现这些目标的最可能的路径。本研究的目的是建立和测试一套这样的智能代理，称为拟人代理，模仿人类的特征，并将协助学习者在他们的监管任务。此外，这些代理人自我调节自己的拟人化特征，如主动性和主题能力，以激发高水平的信任，共享的观点和主体性-从而增加学习者在教育过程中将他们视为有能力的合作伙伴的意愿。研究表明，通过混合主动性的激励策略可以用来支撑学生的情感，认知，和能力的经验，以增加监管的成功。这是通过避免非生产性的心理状态和/或保持更有利于执行调节任务的状态来实现的。其他调解行动包括协助制定目标和查明长处和弱点，利用数据挖掘评估目标进展情况，以及选择最佳战略和适应计划，以最有可能激发所需变革和调整的优先格式和方式提出。目前的监管理论没有模拟拟人化代理人的影响。这项研究的结果将提供新的信息，填补现有知识体系中的这一空白。有真实的原因阻止人们享受调节的好处，包括工作记忆容量低，调节资源的压力或耗尽，执行功能障碍（例如，注意力缺陷多动障碍（ADHD），时间管理能力差，个性和气质，这些都可以通过拟人化软件代理有效地解决。

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.Research shows that motivational strategies through mixed-initiatives can be used to scaffold students'emotional, cognitive, and competence experiences so as to increase the success of regulation. This is accomplished by circumventing unproductive mental states and/or maintaining states that are more conducive for carrying out regulation tasks. Other mediative acts include assistance with goal formulation and identifying strengths and weaknesses, data-mining to evaluate goal-progress as well as selection of optimal strategies and plans for adaptation proffered in a preferred format and manner most likely to inspire the required changes and adjustments. Current theories of regulation do not model the impact of anthropomorphic agents. The results of this research will provide new information that fills this gap in the existing body of knowledge. There are real reasons that prevent people from enjoying the benefits of regulation that include low working memory capacity, stress or depletion of regulatory resources, disorders of executive function (e.g., ADHD), poor time management skills, personality and temperament, which can be effectively addressed with anthropomorphic software agents.