SBIR Phase II: A virtual role-playing simulation for social emotional learning using artificially intelligent characters and crowdsourcing

SBIR第二阶段项目将开发新的3D网络和移动角色扮演模拟，以提高儿童的社交技能。虚拟角色参与与学生的微妙对话，为练习和评估沟通技巧提供转型体验。严格的评估已经证明了通过模拟角色扮演来教授换位思考和社交技能的可行性和有效性，对患有自闭症谱系障碍（ASD）、注意缺陷多动障碍（ADHD）或欺凌的学生有益。这些模拟将扰乱数十亿美元的社会情感学习（SEL）市场，让学生像在老师或治疗师那里一样学习和练习社交技能，但成本却很低。目前练习和评估社交技能的解决方案依赖于面对面的互动，这种方式既昂贵又难以规模化。由于复制人类语言的复杂性所面临的技术挑战，在此之前，制作丰富的、开放式的社交模拟是不可行的。该项目利用一项创新技术克服了这些障碍，该技术有可能通过大规模提供实时个性化反馈来彻底改变人们学习和实践社交技能的方式。基于该项目的社会行为捕捉（So-Cap）技术的未来应用范围从情境感知智能个人助理到社会感知机器人。该项目对开放式自然语言输入的真实响应能力前所未有，它依赖于一种创新的众包人工智能（AI）方法，该方法赋予机器理解上下文对话的能力，并参与涵盖多个主题的扩展对话。个性化的回应是根据用户的输入，从一个庞大的人类对话记录数据库中提取，从在线角色扮演中获取。从这些数据中挖掘有意义模式的过程也采用了众包，依靠在线雇佣的非专家来对单词、话语和事件进行聚类和注释。由这些模式形成的计算模型为实时对话引擎提供动力，该引擎通过结合包括计划识别和基于案例的计划在内的人工智能技术来选择响应。这项研究的目标是开创一个实用的，可重复的过程，使社会模拟的生产民主化，通过提供大规模的社会技能实践，最大限度地降低成本，同时最大限度地提高社会影响。

This SBIR Phase II project will develop novel 3D web and mobile role-playing simulations that can improve social skills in children. Virtual characters participate in nuanced conversations with students, powering a transformational experience for practicing and assessing communication skills. Rigorous evaluations have demonstrated the feasibility and efficacy of teaching perspective-taking and social skills through simulated role-playing, beneficial to students struggling with autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), or bullying. These simulations will disrupt the multi-billion dollar market for social emotional learning (SEL) by allowing students to learn and practice social skills as they would with teachers or therapists, but at a fraction of the cost. Current solutions for practicing and assessing social skills rely on face-to-face interaction, which is costly and difficult to scale. Producing socially rich, open-ended simulations has previously been infeasible, due to technical challenges required to replicate the complexity of human language. This project overcomes these barriers using an innovative technology with the potential to revolutionize how people learn and practice social skills by delivering real-time personalized feedback at scale. Future applications of the social behavior capture (So-Cap) technology that underlies this project range from contextually aware intelligent personal assistants to socially aware robotics. This project's unprecedented ability to respond authentically to open-ended natural language input relies on an innovative crowdsourced approach to artificial intelligence (AI), which imbues machines with the ability to understand dialogue in context and engage in extended conversations covering multiple topics. Individualized responses are tailored to user input by drawing from a massive database of recorded human dialogue, captured from online role-playing. The process for mining meaningful patterns from this data also employs crowdsourcing, relying on non-experts hired online to cluster and annotate words, utterances, and events. A computational model formed by these patterns powers a real-time conversational engine, which selects responses by combining AI techniques including plan recognition and case-based planning. The goal of this research is to pioneer a practical, repeatable process for democratizing the production of social simulations, minimizing cost while maximizing societal impact by providing social skills practice at scale.