Learning Mathematical Concepts and Computational Thinking through Explainable Artificial Intelligence in a Simulation-based Learning Environment

由于计算在STEM学科中的强大创新和应用，STEM+C项目支持跨学科和跨学科方法的研究和开发，以便在正式和非正式环境中为pre -12学生整合STEM教学和学习中的计算。该项目将推动数学教育在人工智能（AI）和高中生计算思维技能背景下的整合。人工智能在我们的日常生活中无处不在。从虚拟助理到自动驾驶汽车，从诊断疾病到建造房屋，今天的许多学生将继续在涉及人工智能或受人工智能影响的领域工作。精通人工智能的语言是劳动力能够继续创新和支持人工智能技术基础设施和生态系统的关键。人工智能建立在数学的基础上。通过说明数学概念如何用于强大的人工智能工具来解决计算问题，通过人工智能和计算学习数学可以成为一种激励和教育工具，说明从K-12 STEM教育到中学后STEM教育，再到STEM职业的途径。如今，大多数人工智能决策过程对非人工智能专家来说都是一个“黑匣子”，甚至对一些人工智能专家来说也是如此。可解释人工智能是一种新兴的智能人机界面，它使人工智能算法的决策对用户透明，最近在可解释人工智能方面的进展创造了一个机会，让高中生也能接触到人工智能机器学习的概念。这个提议的项目在基于模拟的学习环境中使用可解释的人工智能，学生跟随引导的人机团队探索，学习如何创建问题的抽象，利用人工智能算法自动化解决方案生成过程，分析结果，然后提高解决方案的性能。研究人员将迭代地引入挑战问题和场景，鼓励学生修改机器人的决策，学生诊断，修改，测试和分析，并为机器人创造新的能力。拟议中的项目与弗吉尼亚州和加利福尼亚州的三所高中合作。综合学习内容将与高中教师一起开发，重点是高中数学，人工智能和计算思维技能之间的联系。该项目旨在回答一个问题，即交互式可解释的人工智能设计选择和解释在多大程度上有助于高中学习者在使用-创建-修改框架中理解数学和人工智能。研究将涉及(1)评估使用-创建-修改方法及其对计算思维和人工智能使用的自我效能的影响，(2)评估对数学和人工智能的理解，以及促进学习的不同解释用途，以及(3)享受和参与人机模拟。在这种人机互动的学习环境中，制定能够在多大程度上构建高中水平的数学学习及其与计算思维的整合。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

As a result of the powerful innovation and application of computing in STEM disciplines, the STEM+C program supports research and development of interdisciplinary and transdisciplinary approaches to the integration of computing within STEM teaching and learning for preK-12 students in both formal and informal settings. This project will advance the integration of the education of math within the context of artificial intelligence (AI) and computational thinking skills for high school students. AI has become ubiquitous in our everyday lives. From virtual assistants, to self-driving cars, from diagnosing disease, to building houses, many of today's students will go on to work in fields that involve or are influenced by AI. Being proficient in the language of AI is key to a workforce that will be able to continue to innovate and to support the AI-powered technology infrastructure and eco-system. AI builds on the foundation of mathematics. By illustrating how math concepts can be used in powerful AI tools to solve computational problems, learning math through AI and computation can be a motivational and educational vehicle to illustrate the pathway from K-12 STEM education, to post-secondary STEM education, and later to STEM careers. Most of the AI decision-making process today is a "black box" to non-AI experts, and even to some AI experts. Recent advances in explainable AI, an emerging intelligent human-computer interface that makes the decision-making of AI algorithms transparent to users, creates an opportunity to make AI machine learning concepts accessible to high school students. This proposed project employs explainable AI within a simulation-based learning environment where students follow guided human-robot team explorations to learn how to create abstractions of a problem, utilize AI algorithms to automate the process of solution generation, analyze the outcome, and then improve the performance of their solution. Researchers will iteratively introduce challenge problems and scenarios to encourage students to modify the robot's decision-making where students diagnose, revise, test and analyze, and create new capabilities for the robot. The proposed project partners with three high schools from Virginia and California. The integrated learning content will be developed with high school teachers, focusing on the connections between high school math, AI and computational thinking skills. The proposed project aims to answer the question of to what extent interactive explainable AI design choices and explanations contribute to understanding of math and AI in a use-create-modify framework for high school learners. Research studies will address (1) assessment of the use-create-modify approach and its impact on self-efficacy for computational thinking and use of AI, (2) assessment of understanding of math and AI, and the different uses of explanations to promote learning, and (3) enjoyment and engagement with the human-robot simulation, and to what extent enactment in this human-technology interactive learning environment is able to frame high school level learning of math and its integration with computational thinking.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.