CRII: IIS: RUI: Understanding Learning Analytics Algorithms in Teacher and Student Decision-making

这个项目探讨了教师和学生对支撑其教育技术的人工智能(AI)算法的理解之间的关系，以及这种算法理解对学习决策的影响。这项研究将涉及与人们的研究，以调查算法理解如何影响系统信任和学习决策，以及开发“可解释的”或简短的互动辅导系统，以向课堂利益相关者提供算法理解。这两项努力将为算法增强型学习环境的设计者提供一个框架，以确定实现其系统用户知情决策的目标所需的算法理解水平。该项目开发的解释程序将向公众开放，并可供外部项目使用，从而增加最初计划的利益相关者以及普通公众对算法的理解。这项工作的主要贡献包括对学习情境算法理解的知识组成部分进行了方法论上的严格调查，这些知识成分可用于在更广泛的机器学习社区中建立可解释性讨论的模型。研究涉及系统地识别符合条件的概念，针对这些概念构建简短的交互式教学系统，以及观察系统信任和学习情境决策的结果变化。它结合了学习科学、人机交互、伦理学和机器学习的方法。学生研究人员将执行认知任务分析，以确定人工智能模型的专家理解的分层模型，应用以用户为中心的设计过程来开发可解释程序，以教授不同级别的专家理解，并进行评估研究，比较各种可解释程序对算法理解、信任和决策的影响。这一结果将增加更大的机器学习社区正在进行的关于伦理算法透明度的讨论，但也提供了一个可操作的框架，用于发展一个更了解人工智能的学生和教师群体，以及用于附加到外部算法增强的学习环境的轻量级解释。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project explores the relationship between instructor and student understanding of the artificial intelligence (AI) algorithms that underlay their educational technology, and the impact of that algorithmic understanding on decision-making for learning. The research will involve studies with people to investigate how algorithmic understanding impacts system trust and decision-making for learning, as well as the development of "explainables" or brief, engaging interactive tutoring systems to provide algorithmic understanding to classroom stakeholders. These two thrusts will yield a framework for designers of algorithmically enhanced learning environments to determine what level of algorithmic understanding is necessary to achieve the goals of informed decision-making by users of their systems. The explainables developed by this project will be publicly accessible and usable by external projects, increasing algorithmic understanding for the initially intended stakeholders, but also for the general public. The main contributions of this work include a methodologically rigorous investigation of the knowledge components of algorithmic understanding for learning contexts that can be applied to model interpretability discussions in the wider machine learning community.The research involves systematically identifying the concepts that qualify as "understanding" an AI algorithm, building brief interactive tutoring systems to target those concepts, and observing resultant changes in system trust and decision-making for learning contexts. It combines approaches from the learning sciences, human-computer interaction, ethics, and machine learning. Student researchers will perform cognitive task analyses to identify hierarchical models of expert comprehension of AI models, apply a user-centered design process to develop explainables to teach the varying levels of expert comprehension, and perform evaluation studies comparing various explainables' impact on algorithmic understanding, trust, and decision making. The results will add to ongoing discussions about ethical algorithmic transparency in the larger machine learning community, but also provide an actionable framework for developing a more AI-informed student and teacher body as well as lightweight explainables for appending to external algorithmically enhanced learning environments.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.