Collaborative Research: Advancing Quantum Education by Adaptively Addressing Misconceptions in Virtual Reality

量子信息科学（QIS）利用量子物理定律来处理和存储信息，预计将通过商业、治理、隐私、就业、教育和其他领域的新发展对社会产生广泛影响。然而，要取得这些进步，需要训练有素的 QIS 劳动力。不幸的是，QIS 是一个具有挑战性的跨学科领域。该项目的目标是通过使用虚拟现实 (VR) 和机器学习来自适应地解决对该领域的误解，从而推进 QIS 教育。该项目将直接影响大约 120 名学习 QIS 的本科生的教育，并有可能帮助改变如何激励学生并为未来的量子劳动力职位做好准备。该项目将利用 QubitVR，这是一款之前开发的 VR 应用程序，用于学习叠加、测量和纠缠等基础 QIS 概念。作为第一个目标，该项目将通过收集 QubitVR 受控、一般人群研究的数据来识别和预测 QIS 误解。这一目标将包括开发和验证用于评估学习成果的新 QIS 概念入门测试 (QISCIT)。它还将涉及在收集的数据中标记误解，以及基于 VR 跟踪和输入数据的机器学习模型的开发和系统评估，以预测 QubitVR 学习者何时可能产生误解。作为第二个目标，该项目将通过开发 QubitVR 的两个智能辅导版本来自适应地辅导 QIS 误解：一种采用基于机器学习模型的主动概念支架，另一种采用基于传统动作条件规则推理的反应支架。这一目标将涉及少数几项研究之一，通过在科目间、一般人群研究中比较两种版本，直接比较基于机器学习和基于规则的智能辅导方法。第三个目标是，该项目将通过纵向研究收集本科生 QIS 课程的控制、基线和自适应辅导数据，从生态角度验证 QubitVR 的功效。作为最终目标，该项目将开发桌面和智能手机版本的 QubitVR，该版本将与 VR 版本一起公开提供，以产生更广泛的教育影响，并推动超出该项目范围的 QIS 教育。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Quantum information science (QIS), which uses the laws of quantum physics to process and store information, is expected to broadly impact society through new developments in commerce, governance, privacy, employment, education, and other areas. However, a well-trained QIS workforce is necessary to make these advances. Unfortunately, QIS is a challenging, interdisciplinary field to learn. The goal of this project is to advance QIS education by using virtual reality (VR) and machine learning to adaptively address misconceptions about the field. The project will directly impact the education of approximately 120 undergraduate students learning QIS and has the potential to help transform how to motivate and prepare students for future quantum workforce positions.This project will leverage QubitVR, a VR application previously developed for learning foundational QIS concepts like superposition, measurement, and entanglement. As a first aim, the project will identify and predict QIS misconceptions by collecting data from a controlled, general-population study of QubitVR. This aim will include the development and validation of a new QIS Concept Introductory Test (QISCIT) for assessing learning outcomes. It will also involve labeling misconceptions in the collected data and the development and systematic evaluation of machine learning models based on VR tracking and input data for predicting when QubitVR learners are likely to have a misconception. As a second aim, the project will adaptively tutor QIS misconceptions by developing two intelligent tutoring versions of QubitVR: one that employs proactive conceptual scaffolds based on the machine learning models and one that employs reactive scaffolds based on conventional action-condition rules-based reasoning. This aim will involve one of few studies to directly compare machine learning-based and rules-based approaches to intelligent tutoring by comparing the two versions in a between-subject, general-population study. As a third aim, the project will ecologically validate the efficacy of QubitVR by collecting control, baseline, and adaptive tutoring data from undergraduate QIS courses in a longitudinal study. As a final aim, the project will result in the development of desktop and smartphone versions of QubitVR, which will be made openly available alongside the VR versions for broader educational impacts and to advance QIS education beyond the scope of this project.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.