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，训练有素的QIS员工队伍是实现这些进步的必要条件。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有关该领域的误解，从而推进QIS教育。该项目将直接影响大约120名学习量子信息系统的本科生的教育，并有可能帮助改变如何激励和培养未来量子劳动力岗位的学生。该项目将利用QubitVR，这是一款之前开发的VR应用程序，用于学习基础QIS概念，如叠加、测量和纠缠。作为第一个目标，该项目将通过收集来自QubitVR的受控一般人群研究的数据来识别和预测QIS的误解。这一目标将包括开发和验证新的QIS概念入门测试（QISCIT），以评估学习成果。它还将涉及标记收集数据中的误解，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学习者何时可能有误解。作为第二个目标，该项目将通过开发两个智能辅导版本的QubitVR来自适应地指导QIS误解：一个采用基于机器学习模型的主动概念支架，另一个采用基于传统动作条件规则推理的反应性支架。这一目标将涉及为数不多的研究之一，通过在主题之间的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。作为第三个目标，该项目将在纵向研究中收集本科生QIS课程的对照、基线和适应性辅导数据，从生态角度验证QubitVR的有效性。作为最终目标，该项目将开发桌面和智能手机版本的QubitVR，这些版本将与VR版本一起公开提供，以获得更广泛的教育影响，并推动QIS教育超出本项目的范围。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。

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.