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、教育等领域的新发展来广泛影响社会。然而，要取得这些进展，训练有素的合格投资者队伍是必要的。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)和机器学习来促进QIS教育，以自适应地解决对该领域的误解。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，并有可能帮助转变如何激励学生并为未来的量子劳动力职位做准备。该项目将利用QubitVR，这是一款以前开发的VR应用程序，用于学习基本的量子信息系统概念，如叠加、测量和纠缠。作为第一个目标，该项目将通过从QubitVR的受控、一般人群研究中收集数据来识别和预测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)，以评估学习结果。它还将涉及标记收集的数据中的错误概念，以及基于VR跟踪和输入数据开发和系统评估机器学习模型，以预测QubitVR学习者何时可能有错误概念。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自适应地指导QIS的误解：一个采用基于机器学习模型的主动式概念支架，另一个采用基于传统动作条件规则推理的反应式支架。这一目标将涉及少数几项直接比较基于机器学习和基于规则的智能辅导方法的研究之一，方法是在受试者之间的一般人群研究中比较这两个版本。作为第三个目标，该项目将通过在一项纵向研究中收集本科生QIS课程的控制、基线和适应性辅导数据，从生态上验证QubitVR的有效性。作为最终目标，该项目将导致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，以实现更广泛的教育影响，并推动QIS教育超出本项目的范围。该奖项反映了NSF的法定使命，并已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。

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.