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

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.