Collaborative Research: Intelligent Immersive Environments for Learning Robotics

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The global economy is being rapidly reshaped by sophisticated robots that enhance human dexterity, visual perception, speed, and strength. This intense focus on creating and implementing new automation technologies is bringing disruptive changes to job markets. In Architecture, Engineering, and Construction (AEC) industries, robotics automation is transforming jobs at a speed and scale never experienced before, leading to new demand for skilled workers in advanced technologies and robotics. Addressing the learning needs of AEC students, future professionals, and industry workers is critical for ensuring the competitiveness of a large proportion of the US workforce. Our proposal is inspired by recent technological achievements in self-adaptive, data-driven, and autonomous systems for virtual learning. These technologies bear the promise to transform education by personalization and tailoring the learning content and sequence for differences in ability, experience, and sociocultural background. Leveraging these technologies, we will research, develop, and test a personalized learning tool for delivering an industrial robotics curriculum to prepare the next generation of the AEC workforce. We plan to achieve this goal with five educational and scientific innovations: 1) Artificial Intelligence (AI)-assisted Adaptive Intelligent Learning System 2) AI-assisted coaching, 3) Novel curriculum content and delivery in virtual reality, 4) Game-based learning user experience, and user interface and 5) AI-enabled learning analytics. The design and implementation of this project will contribute to technological advancement in AI-assisted Adaptive Intelligent Learning systems and our ability to apply state-of-the-art AI and Natural Language Processing techniques for the analysis of learning data. Advancing this frontier is critical for our ability to evaluate learner data at scale. Further, our development of AI-Assisted coaching will lead to broadly applicable advancements in intelligent tutoring systems. It includes a novel capacity to detect and identify learner failure patterns and to apply known remediation to improve learning outcomes. In addition, the design and implementation of a curriculum that dynamically changes in response to learner input, skill level, and advancement toward learning goals can bring new pedagogical approaches to curriculum development, reshaping our current practices. Finally, our project will enrich learning analytics by integrating biometric and performance data leading to a greater understanding of the learning process.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.