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This project addresses the urgent need for inclusive and scalable robotics training in architecture, engineering, and construction (AEC) through the integration of artificial intelligence (AI) and extended reality (XR) technologies. In collaboration with three Minority Serving Institutions (Florida International University, Arizona State University, and University of Hawai‘i at Mānoa), we developed and tested immersive, adaptive learning environments that personalize robotics education for diverse student populations. These efforts include a VR-based curriculum for industrial robotics, an AR curriculum for environmental sensing technologies, and an overarching Robotics Academy framework that promotes open knowledge exchange and workforce connectivity. By combining real-time performance analytics, natural language processing, and biometric inputs, our systems support individualized learning paths and help mitigate algorithmic bias. This research advances equitable access to robotics education and provides a replicable model for technology-driven workforce development in the AEC sector. Ongoing evaluation demonstrates improved learner engagement, accessibility, and cross-platform skill transferability. 

Peterson, E., Bogosian, B., Vassigh, S. (2025). T. In Proc. 113th ACSA Annual Meeting (pp. ##-##). In press. 

Personalized learning, which customizes content and instructional sequences to account for differences in ability, experience, and sociocultural backgrounds, holds great promise for transforming education. This transformation is increasingly driven by significant advancements in Artificial Intelligence (AI). AI enables detailed analysis and reporting of learners' performance data, paving the way for the development of intelligent adaptive learning systems that offer personalized feedback aligned with each learner’s unique needs and progress. In parallel, immersive technologies are playing a pivotal role in enhancing educational experiences. Technologies such as Virtual Reality (VR) and Augmented Reality (AR) create engaging, interactive environments that deepen learners' understanding and retention of complex concepts. Dr. Vassigh's presentation explores the integration of AI and VR in education, illustrated through a case study from an ongoing project. The talk will highlight the refinement of learning processes through these technologies and demonstrate how they can impact learner engagement and performance. 

In today’s world, augmented reality and virtual reality (AR/VR) technologies have become more accessible to the public than ever. This brings the possibility of immersive learning to the forefront of education for future generations. However, there is still much to discover and improve in using these technologies to analyze and understand learning. This paper explores the utilization of data captured through AR/VR headsets during an immersive training program for industrial robotics. This includes data on time spent, eye gaze, and hand movement during a range of activities to track a learner’s understanding of the content and intelligently estimate learner confidence within these environments using deep learning. Leveraging a dataset that comprises responses and confidence levels from 10 individuals across 35 questions, we aim to improve the uses and applicability of confidence estimation. We explore the possibility of training a model using learners’ data to dynamically fine-tune lessons and activities for each individual, thereby improving performance. We demonstrate that a pre-trained compact LSTM classification model can be fine-tuned with relatively small data, for enhanced performance on an individual basis for better personalized learning.