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1. Enabling Intelligent Immersive Learning using Deep Learning-based Learner Confidence Estimation

   Lor, MA; Chen, SC; Shyu, ML; Tao, Y; Vassigh, S (August 2024, Institute of Electrical and Electronics Engineers (IEEE))

   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. 

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2. Enhancing Data Science Courses Pedagogy through GIFT-Enabled Adaptive Learning Pathways

   Anaroua, Fadjimata I; Li, Qing; Liu, Hong (May 2024, GIFTSymp 12)

   Sinatra, Anne; Goldberg, Benjamin (Ed.)

   Over the past decade, the educational landscape has experienced a surge of online learning and instruc-tional platforms (Liu et al., 2020). This remarkable surge can be attributed to a confluence of factors, including the rising demand for higher education opportunities, the shortage of available teaching staff, and the rapid advancements in information technology and artificial intelligence capabilities. Artificial Intelligence (AI) remained a niche area of research with limited practical applications in education for over half a century (Bhutoria, 2022; Chen et al., 2020; Roll & Wylie, 2016) from 1950 to 2010. Howev-er, in recent years, the advent of Big Data and advancements in computing power have propelled AI into the educational mainstream (Alam, 2021; Chen et al., 2020; Hwang et al., 2020). Today, the rise of machine learning, deep learning, automation, together with advances in big data analysis has sparked novel perspectives and explorations around the potential of enhancing personalized learning, a long-term educational vision of technology-enhanced course options to meet student needs (Grant & Basye, 2014). Fostering personalized learning necessitates the development of digital learning environments that dynamically adapt to individual learners' knowledge, prior experiences, and interests, while effectively and efficiently guiding them towards achieving desired learning outcomes (Spector, 2014, 2016). AI-powered technologies have made it possible to analyze data generated by learners and provide instruc-tion that matches their learning performance. Through learning analytics and data mining techniques, large datasets collected are analyzed and processed to uncover learners' unique learning characteristics, often referred to as learner profiling (Tzouveli et al., 2008). Subsequently, leveraging artificial intelli-gence algorithms, the learning content is tailored, and personalized learning paths are designed to align with each learner's identified needs and preferences, thereby facilitating personalized learning experienc-es. 

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3. Performance-Driven VR Learning for Robotics

   Vassigh, Shahin; Bogosian, Biayna; Peterson, Eric (January 2024, Springer)

   Yan, C; Chai, H; Sun, T; Yuan, PF (Ed.)

   Abstract. The building industry is facing environmental, technological, and economic challenges, placing significant pressure on preparing the workforce for Industry 4.0 needs. The fields of Architecture, Engineering, and Construction (AEC) are being reshaped by robotics technologies which demand new skills and creating disruptive change to job markets. Addressing the learning needs of AEC students, professionals, and industry workers is critical to ensuring the competitiveness of the future workforce. In recent years advancements in Information Technology, Augmented Reality (AR), Virtual Reality (VR), and Artificial Intelligence (AI) have led to new research and theories on virtual learning environments. In the AEC fields researchers are beginning to rethink current robotics training to counteract costly and resource-intensive in-person learning. However, much of this work has been focused on simulation physics and has yet to adequately address how to engage AEC learners with different learning abilities, styles, and diverse backgrounds.This paper presents the advantages and difficulties associated with using new technologies to develop virtual reality (VR) learning games for robotics. It describes an ongoing project for creating performance driven curriculum. Drawing on the Constructivist Learning Theory, the affordances of Adaptive Learning Systems, and data collection methods from the VR game environment, the project provides a customized and performance-oriented approach to carrying out practical robotics tasks in real-world scenarios. 

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4. Robotics, Personalized Learning, Virtual Reality, Augmented Reality, Diversity, and Inclusion

   Bogosian, B; Peterson, E; Vassigh, S (August 2025, AMPS Architecture, media, politics, society proceedings series)

   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. 

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5. Combining Immersive Technologies and Problem-Based Learning in Engineering Education: Bibliometric Analysis and Literature Review

   Nowparvar, Mahgol; Chen, Xing; Ashour, Omar; Ozden, Sabahattin G; Negahban, Ashkan (January 2021, ASEE annual conference)

   null (Ed.)

   There is a cohesive body of research on the effectiveness of problem-based learning (PBL) for a wide range of learner groups across different disciplines in engineering education. On the other hand, there is a growing interest in using immersive technologies such as virtual reality (VR) in engineering education. While there are many literature review articles on each of these subjects separately, there is a lack of review articles on the application of combined PBL-VR learning environments in engineering education. This paper provides an assessment of the applications and potential of implementing immersive technologies in a PBL setting to utilize the advantages of both paradigms. More specifically, this paper aims to provide insights related to two main questions: (1) where (in what disciplines/subjects) PBL and VR have been used together in engineering education? And, (2) how are VR and PBL integrated and used in engineering education? The first question is investigated by performing a bibliometric analysis of relevant papers published in the proceedings of previous ASEE annual conferences. The second question is explored by performing a literature review and classification of ASEE papers that discuss the use of VR in conjunction with PBL. Our findings reveal a gap between the application of integrated PBL and VR across different disciplines in engineering education. We also analyze the trends related to PBL and VR application in engineering education over time. Finally, we identify and propose future opportunities related to the combination of PBL and immersive technologies, including but not limited to immersive simulation-based learning (ISBL) and incorporating artificial intelligence (AI) into immersive virtual/simulated learning environments used in engineering education. 

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