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A convergence of technology advancements including spatial computing, augmented reality (AR), and artificial intelligence (AI) can now support the personalization of learning environments and dynamically respond to learner performance data with personalized feedback. Augmented Learning for Environmental Robotics (ALERT), leverages advances in technology to research, develop, and test an augmented reality-enhanced (AR) curriculum for learning how to develop and use robotic environmental monitoring tools for collecting data on environmentally sensitive construction sites. With this project, our research team aims to develop the ALERT curriculum as an immersive learning environment, implement automation processes that dynamically adjust to learner performance, and address a pressing problem in the construction sector with recent advances in small robotics and remote sensing. 

This paper introduces the Intelligent Learning Platform for Robotics Operations (IL-PRO), a Virtual Reality (VR) system designed to enhance robotics training in the Architecture, Engineering, and Construction (AEC) industry. IL-PRO addresses the growing need for effective training methods as the AEC sector adopts robotic automation. The system integrates VR technology with game-assisted learning, combining online multimedia lessons for theory with immersive VR tasks for practical skills. Developed iteratively using Design-Based Research principles, IL-PRO incorporates realistic robot simulations and progressive task complexity. The VR environment, built in Unity, aims to enhance engagement, motor coordination, and spatial awareness in robotics training. While future goals include AI-driven personalized instruction, this work-in-progress focuses on VR curriculum development and implementation. The paper concludes by discussing future directions, including curriculum expansion and cross-institutional adoption, to establish new benchmarks in innovative robotics education for the AEC industry. 

Harnessing AI for enhanced learning: Insights from the robotics academyHow technology is tailoring personalised learning experiences for the AEC sector. Personalised learning, tailoring learning content and sequence for differences in ability, experience, and sociocultural backgrounds hold the promise to transform education. This transformation is propelled by three significant advancements in emerging technologies, each vital in realising personalised learning. The first of these advancements is in learning analytics, defined as the measurement, collection, analysis, and reporting of learner data (Siemens, 2013). Enhanced by AI and data mining techniques, learning analytics significantly deepens our understanding of learning processes by systematically monitoring learners’ performance and actions. This involves analyzing extensive datasets from learner interactions to uncover patterns, challenges, and cognitive load, providing a comprehensive view of the learning experience. 

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. 

The increasing environmental concerns call for more sophisticated and integrated educational methods. For sustainable outcomes, understanding and navigating complex environmental factors is essential. By imparting knowledge about environmental data and its applications, students can be better prepared to address environmental issues.The Augmented Learning for Environmental Robotics Technologies (ALERT) program introduces an educational method using augmented reality (AR) and artificial intelligence (AI). It provides students, particularly those in architecture, engineering, and construction (AEC), with an immersive learning experience focused on environmental data and robotics. Considering the significant environmental footprint of the AEC sector—emanating from energy-intensive buildings, roads, and infrastructures—the ALERT initiative strives to instill a comprehensive understanding of environmental data collection and visualization. This is done with the aim of promoting data-centric design and construction for a more eco-friendly built environment.In the ALERT program, AR is employed to fashion an augmented learning space where students can engage with both real-time and past environmental data. They learn to set up environmental sensors, collect data, and visualize it to unearth hidden trends and connections. Additionally, AI ensures a tailored learning journey for each student, offering optimal challenges and support. This innovative blend of AR and AI not only offers an enriching learning experience but also prepares AEC students to be at the forefront of transformative shifts, especially those influenced by advancements like robotic automation, fostering a profound understanding of environmental data.This paper outlines the preliminary stages of the ALERT project, detailing its foundational research. Topics include the educational theories guiding the creation of a groundbreaking Intelligent Learning System (ILS) and curriculum, as well as the projected impact of the program. ALERT emerges as a promising venture, potentially empowering students with the expertise to reduce the ecological footprint of infrastructure, paving the way for a greener future. 

AI, robotics, and automation are reshaping many industries, including the Architecture, Engineering, and Construction (AEC) industries. For students aiming to enter these evolving fields, comprehensive and accessible training in high-tech roles is becoming increasingly important. Traditional robotics education, while often effective, usually necessitates small class sizes and specialized equipment. On-the-job training introduces safety risks, particularly for inexperienced individuals. The integration of advanced technologies for training presents an alternative that reduces the need for extensive physical resources and minimizes safety concerns. This paper introduces the Intelligent Learning Platform for Robotics Operations (IL-PRO), an innovative project that integrates the use of Artificial Intelligence (AI), Virtual Reality (VR), and game-assisted learning for teaching robotic arms operations. The goal of this project is to address the limitations of traditional training through the implementation of personalized learning strategies supported by Adaptive Learning Systems (ALS). These systems hold the potential to transform education by customizing content to cater to various levels of understanding, preferred learning styles, past experiences, and diverse linguistic and socio-cultural backgrounds.Central to IL-PRO is the development of its ALS, which uses student progress variables and multimodal machine learning to infer students’ level of understanding and automate task and feedback delivery. The curriculum is organized into modules, starting with fundamental robotic concepts, and advancing to complex motion planning and programming. The curriculum is guided by a learner model that is continuously refined through data collection. Furthermore, the project incorporates gaming elements into its VR learning approach to create an engaging educational environment. Thus, the learning content is designed to engage students with simulated robots and input devices to solve sequences of game-based challenges. The challenge sequences are designed similarly to levels in a game, each with increasing complexity, in order to systematically incrementally build students' knowledge, skills, and confidence in robotic operations. The project is conducted by a team of interdisciplinary faculty from Florida International University (FIU), the University of California Irvine (UCI), the University of Hawaii (UH) and the University of Kansas-Missouri (UKM). The collaboration between these institutions enables the sharing of resources and expertise that are essential for the development of this comprehensive learning platform. 

Abstract—Periods of unique economic distress such as the COVID-19 pandemic can be quite difficult for small businesses. Challenges acquiring the supplies necessary to adhere to safety regulations created in the wake of such events can introduce stress on these businesses. This is further exacerbated when supply chains have slowed down, leading to global shortages from most large suppliers. This paper proposes a platform to aid such businesses in procuring COVID-19 related supplies such as Personal Protective Equipment (PPE) from one another, leveraging advanced data acquisition, integration, and Natural Language Processing (NLP) methods. With the pandemic end in sight, the platform described in this paper can be reused for other emergencies such as hurricanes, floods, among others. The proposed platform supports business transactions within a Buyer’s Club (BC), keyword-based sourcing of new businesses to join the platform, and matching products to relevant regulations using greater-than-word length encoding, helping businesses comply with the ever-changing regulatory landscape. Index Terms—COVID-19, Disaster, Natural Language Processing, Data Acquisition, Data Retrieval, User Interfaces 

Immersive Learning Environments (ILEs) developed in Virtual and Augmented Reality (VR/AR) are a novel pro- fessional training platform. An ILE can facilitate an Adaptive Learning System (ALS), which has proven beneficial to the learning process. However, there is no existing AI-ready ILE that facilitates collecting multimedia multimodal data from the environment and users for training AI models, nor allows for the learning contents and complex learning process to be dynamically adapted by an ALS. This paper proposes a novel multimedia system in VR/AR to dynamically build ILEs for a wide range of use-cases, based on a description language for the generalizable ILE structure. It will detail users’ paths and conditions for completing learning activities, and a content adaptation algorithm to update the ILE at runtime. Human and AI systems can customize the environment based on user learning metrics. Results show that this framework is efficient and low- overhead, suggesting a path to simplifying and democratizing the ILE development without introducing bloat. Index Terms—virtual reality, augmented reality, content generation, immersive learning, 3D environments 

Advancements in Artificial Intelligence (AI), Information Technology, Augmented Reality (AR) and Virtual Reality (VR), and Robotic Automation is transforming jobs in the Architecture, Engineering and Construction (AEC) industries. However, it is also expected that these technologies will lead to job displacement, alter skill profiles for existing jobs, and change how people work. Therefore, preparing the workforce for an economy defined by these technologies is imperative. This ongoing research focuses on developing an immersive learning training curriculum to prepare the future workforce of the building industry. In this paper we are demonstrating a prototype of a mobile AR application to deliver lessons for training in robotic automation for construction industry workers. The application allows a user to interact with a virtual robot manipulator to learn its basic operations. The goal is to evaluate the effectiveness of the AR application by gauging participants' performance using pre and post surveys.