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  1. null (Ed.)
    Artificial Intelligence (AI) techniques such as Generative Neural Networks (GNNs) have resulted in remarkable breakthroughs such as the generation of hyper-realistic images, 3D geometries, and textual data. This work investigates the ability of STEM learners and educators to decipher AI generated video in order to safeguard the public-availability of high-quality online STEM learning content. The COVID-19 pandemic has increased STEM learners’ reliance on online learning content. Consequently, safeguarding the veracity of STEM learning content is critical to ensuring the safety and trust that both STEM educators and learners have in publicly-available STEM learning content. In this study, state of the art AI algorithms are trained on a specific STEM context (e.g., climate change) using publicly-available data. STEM learners are then presented with AI-generated STEM learning content and asked to determine whether the AI-generated output is visually convincing (i.e., “looks real”) and whether the context being presented is plausible. Knowledge gained from this study will help enhance society’s understanding of AI algorithms, their ability to generate convincing video output, and the threat that those generated output have in potentially deceiving STEM learners who may be exposed to them during online learning activities. 
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  2. Abstract This work presents a deep reinforcement learning (DRL) approach for procedural content generation (PCG) to automatically generate three-dimensional (3D) virtual environments that users can interact with. The primary objective of PCG methods is to algorithmically generate new content in order to improve user experience. Researchers have started exploring the use of machine learning (ML) methods to generate content. However, these approaches frequently implement supervised ML algorithms that require initial datasets to train their generative models. In contrast, RL algorithms do not require training data to be collected a priori since they take advantage of simulation to train their models. Considering the advantages of RL algorithms, this work presents a method that generates new 3D virtual environments by training an RL agent using a 3D simulation platform. This work extends the authors’ previous work and presents the results of a case study that supports the capability of the proposed method to generate new 3D virtual environments. The ability to automatically generate new content has the potential to maintain users’ engagement in a wide variety of applications such as virtual reality applications for education and training, and engineering conceptual design. 
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  3. In this work, a Deep Reinforcement Learning (RL) approach is proposed for Procedural Content Generation (PCG) that seeks to automate the generation of multiple related virtual reality (VR) environments for enhanced personalized learning. This allows for the user to be exposed to multiple virtual scenarios that demonstrate a consistent theme, which is especially valuable in an educational context. RL approaches to PCG offer the advantage of not requiring training data, as opposed to other PCG approaches that employ supervised learning approaches. This work advances the state of the art in RL-based PCG by demonstrating the ability to generate a diversity of contexts in order to teach the same underlying concept. A case study is presented that demonstrates the feasibility of the proposed RL-based PCG method using examples of probability distributions in both manufacturing facility and grocery store virtual environments. The method demonstrated in this paper has the potential to enable the automatic generation of a variety of virtual environments that are connected by a common concept or theme. 
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  4. The objective of this work is to present an initial investigation of the impact the Connected Learning and Integrated Course Knowledge (CLICK) approach has had on students’ motivation, engineering identity, and learning outcomes. CLICK is an approach that leverages Virtual Reality (VR) technology to provide an integrative learning experience in the Industrial Engineering (IE) curriculum. To achieve this integration, the approach aims to leverage VR learning modules to simulate a variety of systems. The VR learning modules offer an immersive experience and provide the context for real-life applications. The virtual simulated system represents a theme to transfer the system concepts and knowledge across multiple IE courses as well as connect the experience with real-world applications. The CLICK approach has the combined effect of immersion and learning-by-doing benefits. In this work, VR learning modules are developed for a simulated manufacturing system. The modules teach the concepts of measures of location and dispersion, which are used in an introductory probability course within the IE curriculum. This work presents the initial results of comparing the motivation, engineering identity, and knowledge gain between a control and an intervention group (i.e., traditional vs. CLICK teaching groups). The CLICK approach group showed greater motivation compared to a traditional teaching group. However, there were no effects on engineering identity and knowledge gain. Nevertheless, it is hypothesized that the VR learning modules will have a positive impact on the students’ motivation, engineering identity, and knowledge gain over the long run and when used across the curriculum. Moreover, IE instructors interested in providing an immersive and integrative learning experience to their students could leverage the VR learning modules developed for this project. 
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  5. This work presents a Procedural Content Generation (PCG) method based on a Neural Network Reinforcement Learning (RL) approach that generates new environments for Virtual Reality (VR) learning applications. The primary objective of PCG methods is to algorithmically generate new content (e.g., environments, levels) in order to improve user experience. Researchers have started exploring the integration of Machine Learning (ML) algorithms into their PCG methods. These ML approaches help explore the design space and generate new content more efficiently. The capability to provide users with new content has great potential for learning applications. However, these ML algorithms require large datasets to train their generative models. In contrast, RL based methods do not require any training data to be collected a priori since they take advantage of simulation to train their models. Moreover, even though VR has become an emerging technology to engage users, there have been few studies that explore PCG for learning purposes and fewer in the context of VR. Considering these limitations, this work presents a method that generates new VR environments by training an RL in a simulation platform. This PCG method has the potential to maintain users’ engagement over time by presenting them with new environments in VR learning applications. 
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  6. This work introduces a new approach called Connected Learning and Integrated Course Knowledge (CLICK). CLICK is intended to provide an integrative learning experience by leveraging Virtual Reality (VR) technology to help provide a theme to connect and transfer the knowledge of engendering concepts. Integrative learning is described as the process of creating connections between concepts (i.e., skill and knowledge) from different resources and experiences, linking theory and practice, and using a variation of platforms to help students’ understanding. In the CLICK approach, the integration is achieved by VR learning modules that serve as a platform for a common theme and include various challenges and exercises from multiple courses across the IE curriculum. Moreover, the modules will provide an immersive and realistic experience, which the authors hypothesize, will improve how the students relate what they learn in a classroom, to real-life experiences. The goals of the CLICK approach are to (i) provide the needed connection between courses and improve students’ learning, and (ii) provide the needed linkage between theory and practice through a realistic representation of systems using VR. This work presents the results from an initial usability test performed on one of the VR modules. The results from the usability test indicate that participants liked the realism of the VR module. However, there are still some areas for improvement, and future work will focus on assessing the impact of the CLICK approach on students’ learning, motivation, and preparation to be successful engineers, areas which could translate to a STEM pipeline for the future workforce. 
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