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Manufacturing systems often face unexpected disruptions such as machine failures or material shortages, which can severely impact the production performance. Traditional methods for addressing these disruptions tend to be time-consuming and resource-intensive, which cannot effectively maintain the resilience of manufacturing systems. Despite recent advances in digital twins (DTs) and artificial intelligence (AI), very little has been done to mitigate high computational demands and generate resilient designs of manufacturing system under uncertainty. Therefore, this paper presents a new Generative Adversarial Network (GAN) approach for the on-the-fly design of manufacturing systems in response to production disruptions. First, we propose a novel Generative Adversarial Network (D-GAN) to generate diverse, adaptive system designs that align production performance with target key performance indicators (KPIs). Second, DT models are coupled with statistical metamodeling to sequentially generate large amounts of training data samples under different scenarios. Experimental results show the high potential of the proposed D-GAN approaches to generate cost-effective system designs and enhance manufacturing resilience. 

The rapid evolution of modern manufacturing systems is driven by the integration of emerging metaverse technologies such as artificial intelligence (AI), digital twin (DT), and different forms of extended reality (XR) like virtual reality (VR), augmented reality (AR), and mixed reality (MR). These advances confront manufacturing workers with complex and evolving environments that demand digital literacy for problem solving in the future workplace. However, manufacturing industry faces a critical shortage of skilled workforce with digital literacy in the world. Further, global pandemic has significantly changed how people work and collaborate digitally and remotely. There is an urgent need to rethink digital platformization and leverage emerging technologies to propel industrial evolution toward human-centered manufacturing metaverse (MfgVerse). This paper presents a forward-looking perspective on the development of MfgVerse, highlighting current efforts in learning factory, cognitive digital twinning, and the new sharing economy of manufacturing-as-a-service (MaaS). MfgVerse is converging into multiplex networks, including a social network of human stakeholders, an interconnected network of manufacturing things or agents (e.g., machines, robots, facilities, material handling systems), a network of digital twins of physical things, as well as auxiliary networks of sales, supply chain, logistics, and remanufacturing systems. We also showcase the design and development of a virtual learning factory for workforce training. Finally, future directions, challenges, and opportunities are discussed for human-centered manufacturing metaverse. We hope this work helps stimulate more comprehensive studies and in-depth research efforts to advance MfgVerse technologies. 

The approval of disease-modifying treatments for Alzheimer's disease demands a rethinking of cognitive screening. Drawing on over 180 stakeholder interviews from the NSF National I-Corps program, this perspective highlights barriers in current workflows, from time constraints in primary care to learning effects in long-term care, and presents innovation pathways centered on AI and digital biomarkers. Speech analysis, in particular, offers a scalable and cost-effective screening tool aligned with existing CPT codes. We outline implementation strategies and emphasize the urgent opportunity to align technological innovation with frontline clinical needs to ensure advances translate into meaningful patient and provider benefit. 

Modern manufacturing is increasingly challenged by larger product varieties, shorter product life cycles, and unexpected production disruptions. Examples of such disruptions include market uncertainty, machine failures, and delivery backlogs. These disturbances are intricately interrelated, exacerbating system complexity and necessitating the adaptive organization (or re-configuration) of machine networks within factory layouts. However, traditional factory layouts are often stationary and lack the flexibility to rearrange or adjust machine networks in response to volatile markets and unexpected disruptions. Also, layout planning typically emphasizes offline design and configuration of machine networks and resources within a facility to optimize process flow and production performance, but tends to overlook the self-organizing arrangement of machines in a dynamic environment. Therefore, to address this gap, this paper presents a novel Self-organizing Machine Network (SOMN) model that optimizes the spatial layout of machine positions and queue configurations, thereby enhancing the manufacturing system’s resilience to unexpected disruptions. First, as opposed to traditional fixed machine positions, we design intelligent machine agents that communicate and autonomously reorganize in real-time to optimize key performance indicators (KPIs). Second, we develop the machine network model in a Digital Twin (DT) environment, facilitating cyber-physical interactions and capturing variations of state-action space in machine agents. Third, multi-agent reinforcement learning (MARL) algorithms empower these networked machine agents to adapt layouts and minimize the impact of disruptions on production performance. We evaluate and validate the proposed SOMN model through computer experiments, benchmarking it against random search and simulated annealing approaches. Experimental results show that the SOMN model significantly improves material handling efficiency, reduces computational overhead, and maintains productivity in different scenarios of manufacturing disruptions. This research holds strong potential for enabling distributed intelligence within self-organizing machine networks for resilient manufacturing. 

Rapid advances in Digital Twin (DT) provide an unprecedented opportunity to derive data-enabled intelligence for smart manufacturing. However, traditional DT is more concerned about real-time data streaming, dashboard visualization, and predictive analytics, but focuses less on multi-agent intelligence. This limitation hampers the development of agentic intelligence for decentralized decision making in complex manufacturing environments. Therefore, this paper presents a Cognitive Digital Twin (CDT) approach for multi-objective production scheduling through decentralized, collaborative multi-agent learning. First, we propose to construct models of heterogeneous agents (e.g., machines, jobs, automated guided vehicles, and automated storage and retrieval systems) that interact with physical and digital twins. Second, multi-objective optimization is embedded in CDT to align production schedules with diverse and often conflicting objectives such as throughput, task transition efficiency, and workload balance. Third, we develop a multi-agent learning approach to enable decentralized decision making in response to unexpected disruptions and dynamic demands. Each agent operates independently and collaboratively with cognitive capabilities, including perception, learning, and reasoning, to optimize the individual agentic objective while contributing to overarching system-wide goals. Finally, the proposed CDT is evaluated and validated with experimental studies in a learning factory environment. Experimental results demonstrate that CDT improves operational performance in terms of task allocation, resource utilization, and system resilience compared to traditional centralized approaches. This initial study of CDT highlights the potential to bring multi-agent cognitive intelligence into next-generation smart manufacturing. 

As demand for data scientists has increased to inform decision-making across multiple fields of societal importance, postsecondary institutions have expanded data science course offerings. Despite such growth, educators struggle to teach students all the skills central to data science. They focus on programming and statistical tools and lack time for mentoring students in data storytelling. This working paper reviewed literature and interviewed experts to model the domain knowledge of data storytelling to inform the design of intelligent technology to support data storytelling instruction at scale. The paper closes with a recommendation of two ways that artificial intelligence tools can support the development of students’ data storytelling knowledge and skills: "direct" feedback to students on routine data science tasks and "facilitated" summaries of students' data story progress to inform instructors' feedback. We intend to apply these insights to the design of intelligent coaching in an online platform to support the development of storytelling competency at scale. 

The Computer Science Teachers Association (CSTA) K-12 Computer Science Standards identify ‘Algorithms and Programming’ as a key CS concept across all grade bands that encompasses sub-concepts such as algorithms, decomposition, variables, and control structures. Previous studies have shown that algorithms and programming concepts often pose challenges for novice programmers, and that instruction in these areas is often superficial. We developed formative assessment tasks to investigate middle school students’ understanding of four CS standards related to algorithms and programming and collected responses from over 100 students associated with five different teachers. We found that students demonstrated a limited understanding of the standards. These findings contribute to the growing literature on middle school students’ understanding of algorithms and programming, and provide insights that can inform CS teacher development, instruction, and curriculum design.