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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 safe internal transportation of hazardous materials within healthcare facilities is critical to mitigating risks to patients, staff, and visitors. This paper presents a risk-averse path planning framework for autonomously handling hazardous materials in healthcare systems. We model the indoor environment with grid-based obstacle and risk maps, where risk arises from pedestrian flow density and proximity to critical zones. Our novel risk-averse path planning approach integrates risk directly into each transition cost, thereby enabling more robust and secure path selection. We further improve efficiency through (i) a bidirectional variant that cuts search time and (ii) a post-optimization step that minimizes unnecessary heading changes while respecting a risk budget. We evaluated our framework on multiple simulated grid maps and compared it with established methods, measuring path length, average risk, and computational time. The results demonstrate that the proposed framework consistently generates safe and efficient paths while minimizing computational overhead. 

As manufacturing processes become increasingly complex, maintaining quality and improving efficiency requires mapping of process flows. Mapping process flows, in turn, depends on comprehensive end-to-end data traceability. Such traceability relies on lifecycle data that capture every stage, from raw-material handling to final-product assembly, and provide indispensable insights for process refinement. However, conventional centralized database-based systems for managing these data introduce single points of failure and remain vulnerable to tampering and cyberattacks. As a result, data traceability and authenticity are compromised. Therefore, this research develops a novel blockchain architecture coupled with digital twin (DT) model to secure end-to-end documentation of manufacturing process flows. First, a hierarchical blockchain framework is developed to record production events and ensure comprehensive, tamper-proof records of process activities. Second, the DT model, operating in collaboration with the blockchain tiers, enables real-time alignment between the manufacturing floor and its virtual twin. Third, a unified data representation is designed to transform diverse manufacturing datasets into a homogeneously structured format. Experimental results show that the proposed framework significantly enhances data authenticity while reducing the time required to map manufacturing process flows. 

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. 

The "Computer Science for All" initiative advocates for universal access to computer science (CS) instruction. A key strategy toward this end has been to establish CS content standards outlining what all students should have the opportunity to learn. Standards can support curriculum quality and access to quality CS instruction, but only if they are used to inform curriculum design and instructional practice. Professional learning offered to teachers of CS has typically focused on learning to implement a specific curriculum, rather than deepening understanding of CS concepts. We set out to develop a set of educative resources, formative assessment tools and teacher professional development (PD) sessions to support middle school CS teachers' knowledge of CS standards and standards-aligned formative assessment literacy. Our PD and associated resources focus on five CS standards in the Algorithm and Programming strand and are meant to support teachers using any CS curriculum or programming language. In this experience report, we share what we learned from implementing our standards-based PD with four middle school CS teachers. Teachers initially perceived standards as irrelevant to their teaching but they came to appreciate how a deeper understanding of CS concepts could enhance their instructional practice. Analysis of PD observations and exit surveys, teacher interviews, and teacher responses to a survey assessing CS pedagogical content knowledge demonstrated the complexity of using content standards as a driver of high-quality CS instruction at the middle school level, and reinforced our position that more standards-focused PD is needed. 

The FDA approval of disease-modifying Alzheimer's disease therapies marks a major shift in treatment but exposes a critical challenge: identifying patients during the mild cognitive impairment (MCI) stage when intervention is most effective. Despite early biological changes, most diagnoses occur after significant decline. Drawing from over 180 stakeholder interviews conducted through the NSF I-Corps program reveal major detection gaps across primary care, specialty access, and available tools. This commentary highlights the consequences of delayed diagnosis and proposes translational strategies to align early detection with therapeutic opportunity, positioning MCI as the critical window for Alzheimer's disease intervention.