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Early detection of cognitive impairment in assisted living is hindered by time-intensive tools like the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). We present a 60-second voice-based screening model that analyzes picture descriptions to estimate dementia risk. While recent deep learning approaches have shown promise on similar tasks, their lack of interpretability, large data requirements, and computational complexity limit clinical adoption. Using transcripts from the DementiaBank corpus, our model integrates traditional linguistic features (pause rate, pronoun use, syntactic complexity) with latent semantic dimensions extracted from language model embeddings. These semantic axes, interpretable constructs like “Drift & Hesitation” or “Over-detailed Narration”, consistently emerged as top predictors and may represent novel linguistic biomarkers of early decline. The final ElasticNet classifier is sparse, interpretable, and outperforms known non–deep learning baselines (AUC = 0.858), exceeding MMSE. Its simplicity enables deployment in mobile apps or in-room monitors, offering scalable, low-burden screening for early dementia. This work supports a shift toward linguistically grounded, tech-enabled cognitive care in aging populations. 

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. 

How can we best leverage higher education to address the pressing issues we face? While the SDGs reflect a global landscape dominated by complex, interconnected ‘wicked’ problems, Higher Education Institutions (HEIs) remain largely tethered to siloed, decontextualized instructional models. The inability of HEIs to shift practices to foster transversal, 21st-century skills necessary to navigate uncertain futures is a critical competency gap. This paper highlights the transformative potential of challenge-based learning (CBL) and place-based learning (PBL), high-impact experiential pedagogies, as critical tools for sustainability transformations. The Consortium for Adaptive Futures, an interdisciplinary and transnational community of practice, was launched in early 2025 to promote co-learning among faculty from North America and Europe for these practices. Drawing on insights from an inaugural symposium in Dublin, we identify three critical elements to HEI transformations that support sustainability and 21st-century skills to address grand challenges. These are: the importance of real-world relevance in education; the need for pedagogical shifts and faculty development to support these new roles; and the essential role of supportive institutional structures and resources to enable sustained engagement. In doing so, we issue a call to arms to HEIs and forward a reimagining of undergraduate education. 

As mixed reality (MR) technologies become increasingly integrated into real-world applications (e.g., training, healthcare, and manufacturing), accurately assessing cognitive workload is essential for maintaining performance and preventing mental overload. This study investigates the potential of heart rate variability (HRV), evaluated through recurrence quantification analysis (RQA), as a non-invasive, real-time indicator of cognitive workload in immersive mixed reality (MR) environments. A total of 103 participants performed a manufacturing assembly task in an MR environment while their physiological responses were recorded. Key RQA features such as recurrence rate (REC), determinism (DET), laminarity (LAM), and average diagonal length (ADL) showed significant correlations with self-reported workload metrics, including temporal demand, frustration, presence, and situational stress. These findings suggest that non-linear patterns in root mean square of successive differences (RMSSD) and standard deviation of normal-normal beats (SDNN), as quantified through RQA, reflect the dynamic interplay between cognitive and emotional states during complex MR tasks. This research contributes to the development of data-driven approaches for real-time cognitive workload assessment and highlights the value of integrating physiological monitoring into immersive systems to support adaptive human-machine interaction and personalized training. 

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. 

Abstract Dementia care is reaching a technological turning point as rapid advances in artificial intelligence (AI), digital biomarkers, and sensor‐based monitoring continue to outpace current clinical practice. Yet most tools remain episodic and disconnected from the everyday environments where cognitive change unfolds. Early cognitive drift often goes unnoticed, the mild cognitive impairment (MCI) window remains a major diagnostic gap, and care teams lack continuous, actionable insights. In this perspective, we introduce Agentic Neurotechnology, an integrated framework that combines adaptive multi‐agent AI systems with longitudinal cognitive modeling to support proactive and individualized dementia care. We define a five‐layer agentic stack consisting of (1) sensing and digital biomarker agents, (2) interpretation and reasoning agents, (3) longitudinal cognitive trajectory models, (4) care navigation agents, and (5) population‐level forecasting agents. We further outline the Cognitive Continuum Loop, a closed‐cycle process in which detection, interpretation, reasoning, monitoring, intervention, and adaptation occur continuously across the care environment. This framework highlights three actionable windows for cognitive intervention: the Preclinical Risk Detection window, the MCI Detection window, and the Functional Instability Response window. Each stage presents unique opportunities for agentic AI systems that operate continuously rather than through isolated assessments. We conclude by presenting a translational pathway for deploying agentic neurotechnologies across assisted living, primary care, telehealth, and home‐based settings. The goal is to enable earlier detection, greater clinical efficiency, and more personalized cognitive support. Agentic Neurotechnology reframes AI not as a passive screening tool but as an active partner in continuous cognitive care.