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The global health crisis represents an unprecedented opportunity for the development of artificial intelligence (AI) solutions. This article aims to tackle part of the biases in artificial intelligence by implementing a human-centric AI to help decision-makers in organizations. It relies on the results of two design science research (DSR) projects: SCHOPPER and VRAILEXIA. These two design projects operationalize the human-centric AI approach with two complementary stages: 1) the first installs a human-in-loop informed design process, and 2) the second implements a usage architecture that aggregates AI and humans. The proposed framework offers many advantages such as permitting to integrate of human knowledge into the design and training of the AI, providing humans with an understandable explanation of their predictions, and driving the advent of augmented intelligence that can turn algorithms into a powerful counterweight to human decision-making errors and humans as a counterweight to AI biases.

Statistical learners are leading towards auto-epistemic logic, but is it the right way to progress in artificial intelligence (AI)? Ways to discover AI fit the senses and the intellect. The structure of symbols–the operations by which the intellectual solution is realized–and the search for strategic reference points evoke essential issues in the analysis of AI. Studying how knowledge can be represented through methods of theoretical generalization and empirical observation is only the latest step in a long process of evolution. In this paper, we try to outline the origin of knowledge and how modern artificial minds have inherited it.

Artificial intelligence (AI) has increased the ability of organizations to accumulate tacit and explicit knowledge to inform management decision-making. Despite the hype and popularity of AI, there is a noticeable scarcity of research focusing on AI's potential role in enriching and augmenting organizational knowledge. This paper develops a recursive theory of knowledge augmentation in organizations (the KAM model) based on a synthesis of extant literature and a four-year revised canonical action research project. The project aimed to design and implement a human-centric AI (called Project) to solve the lack of integration of tacit and explicit knowledge in a scientific research center (SRC). To explore the patterns of knowledge augmentation in organizations, this study extends Nonaka's SECI (socialization, externalization, combination, and internalization) model by incorporating the human-in-the-loop Informed Artificial Intelligence (IAI) approach. The proposed design offers the possibility to integrate experts' intuition and domain knowledge in AI in an explainable way. The findings show that organizational knowledge can be augmented through a recursive process enabled by the design and implementation of human-in-the-loop IAI. The study has important implications for research and practice.

The implementation of Digital Twins (DTs) in hospitality facilities represents a significant opportunity to optimize front-end services, enhancing guest experience and operational efficiency. This paper proposes an ontology-driven approach for DTs in hotel reception areas, focusing on integrating IoT devices, real-time data processing, and service optimization. By modeling interactions between guests, receptionists, and hotel management systems, DTs enhance resource allocation, predictive maintenance, and customer satisfaction. Simulations and historical data analysis enable forecasting demand fluctuations and optimizing check-in/check-out processes. This research provides a structured framework for DT applications in hospitality, validated through scenario-based simulations, showing significant improvements in check-in time and guest satisfaction. Validation was conducted through scenario-based simulations reflecting real-world operational challenges, such as guest surges, room assignment, and staff workload balancing. Metrics including check-in time, guest satisfaction index, task completion rates, and prediction accuracy were used to evaluate performance. Simulations were grounded in historical hotel data and modeled typical peak-period dynamics to ensure realism. Results demonstrated a 25–35% reduction in check-in time, a 20% improvement in staff efficiency, and significant enhancements in guest satisfaction, underscoring the practical value of the proposed framework in real hospitality settings.

This empirical study looks at how the industrial sector is affected by the deployment of human-centric AI and finds some amazing changes in the workplace. Following implementation, employee productivity increased by 35.5%, demonstrating the significant advantages of AI in automating repetitive jobs and improving overall efficiency. Simultaneously, job satisfaction increased by a significant 20.6%, highlighting the alignment of AI with worker well-being. Employee skill development increased by 29.6% as a result of structured AI training, which is consistent with the larger goals of adopting AI that is human-centric. Significant cost reductions of up to 40% of budgets were also realized by departments, resulting in significant economic benefits. These revelations highlight the revolutionary potential of AI integration in Industry 5.0, promoting a harmonic convergence of intelligent technology and human skills for an industrial future that is more productive, happy, and financially stable.

This systematic review analyses advancements in cognitive state recognition from 2010 to early 2024, evaluating 405 relevant articles from an initial pool of 2398 records identified through five databases: Scopus, Engineering Village, Web of Science, IEEE Xplore, and PubMed. Studies were included if they assessed cognitive states using physiological signals and applied machine learning (ML) or deep learning (DL) techniques in practical task settings. The review highlights a pivotal shift from shallow ML to DL approaches for analysing physiological signals, driven by DL’s ability to autonomously learn complex patterns in large datasets. By 2023, DL has become the dominant methodology, though traditional ML techniques remain relevant. Additionally, there has been a move from neuroimaging to multimodal physiological modalities, with the decrease in neuroimaging use reflecting a trend towards integrating various physiological signals for more comprehensive insights. Cognitive state recognition is applied across diverse domains such as the automotive, aviation, maritime, and healthcare industries, enhancing performance and safety in high-stakes environments. Electrocardiogram (ECG) is the most utilised modality, with convolutional neural networks (CNNs) being the primary DL approach. The trend in cognitive state recognition research is moving towards integrating ECG signals with CNNs and adopting privacy-preserving methodologies like differential privacy and federated learning, highlighting the potential of cognitive state recognition to enhance performance, safety, and innovation across various real-world applications.

National Artificial Intelligence (AI) strategies articulate imaginaries of the integration of AI into society and envision the governing of AI research, development and applications accordingly. To integrate these central aspects of national AI strategies under one coherent perspective, this paper presented an analysis of Germany’s strategy ‘AI made in Germany’ through the conceptual lens of ordoliberal political rationality . The first part of the paper analyses how the guiding vision of a human-centric AI not only adheres to ethical and legal principles consistent with Germany’s liberal democratic constitutional system but also addresses the risks and promises inherent to the ordoliberal problematization of freedom. Second, it is scrutinized how the strategy cultivates the fear of not achieving technological sovereignty in the AI sector. Thereby, it frames the global AI race as a race of competing (national) approaches to governing AI and articulates an ordoliberal approach to governing AI (the ‘third way’), according to which government has to operate between the twin dangers of governing too much and not governing enough. Third, the paper analyses how this ordoliberal proportionality of governing structures Germany’s Science Technology & Innovation Policy. It is shown that the corresponding risk-based approach of regulating AI constitutes a security apparatus as it produces an assessment of fears: weighting the fear of the failure to innovate with the fear of the ramifications of innovation. Finally, two lines of critical engagement based on this analysis are conducted.

This paper introduces an innovative EEG sensor-based computational framework that establishes a pioneering nexus between personality trait quantification and neural dynamics, leveraging biosignal processing of brainwave activity to elucidate their intrinsic influence on cognitive health and oscillatory brain rhythms. By employing electroencephalography (EEG) recordings from 21 participants undergoing the Trier Social Stress Test (TSST), we propose a machine learning (ML)-driven methodology to decode the Big Five personality traits—Extraversion (Ex), Agreeableness (A), Neuroticism (N), Conscientiousness (C), and Openness (O)—using classification algorithms such as support vector machine (SVM) and multilayer perceptron (MLP) applied to 64-electrode EEG sensor data. A novel multiphase neurocognitive analysis across the TSST stages (baseline, mental arithmetic, job interview, and recovery) systematically evaluates the bidirectional relationship between personality traits and stress-induced neural responses. The proposed framework reveals significant negative correlations between frontal–temporal theta–beta ratio (TBR) and self-reported Extraversion, Conscientiousness, and Openness, indicating faster stress recovery and higher cognitive resilience in individuals with elevated trait scores. The binary classification model achieves high accuracy (88.1% Ex, 94.7% A, 84.2% N, 81.5% C, and 93.4% O), surpassing the current benchmarks in personality neuroscience. These findings empirically validate the close alignment between personality constructs and neural oscillatory patterns, highlighting the potential of EEG-based sensing and machine-learning analytics for personalized mental-health monitoring and human-centric AI systems attuned to individual neurocognitive profiles.

As the capabilities of artificial intelligence (AI) continue to expand, concerns are also growing about the ethical and social consequences of unregulated development and, above all, use of AI systems in a wide range of social areas. It is therefore indisputable that the application of AI requires social standardization and regulation. For years, innovation policy measures and the most diverse activities of European and German institutions have been directed toward this goal. Under the label “Trustworthy AI” (TAI), a promise is formulated, according to which AI can meet criteria of transparency, legality, privacy, non-discrimination, and reliability. In this article, we ask what significance and scope the politically initiated concepts of TAI occupy in the current process of AI dynamics and to what extent they can stand for an independent, unique European or German development path of this technology.