Human-AI collaboration faces growing challenges as AI systems increasingly outperform humans on complex tasks, while humans remain responsible for orchestration, validation, and decision oversight. To address this imbalance, we introduce Human Tool, an MCP-style interface abstraction, building on recent Model Context Protocol designs, that exposes humans as callable tools within AI-led, proactive workflows. Here, "tool" denotes a coordination abstraction, not a reduction of human authority or responsibility. Building on LLM-based agent architectures, we operationalize Human Tool by modeling human contributions through structured tool schemas of capabilities, information, and authority. These schemas enable agents to dynamically invoke human input based on relative strengths and reintegrate it through efficient, natural interaction protocols. We validate the framework through controlled studies in both decision-making and creative tasks, demonstrating improved task performance, reduced human workload, and more balanced collaboration dynamics compared to baseline systems. Finally, we discuss implications for human-centered AI design, highlighting how MCP-style human tools enable strong AI leadership while amplifying uniquely human strengths.
AI-augmented systems are traditionally designed to streamline human decision-making by minimizing cognitive load, clarifying arguments, and optimizing efficiency. However, in a world where algorithmic certainty risks becoming an Orwellian tool of epistemic control, true intellectual growth demands not passive acceptance but active struggle. Drawing on the dystopian visions of George Orwell and Philip K. Dick - where reality is unstable, perception malleable, and truth contested - this paper introduces Cognitive Dissonance AI (CD-AI): a novel framework that deliberately sustains uncertainty rather than resolving it. CD-AI does not offer closure, but compels users to navigate contradictions, challenge biases, and wrestle with competing truths. By delaying resolution and promoting dialectical engagement, CD-AI enhances reflective reasoning, epistemic humility, critical thinking, and adaptability in complex decision-making. This paper examines the theoretical foundations of the approach, presents an implementation model, explores its application in domains such as ethics, law, politics, and science, and addresses key ethical concerns - including decision paralysis, erosion of user autonomy, cognitive manipulation, and bias in AI reasoning. In reimagining AI as an engine of doubt rather than a deliverer of certainty, CD-AI challenges dominant paradigms of AI-augmented reasoning and offers a new vision - one in which AI sharpens the mind not by resolving conflict, but by sustaining it. Rather than reinforcing Huxleyan complacency or pacifying the user into intellectual conformity, CD-AI echoes Nietzsche's vision of the Uebermensch - urging users to transcend passive cognition through active epistemic struggle.
This paper offers a Christian ethical evaluation of transhumanism. It employs a two-part framework. First, the paper contextualizes transhumanism within the evolutionary cosmology of Pierre Teilhard de Chardin and thereby suggests a theological openness to technologically influenced development as part of an ongoing cosmogenesis towards greater consciousness, or the Omega Point. Second, the paper critically evaluates transhumanist values against five key principles of Catholic Social Teaching (CST): natural law, human dignity, human flourishing, the common good, and care for creation. While the Teilhardian lens does indeed allow us to interpret certain transhumanist goals as potentially conducive to humans, the CST assessment reveals serious ethical concerns that must be addressed. These concerns include threats to inherent dignity through the reductionism of the human person, the potential unchecked exacerbation of current social inequality, and significant conflicts with the care of creation stemming from an unchecked technocratic paradigm as described in <i>Laudato Si’</i>. This paper concludes that while engagement with transhumanism is necessary, a Christian perspective should strive to ensure that technological advancement remains subordinate to the universal dignity of all persons, the common good, and authentic flourishing in communion with God.
The paper explores the evolution of communication etiquette between humans and artificial intelligence (AI), focusing particularly on the adaptation of traditional politeness strategies. While the politeness of AI can enhance the human level of trust, human politeness towards AI is equally important as it can impact the efficiency of communication. To demonstrate this, I conducted a pilot experiment with ChatGPT 4.0, using polite and non-polite prompts in Russian. The results suggest that politeness is likely to positively impact the accuracy of responses. Furthermore, the paper examines changes in speech etiquette, highlighting how interactions with AI often omit traditional greetings and farewells, reflecting a more functional approach to communication. Although the paper does not provide definitive answers on how politeness strategies between humans and AI should function, it underscores sociolinguistic points that are likely to become increasingly pressing over time. Overall, the paper illustrates a significant shift in communication practices, driven by the integration of AI into daily interactions, necessitating a reevaluation of the established social norms and speech etiquette.
Rayden Tseng, Suzan Verberne, Peter van der Putten
ChatGPT, GPT-3.5, and other large language models (LLMs) have drawn significant attention since their release, and the abilities of these models have been investigated for a wide variety of tasks. In this research we investigate to what extent GPT-3.5 can generate human-like comments on Dutch news articles. We define human likeness as `not distinguishable from human comments', approximated by the difficulty of automatic classification between human and GPT comments. We analyze human likeness across multiple prompting techniques. In particular, we utilize zero-shot, few-shot and context prompts, for two generated personas. We found that our fine-tuned BERT models can easily distinguish human-written comments from GPT-3.5 generated comments, with none of the used prompting methods performing noticeably better. We further analyzed that human comments consistently showed higher lexical diversity than GPT-generated comments. This indicates that although generative LLMs can generate fluent text, their capability to create human-like opinionated comments is still limited.
Identifying the spatial patterns of genetic structure of influenza A viruses is a key factor for understanding their spread and evolutionary dynamics. In this study, we used phylogenetic and Bayesian clustering analyses of genetic sequences of the A/H1N1pdm09 virus with district-level locations in mainland China to investigate the spatial genetic structure of the A/H1N1pdm09 virus across human population landscapes. Positive correlation between geographic and genetic distances indicates high degrees of genetic similarity among viruses within small geographic regions but broad-scale genetic differentiation, implying that local viral circulation was a more important driver in the formation of the spatial genetic structure of the A/H1N1pdm09 virus than even, countrywide viral mixing and gene flow. Geographic heterogeneity in the distribution of genetic subpopulations of A/H1N1pdm09 virus in mainland China indicates both local to local transmission as well as broad-range viral migration. This combination of both local and global structure suggests that both small-scale and large-scale population circulation in China is responsible for viral genetic structure. Our study provides implications for understanding the evolution and spread of A/H1N1pdm09 virus across the population landscape of mainland China, which can inform disease control strategies for future pandemics.
Junya Sunagawa, Hyeongki Park, Kwang Su Kim
et al.
Abstract During the COVID-19 pandemic, human behavior change as a result of nonpharmaceutical interventions such as isolation may have induced directional selection for viral evolution. By combining previously published empirical clinical data analysis and multi-level mathematical modeling, we find that the SARS-CoV-2 variants selected for as the virus evolved from the pre-Alpha to the Delta variant had earlier and higher peak in viral load dynamics but a shorter duration of infection. Selection for increased transmissibility shapes the viral load dynamics, and the isolation measure is likely to be a driver of these evolutionary transitions. In addition, we show that a decreased incubation period and an increased proportion of asymptomatic infection are also positively selected for as SARS-CoV-2 mutated to adapt to human behavior (i.e., Omicron variants). The quantitative information and predictions we present here can guide future responses in the potential arms race between pandemic interventions and viral evolution.
Domestic violence is a worldwide crime recognized as a severe violation of Human Rights, which includes Intimate Partner Violence (IPV). The studies remark that the asymmetries in the social relations between men and women result in domination dynamics. Thus, this study analyzed the relationship between gender and IPV beliefs in the general population, university students, and healthcare/safety/justice professionals by comparing IPV legitimization between men and women and with age. The sample was composed by 3413 Portuguese participants, 1551 men (45.4%) and 1826 women (54.6%), aged 18 to 100 (<i>M</i> = 37.97; <i>SD</i> = 18.09), 1936 participants from the general population (56.7%), 866 university students [e.g., healthcare students] (25.4%) and 611 healthcare/safety/justice professionals [e.g., doctors, psychologists, police officers, lawyers] (17.9%). The sample filled out the Scale of Beliefs about Marital Violence (ECVC), a self-report scale on beliefs about IPV. Results confirmed our hypothesis that men have significantly higher levels of IPV legitimization than women. In accordance with our second hypothesis, significant positive correlations were found between age and IPV beliefs. As age increases, older people tend to be more tolerant of IPV, and young people tend to be less endorsing such IPV beliefs. Finally, we found the hypothesis that university students and healthcare/safety/justice professionals have lower levels of beliefs compared with other participants in the general population. Findings show that we need to work hard with the social evolution in men’s and women’s beliefs on IPV, reinforcing the importance of targeting IPV prevention by gender and age in the general population but also in students and professionals.
The evolution of human cognitive function is reliant on complex social interactions which form the behavioural foundation of who we are. These social capacities are subject to dramatic change in disease and injury; yet their supporting neural substrates remain poorly understood. Hyperscanning employs functional neuroimaging to simultaneously assess brain activity in two individuals and offers the best means to understand the neural basis of social interaction. However, present technologies are limited, either by poor performance (low spatial/temporal precision) or an unnatural scanning environment (claustrophobic scanners, with interactions via video). Here, we describe hyperscanning using wearable magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs). We demonstrate our approach by simultaneously measuring brain activity in two subjects undertaking two separate tasks—an interactive touching task and a ball game. Despite large and unpredictable subject motion, sensorimotor brain activity was delineated clearly, and the correlation of the envelope of neuronal oscillations between the two subjects was demonstrated. Our results show that unlike existing modalities, OPM-MEG combines high-fidelity data acquisition and a naturalistic setting and thus presents significant potential to investigate neural correlates of social interaction.
There is a clear desire to model and comprehend human behavior. Trends in research covering this topic show a clear assumption that many view human reasoning as the presupposed standard in artificial reasoning. As such, topics such as game theory, theory of mind, machine learning, etc. all integrate concepts which are assumed components of human reasoning. These serve as techniques to attempt to both replicate and understand the behaviors of humans. In addition, next generation autonomous and adaptive systems will largely include AI agents and humans working together as teams. To make this possible, autonomous agents will require the ability to embed practical models of human behavior, which allow them not only to replicate human models as a technique to "learn", but to to understand the actions of users and anticipate their behavior, so as to truly operate in symbiosis with them. The main objective of this paper it to provide a succinct yet systematic review of the most important approaches in two areas dealing with quantitative models of human behaviors. Specifically, we focus on (i) techniques which learn a model or policy of behavior through exploration and feedback, such as Reinforcement Learning, and (ii) directly model mechanisms of human reasoning, such as beliefs and bias, without going necessarily learning via trial-and-error.
Aurora Saibene, Silvia Corchs, Mirko Caglioni
et al.
The Motor Imagery (MI) electroencephalography (EEG) based Brain Computer Interfaces (BCIs) allow the direct communication between humans and machines by exploiting the neural pathways connected to motor imagination. Therefore, these systems open the possibility of developing applications that could span from the medical field to the entertainment industry. In this context, Artificial Intelligence (AI) approaches become of fundamental importance especially when wanting to provide a correct and coherent feedback to BCI users. Moreover, publicly available datasets in the field of MI EEG-based BCIs have been widely exploited to test new techniques from the AI domain. In this work, AI approaches applied to datasets collected in different years and with different devices but with coherent experimental paradigms are investigated with the aim of providing a concise yet sufficiently comprehensive survey on the evolution and influence of AI techniques on MI EEG-based BCI data.