Human social cognition has evolved in the "we mode," a uniquely human capacity to form shared intentions and collaborate as unified agents. This collective intentionality, rooted in embodied mechanisms such as synchronized behavior, joint attention, interbrain coupling, and emotional attunement, has enabled the open-ended creativity and adaptability that define human culture. However, the rise of social media and artificial intelligence is reshaping these foundational dynamics. This article identifies a dual threat posed by the emergence of a "digital we." First, algorithmically mediated digital communication erodes embodied social cues and reinforces homophily, weakening the neurobiological scaffolding that sustains we mode cognition. Second, the increasing reliance on generative AI in communication and creative domains risks reducing novelty and fostering convergence, thereby narrowing the scope of cultural innovation. These developments are situated within the broader "comfort-growth paradox," the tension between technological systems designed for intuitive, predictable user experiences and the human need for cognitive challenge, ambiguity, and developmental tension. While comfort-driven design maximizes engagement, it may suppress the very dissonance necessary for creativity and growth. As a result, the "digital we" risks cultivating passive familiarity at the expense of dialogical tension and pluralistic meaning-making. To counter this trajectory, the article argues for reimagining digital technologies not as replacements for human connection but as extensions of it. Prioritizing design that fosters discovery, exploration, and critical intersubjectivity is essential for preserving the open-ended, transformative potential of human culture in an age of digital mediation. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

This conceptual review paper explores the emerging phenomenon of skill degradation in the context of increasing reliance on artificial intelligence (AI) within higher education and professional environments. As AI tools become integral to learning, writing, assessment, and decision-making processes, many users particularly students and instructors experience what has been termed the illusion of competence, a misleading perception of mastery created by AI-generated outputs that mask underlying cognitive deficits. Drawing on Cognitive Load Theory and Technological Dependency Theory, this paper examines how the offloading of intellectual effort to AI systems diminishes core human faculties such as memory retention, critical thinking, metacognitive awareness, creativity, and professional judgment. The analysis is structured across multiple dimensions, including the cognitive mechanisms of skill loss, real-world manifestations in academic and occupational settings, and the broader psychological and social consequences of overdependence. It highlights risks such as academic underperformance, reduced originality, erosion of self-efficacy, widening equity gaps, and the devaluation of human expertise. Ethical and pedagogical concerns such as fairness, transparency, data privacy, and faculty readiness are also addressed. The paper concludes with strategic recommendations for educational institutions, including the need for AI literacy training, faculty development, assessment reform, and policy frameworks that encourage responsible and critical engagement with AI technologies. Ultimately, the paper argues for a balanced, human-centered approach to AI integration, one that positions AI as a support system rather than a substitute for cognitive engagement, ensuring that technological advancement enhances rather than displaces the human capacity for deep, reflective learning.

Abstract This study examines the threat of AI to the emancipation of the human brain, with a particular focus on its impact on human logical thinking and creativity. It reveals the centrality of AI in knowledge engineering by analyzing expert systems constructed by the Symbolist school, which emphasize the role of knowledge bases and reasoning machines by simulating human logical thinking. The study also examines how AI affects modern human subjectivity, utilizing the “Valley of Terror” curve theory to explain the changes in human attitudes toward AI Further, the study introduces human brain-based AI models, such as the application of back-propagation algorithms in neural networks, as well as a cognitive-based model of knowledge operations in the human brain. The results show that the development of AI poses a threat to human brain power, especially in information processing and decision making. AI can replace human brain functions, leading to human logic and creativity degradation. For example, the application of ChatGPT in education can significantly improve students’ test rankings and reduce the reliance on individual thinking skills. In summary, AI may lead to the degradation of human logical thinking and creative ability while improving efficiency and convenience, affecting the overall development of human beings. Therefore, the focus should be on balancing the relationship between the utilization of AI and the development of human brain capacity, significantly to enhance the cultivation of human creativity and imagination in education and innovation.

Although artificial intelligence tools increasingly shape creative thinking in education, existing research focused more on outcome effects than on cognitive processes. This systematic review examined the cognitive mechanisms, process-phase transformations, and student strategies characterizing AI-integrated creative thinking. Guided by PRISMA 2020 (Page et al., 2021), 49 peer-reviewed empirical studies published between 2022 and 2025 were selected from Scopus and Web of Science. A process-evidence criterion framework ensured the inclusion of studies reporting process-level data beyond outcome-only measures. Methodological quality was assessed using the MMAT, and data were analyzed through content analysis and thematic synthesis. The most striking finding revealed that AI-mediated creative thinking is shaped by how learners engage with AI outputs. Across many studies, the same AI use was associated with both facilitative and inhibitory cognitive mechanisms within the same learning activity. This pattern suggests that the impact of AI depends not simply on using AI for part of the thinking process, but on whether learners engage with AI outputs critically or accept them uncritically. Accordingly, the central challenge is no longer simply whether AI can enhance creativity, but how educators can design learning environments that harness AI productively while preserving and developing the deeper cognitive processes that underlie human creative thinking.

The global shortage of mental health professionals, exacerbated by increasing mental health needs post COVID-19, has stimulated growing interest in leveraging large language models to address these challenges. This systematic review aims to evaluate the current capabilities of generative artificial intelligence (GenAI) models in the context of mental health applications. A comprehensive search across 5 databases yielded 1046 references, of which 8 studies met the inclusion criteria. The included studies were original research with experimental designs (eg, Turing tests, sociocognitive tasks, trials, or qualitative methods); a focus on GenAI models; and explicit measurement of sociocognitive abilities (eg, empathy and emotional awareness), mental health outcomes, and user experience (eg, perceived trust and empathy). The studies, published between 2023 and 2024, primarily evaluated models such as ChatGPT-3.5 and 4.0, Bard, and Claude in tasks such as psychoeducation, diagnosis, emotional awareness, and clinical interventions. Most studies used zero-shot prompting and human evaluators to assess the AI responses, using standardized rating scales or qualitative analysis. However, these methods were often insufficient to fully capture the complexity of GenAI capabilities. The reliance on single-shot prompting techniques, limited comparisons, and task-based assessments isolated from a context may oversimplify GenAI's abilities and overlook the nuances of human-artificial intelligence interaction, especially in clinical applications that require contextual reasoning and cultural sensitivity. The findings suggest that while GenAI models demonstrate strengths in psychoeducation and emotional awareness, their diagnostic accuracy, cultural competence, and ability to engage users emotionally remain limited. Users frequently reported concerns about trustworthiness, accuracy, and the lack of emotional engagement. Future research could use more sophisticated evaluation methods, such as few-shot and chain-of-thought prompting to fully uncover GenAI's potential. Longitudinal studies and broader comparisons with human benchmarks are needed to explore the effects of GenAI-integrated mental health care.

Safety and efficiency of human-AI collaboration often depend on how humans could appropriately calibrate their trust towards the AI agents. Over-trusting the autonomous system sometimes causes serious safety issues. Although many studies focused on the importance of system transparency in keeping proper trust calibration, the research in detecting and mitigating improper trust calibration remains very limited. To fill these research gaps, we propose a method of adaptive trust calibration that consists of a framework for detecting the inappropriate calibration status by monitoring the user’s re