Human Cognition Research Articles

From "online brains" to "online lives": understanding the individualized impacts of Internet use across psychological, cognitive and social dimensions.

In response to the mass adoption and extensive usage of Internet-enabled devices across the world, a major review published in this journal in 2019 examined the impact of Internet on human cognition, discussing the concepts and ideas behind the "online brain". Since then, the online world has become further entwined with the fabric of society, and the extent to which we use such technologies has continued to grow. Furthermore, the research evidence on the ways in which Internet usage affects the human mind has advanced considerably. In this paper, we sought to draw upon the latest data from large-scale epidemiological studies and systematic reviews, along with randomized controlled trials and qualitative research recently emerging on this topic, in order to now provide a multi-dimensional overview of the impacts of Internet usage across psychological, cognitive and societal outcomes. Within this, we detail the empirical evidence on how effects differ according to various factors such as age, gender, and usage types. We also draw from new research examining more experiential aspects of individuals' online lives, to understand how the specifics of their interactions with the Internet, and the impact on their lifestyle, determine the benefits or drawbacks of online time. Additionally, we explore how the nascent but intriguing areas of culturomics, artificial intelligence, virtual reality, and augmented reality are changing our understanding of how the Internet can interact with brain and behavior. Overall, the importance of taking an individualized and multi-dimensional approach to how the Internet affects mental health, cognition and social functioning is clear. Furthermore, we emphasize the need for guidelines, policies and initiatives around Internet usage to make full use of the evidence available from neuroscientific, behavioral and societal levels of research presented herein.

Recent Developments in RFT Encourage Interbehavioral Field-Based Views of Human Language and Cognition: A Preliminary Analysis

AbstractRelational frame theory (RFT) as a behavior-analytic approach to understanding human language and cognition is now over 40 years old. However, the last 8 years have seen a relatively intense period of empirical and conceptual developments within the theory. Some of this work has begun to draw on early and much underplayed features of RFT, including field-theoretical analyses and concepts. These analyses are relatively nascent and thus the current article aims to provide a relatively detailed example of a field-theoretical analysis of a specific RFT research program. We begin with a brief overview of the “traditional” RFT approach to human language and cognition, followed by a summary of recent research involving the implicit relational assessment procedure (IRAP) and the differential arbitrarily applicable relational responding effects (DAARRE) model. We then go on to consider the DAARRE model in the context of J. R. Kantor’s interbehavioral formula for the psychological event. Having done so, we conclude that the challenge involved in analyzing increasingly complex forms of human language and cognition appears to call for more field-based theorizing in some form or another.

Diurnal biological effects of correlated colour temperature and its exposure timing on alertness, cognition, and mood in an enclosed environment

Artificial lighting, which profits from the non-visual effects of light, is a potentially promising solution to support residents’ psychophysiological health and performance at specific times of the day in enclosed environments. However, few studies have investigated the non-visual effects of daytime correlated colour temperature (CCT) and its exposure timing on human alertness, cognition, and mood. However, the neural mechanisms underlying these effects are largely unknown. The current study evaluated the effects of daytime CCT and its exposure timing on markers of subjective experience, cognitive performance, and cerebral activity in a simulated enclosed environment. Forty-two participants participated a single-blind laboratory study with a 4 within (CCT: 4000 K vs. 6500 K vs. 8500 K vs. 12,000 K) × 2 between (exposure timing: morning vs. afternoon) mixed design. The results showed time of the day dependent benefits of the daytime CCT on subjective experience, vigilant attention, response inhibition, working memory, emotional perception, and risk decisions. The results of the electroencephalogram (EEG) revealed that lower-frequency EEG bands, including theta, alpha, and alpha-theta, were quite sensitive to daytime CCT intervention, which provides a valuable reference for trying to establish the underlying mechanisms that support the performance-enhancement effects of exposure to CCT in the daytime. However, the results revealed no consistent intervention pattern across these measurements. Therefore, future studies should consider personalised optimisation of daytime CCT for different cognitive demands.

The emergence of enhanced intelligence in a brain-inspired cognitive architecture

The Causal Cognitive Architecture is a brain-inspired cognitive architecture developed from the hypothesis that the navigation circuits in the ancestors of mammals duplicated to eventually form the neocortex. Thus, millions of neocortical minicolumns are functionally modeled in the architecture as millions of “navigation maps.” An investigation of a cognitive architecture based on these navigation maps has previously shown that modest changes in the architecture allow the ready emergence of human cognitive abilities such as grounded, full causal decision-making, full analogical reasoning, and near-full compositional language abilities. In this study, additional biologically plausible modest changes to the architecture are considered and show the emergence of super-human planning abilities. The architecture should be considered as a viable alternative pathway toward the development of more advanced artificial intelligence, as well as to give insight into the emergence of natural human intelligence.

The Impact of Writing Direction on Order-Picking Performance: Evidence on Diversity and Efficiency in Operations Management

Language system diversity is a source of individual differences. Research on human cognition has established that writing direction influences non-linguistic mental schemata such as spatial orientation. However, there is little empirical evidence of its impact on task performance. We examine whether task performance in manual order-picking is higher when the in-aisle travel direction follows the writing direction of order pickers. We conducted this study in cooperation with a German brick-and-mortar grocery retailer, allowing us to employ a unique real-world data set comprising 3,200,534 storage-location visits by 113 order pickers, 61 of whom had a left-to-right and 52 a right-to-left writing direction. Our statistical analyses suggest that order-picking task performance improves when the in-aisle travel direction follows individual writing direction. This creates a path to diversity-inspired operations management that treats efficiency and the diversity and inclusion of human workers as equally important for optimization.

CAST: Cross-Modal Retrieval and Visual Conditioning for image captioning

Image captioning requires not only accurate recognition of objects and corresponding relationships, but also full comprehension of the scene information. However, existing models suffer from partial understanding and object hallucination. In this paper, a Cross-modal retrievAl and viSual condiTioning model (CAST) is proposed to address the above issues for image captioning with three key modules: an image–text retriever, an image & memory comprehender and a dual attention decoder. Aiming at a comprehensive understanding, we propose to exploit cross-modal retrieval to mimic human cognition, i.e., to trigger retrieval of contextual information (called episodic memory) about a specific event. Specifically, the image–text retriever searches the top n relevant sentences which serve as episodic memory for each input image. Then the image & memory comprehender encodes an input image and enriches episodic memory by self-attention and relevance attention respectively, which can encourage CAST to comprehend the scene thoroughly and support decoding more effectively. Finally, such image representation and memory are integrated into our dual attention decoder, which performs visual conditioning by re-weighting image and text features to alleviate object hallucination. Extensive experiments are conducted on MS COCO and Flickr30k datasets, which demonstrate that our CAST achieves state-of-the-art performance. Our model also has a promising performance even in low-resource scenarios (i.e. 0.1%, 0.5% and 1% of MS COCO training set).

Generation of 'semi-guided' cortical organoids with complex neural oscillations.

Temporal development of neural electrophysiology follows genetic programming, similar to cellular maturation and organization during development. The emergent properties of this electrophysiological development, namely neural oscillations, can be used to characterize brain development. Recently, we utilized the innate programming encoded in the human genome to generate functionally mature cortical organoids. In brief, stem cells are suspended in culture via continuous shaking and naturally aggregate into embryoid bodies before being exposed to media formulations for neural induction, differentiation and maturation. The specific culture format, media composition and duration of exposure to these media distinguish organoid protocols and determine whether a protocol is guided or unguided toward specific neural fate. The 'semi-guided' protocol presented here has shorter induction and differentiation steps with less-specific patterning molecules than most guided protocols but maintains the use of neurotrophic factors such as brain-derived growth factor and neurotrophin-3, unlike unguided approaches. This approach yields the cell type diversity of unguided approaches while maintaining reproducibility for disease modeling. Importantly, we characterized the electrophysiology of these organoids and found that they recapitulate the maturation of neural oscillations observed in the developing human brain, a feature not shown with other approaches. This protocol represents the potential first steps toward bridging molecular and cellular biology to human cognition, and it has already been used to discover underlying features of human brain development, evolution and neurological conditions. Experienced cell culture technicians can expect the protocol to take 1 month, with extended maturation, electrophysiology recording, and adeno-associated virus transduction procedure options.

Viral invasion, incubation, and outbreak under the normalized operation of urban systems: a spatial cognition-driven transmission model of infectious diseases

ABSTRACT Large-scale epidemics, such as COVID-19, pose significant threats to human health and social stability due to their rapid and covert transmission, emerging as one of the main challenges to maintaining a well-functioning urban system. During the pandemic, the spatial transmission mechanisms and their influencing factors of infectious diseases have received as a central topic. Scholars concentrated on researching the phase of extensive dissemination and mandatory intervention, utilizing multiple datasets. However, there remains a notable gap in the investigation of virus spatial transmission mechanisms under normalized urban operations due to limitations in detection timeliness and data collection issues. Moreover, compared to various intervention strategies, the influence of human behavioral dynamics and spatial cognition remains understudied. The paper simulates invasion, incubation, and outbreak of the Omicron variant in Zhuhai City in 2022, validating the results against real-world epidemic data. By comparing spatial diffusion patterns under three levels of spatial cognition, it sheds light on the delayed impact of human spatial cognitive abilities on urban pandemic outbreaks and their influence on virus invasion orders. The simulation model and revealed diffusion mechanisms will provide important guidance for the proactive prevention and control of unpredictable large-scale epidemics in future urban systems.

Metaphorical models of the concept of "Longing" in the idiostyle of A. P. Platonov

The article is devoted to the identification and analysis of conceptual metaphors that are involved in the implementation of the concept of "Longing" in the works of A. P. Platonov. The subject of the study is the metaphorical models of the concept under study. At the present stage of linguistic science, metaphors are presented as complex phenomena that are not only a fact of language, but reflect the mechanisms of human consciousness and ideas about the world. Conceptual metaphors are considered as a special tool of human cognition, having a culturally archetypal nature. The mechanisms of perception lie in the subconscious of a person, at the level of which there are units of the mental level – concepts represented to a greater extent by conceptual (cognitive) metaphors. Metaphorical models are one of the components of a concept and can be identified with the "signs of the concept that form its structure". The work is carried out in line with the cognitive campaign in linguistics, based on the research of conceptual metaphor. The following methods were used: continuous sampling, descriptive, contextual, modeling. The scientific novelty of the research consists in identifying metaphorical models of the concept of "Longing" in the discourse of A. P. Platonov. As a result of the study, 6 cognitive models of metaphors were identified: "Localization", "Living being", "Substance", "Object", "Organ", "Receptacle". These models represent the idea of the concept of "Longing" as an entity that has a location inside a person, more often it is the heart of a person. This entity is represented as a living being or object. At the same time, if longing is a living being, then a person cannot control it, but can only hide from it, and if longing appears as an object, then a person can manipulate it. Also, longing is thought of as a kind of container that can absorb a person.

Human Cognition in AI: Overview on rise of Artificial General Intelligence

Abstract: Artificial General Intelligence introduces the transformation in the standpoint of Artificial Intelligence , essential to develop new intelligent machines and systems that simulate the cognitive skills in par with the human intelligence. This paper explores the core concepts of AGI or also called the human-level AI. By leveraging the power of human cognition and intelligence , the AGI can perform various cognitive tasks efficiently. It can also outperform the human intelligence across broad range of domains rather than being constricted to the limitations of narrow AI. Unlike the narrow AI’s that focuses on resolving the particular problems related to a specific domain and involving certain complex computations , an AGI integrated system aims for a holistic understanding of the intelligence and can resolve inter-domain issues simulating the human cognitive ability. Such systems would not only cater to provide solutions to predefined or existing problems but can intuitively step ahead and attempt to eradicate the future problems. The scope for the ability of an AGI powered system to be able to provide resolves for the future problems with advance reasoning and problem-solving skills can make it an indispensable partner in fields of scientific research, innovation , education and the list goes on. This paper accumulates the all-inclusive examination of current state of AGI and its potential scope in the near future

A Novel Intuitionistic Fuzzy Set-Based Risk Priority Number Method for Solving Chemical Experiment Risk Evaluation

Scientific experiments cover a wide range of fields—from basic to applied scientific research. Chemical experiments are the basis for cultivating chemical knowledge in scientific experiments and are an important way to cultivate scientific thinking and methods. However, due to the toxicity or flammability of the chemical substances in the experiments, hazardous events often lead to personal injuries and environmental damage. Exactly assessing risk factors and reducing the risk of hazards to protect the experimenters and ensure environmental safety are crucial in chemical experiments. However, while the traditional risk evaluation method cannot consider the weight of risk evaluation criteria, it also cannot effectively address problems through hierarchical analysis, as well as imprecise and ambiguous information inherent in human cognition. Therefore, this paper proposed an approach based on failure mode and effects analysis (FMEA) to assess the risk of chemical experiments in a fuzzy information environment. The approach combines the typical analytic hierarchy process (AHP), the risk priority number (RPN) of FMEA, and the intuitionistic fuzzy set (IFS) methods to evaluate risks associated with chemical experiments and consider the damage recovery in chemical experiments. This study applied the case of a university chemistry experiment, “preparation of hydrogels”, to validate the reasonableness and correctness of the proposed approach and compare its numerical verification results with those from the typical RPN, the AHP-RPN, and the AHP-fuzzy risk priority number (AHP-FRPN) methods. The finding demonstrates that the proposed method can more effectively address risk evaluation problems in chemical experiments than the other methods. This result serves as an important reference for reducing chemical experiment risk occurrences.

AI-Powered Defense Against Phishing Threats for Enterprises

Abstract: In the landscape of cybersecurity, the relentless onslaughtof phishing attacks poses a persistent and multifaceted threat, exploiting vulnerabilities in human cognition to perpetrate crimes ranging from identity theft to financial fraud and data breaches. In response to this evolving menace, we present an innovative and robust Phishing Website Detection System that leverages cutting-edge machine learning techniques to fortify the digital defenses of individuals and organizations alike. Our system represents a culmination of research and development efforts aimed at creating a comprehensive solution capable of effectively identifying and thwarting phishing attempts across diverse online platforms. By integrating a multitude of features spanning URL structure analysis, content-based attributes, SSL certificate characteristics, and user interaction patterns, our system adopts a multifaceted approach to phishing detection, enabling it to discern between legitimate and malicious websites with unparalleled accuracy.

Neurophysiological insights into sequential decision-making: exploring the secretary problem through ERPs and TBR dynamics.

Decision-making under uncertainty, a cornerstone of human cognition, is encapsulated by the "secretary problem" in optimal stopping theory. Our study examines this decision-making challenge, where participants are required to sequentially evaluate and make irreversible choices under conditions that simulate cognitive overload. We probed neurophysiological responses by engaging 27 students in a secretary problem simulation while undergoing EEG monitoring, focusing on Event-Related Potentials (ERPs) P200 and P400, and Theta to Beta Ratio (TBR) dynamics.Results revealed a nuanced pattern: the P200 component's amplitude declined from the initial to the middle offers, suggesting a diminishing attention span as participants grew accustomed to the task. This attenuation reversed at the final offer, indicating a heightened cognitive processing as the task concluded. In contrast, the P400 component's amplitude peaked at the middle offer, hinting at increased cognitive evaluation, and tapered off at the final decision. Additionally, TBR dynamics illustrated a fluctuation in attentional control and emotional regulation throughout the decision-making sequence, enhancing our understanding of the cognitive strategies employed.The research elucidates the dynamic interplay of cognitive processes in high-stakes environments, with neurophysiological markers fluctuating significantly as participants navigated sequential choices. By correlating these fluctuations with decision-making behavior, we provide insights into the evolving strategies from heightened alertness to strategic evaluation. Our findings offer insights that could inform the use of neurophysiological data in the development of decision-making frameworks, potentially contributing to the practical application of cognitive research in real-life contexts.

THE ROLE OF BLENDING THEORY IN POLYSEMY AND METAPHOR

Blending Theory offers a revolutionary perspective in cognitive linguistics, highlighting how innovation in language and thought stems from the integration of multiple conceptual spaces. This theory elucidates the cognitive mechanisms behind human creativity, providing a comprehensive framework for understanding the emergence of complex concepts, innovative metaphors, and the polysemous nature of language. Its application extends our insights into the dynamic nature of meaning-making and metaphorical thinking in human cognition.

Perplexity Games: Maoism vs. Literature through the Lens of Cognitive Stylometry

The arrival of large language models (LLMs) has provoked an urgent search for stylistic markers that could differentiate machine text from human text, but while the human-like appearance of machine text has captivated public attention, the reverse phenomenon—human text becoming machine-like—has raised much less concern. This conceptual lag is surprising given the ample historical evidence of state-backed attempts to regulate human thought. The present article proposes a new comparative framework, Perplexity Games, to leverage the predictive power of LLMs and compare the statistical properties of Maospeak, a language style that emerged during the Mao Zedong’s era in China (1949-1976), with the style of canonical modern Chinese writers, such as Eileen Chang (1920-1995) and Mo Yan (1955-). The low perplexity of Maospeak, as computed across different GPT models, suggests that the impact of ideologies on language can be compared to likelihood-maximization text-generation techniques which reduce the scope of valid sequence continuations. These findings have cognitive implications: whereas engineered languages such as Maospeak hijack the predictive mechanisms of human cognition by narrowing the space of linguistic possibilities, literature resists such cognitive constraints by dispersing the probability mass over multiple, equally valid paths. Exposure to diverse language data counters the influences of ideologies on our linguistically mediated perceptions of the world and increases the perplexity of our imaginations.

Exploring the Performance of ChatGPT Versions 3.5, 4, and 4 With Vision in the Chilean Medical Licensing Examination: Observational Study.

The deployment of OpenAI's ChatGPT-3.5 and its subsequent versions, ChatGPT-4 and ChatGPT-4 With Vision (4V; also known as "GPT-4 Turbo With Vision"), has notably influenced the medical field. Having demonstrated remarkable performance in medical examinations globally, these models show potential for educational applications. However, their effectiveness in non-English contexts, particularly in Chile's medical licensing examinations-a critical step for medical practitioners in Chile-is less explored. This gap highlights the need to evaluate ChatGPT's adaptability to diverse linguistic and cultural contexts. This study aims to evaluate the performance of ChatGPT versions 3.5, 4, and 4V in the EUNACOM (Examen Único Nacional de Conocimientos de Medicina), a major medical examination in Chile. Three official practice drills (540 questions) from the University of Chile, mirroring the EUNACOM's structure and difficulty, were used to test ChatGPT versions 3.5, 4, and 4V. The 3 ChatGPT versions were provided 3 attempts for each drill. Responses to questions during each attempt were systematically categorized and analyzed to assess their accuracy rate. All versions of ChatGPT passed the EUNACOM drills. Specifically, versions 4 and 4V outperformed version 3.5, achieving average accuracy rates of 79.32% and 78.83%, respectively, compared to 57.53% for version 3.5 (P<.001). Version 4V, however, did not outperform version 4 (P=.73), despite the additional visual capabilities. We also evaluated ChatGPT's performance in different medical areas of the EUNACOM and found that versions 4 and 4V consistently outperformed version 3.5. Across the different medical areas, version 3.5 displayed the highest accuracy in psychiatry (69.84%), while versions 4 and 4V achieved the highest accuracy in surgery (90.00% and 86.11%, respectively). Versions 3.5 and 4 had the lowest performance in internal medicine (52.74% and 75.62%, respectively), while version 4V had the lowest performance in public health (74.07%). This study reveals ChatGPT's ability to pass the EUNACOM, with distinct proficiencies across versions 3.5, 4, and 4V. Notably, advancements in artificial intelligence (AI) have not significantly led to enhancements in performance on image-based questions. The variations in proficiency across medical fields suggest the need for more nuanced AI training. Additionally, the study underscores the importance of exploring innovative approaches to using AI to augment human cognition and enhance the learning process. Such advancements have the potential to significantly influence medical education, fostering not only knowledge acquisition but also the development of critical thinking and problem-solving skills among health care professionals.

What Can Language Models Tell Us About Human Cognition?

Language models are a rapidly developing field of artificial intelligence with enormous potential to improve our understanding of human cognition. However, many popular language models are cognitively implausible on multiple fronts. For language models to offer plausible insights into human cognitive processing, they should implement a transparent and cognitively plausible learning mechanism, train on a quantity of text that is achievable in a human’s lifetime of language exposure, and not assume to represent all of word meaning. When care is taken to create plausible language models within these constraints, they can be a powerful tool in uncovering the nature and scope of how language shapes semantic knowledge. The distributional relationships between words, which humans represent in memory as linguistic distributional knowledge, allow people to represent and process semantic information flexibly, robustly, and efficiently.

Analyzing Reddit Data: Hybrid Model for Depression Sentiment using FastText Embedding

Depression, a prevalent mental condition globally, exerts a substantial influence on various aspects of human cognition, emotions, and behaviour. The alarming increase in deaths attributable to depression over the past few years demonstrates the imperative need to address this problem through prevention and treatment interventions. In the era of thriving social media platforms, which have a significant impact on society and psychological aspects, these platforms have become a means for people to convey their emotions and experiences openly. Reddit stands out among these platforms as a significant place. The main aim of this study is to examine the feasibility of forecasting individuals' mental states by classifying Reddit articles on depression and non-depression. This work aims to employ deep learning algorithms and word embeddings to analyze the textual and semantic settings of narratives to detect symptoms of depression. The study effectively employed a BiLSTM-BiGRU model that applied FastText word embeddings. The BiLSTM-BiGRU model analyzes information bidirectionally, detecting correlations in sequential data. It's suitable for tasks dependent on input order or addressing data uncertainties. The Reddit dataset, featuring text concerning depression, achieved an accuracy score of 97.03% and an F1 score of 97.02%.

Harmonizing Human-AI Synergy in Smart Cities: Exploring Emotion Understanding and Technology Anxiety in an Asian Context

Artificial Superintelligence (ASI) envisions AI surpassing human cognitive abilities, mimicking and developing its own thinking skills. However, this raises ethical and control issues, emphasizing the need for robust regulations in AI development. The prime objective of the study is to align aligning human intelligence with artificial superintelligence in smart cities. Particularly, the study has examined the mediating role of emotion-understanding ability, and the technology anxiety between AI and shared decision-making. This study investigates the relationship between emotional intelligence and decision-making in the Khon Kaen Smart City, Thailand. Data was collected from various financial institutions, including community and commercial banks, and managers working in the city. The largest and most developed province served as the primary focus of the study, and area cluster sampling was employed to obtain a representative sample of the province's varied population. A total of 297 out of the 700 questionnaires that were given to the seven different groups for the survey were filled out. Shared Decision-Making (SDM) and Emotion-Understanding Ability (EUA) were found to have a strong correlation in the study, which highlights the importance of emotional intelligence in decision-making due to the strong correlation between the two. This comprehension is especially helpful when artificial intelligence is used to improve decision-making processes. This highlights the significance of human factors, such as comprehension and emotion, in the process of developing effective decision-making framework.

Impact of artificial intelligence on health information literacy: guidance for healthcare professionals

Purpose This paper aims to explore the intricate relationship between artificial intelligence (AI) and health information literacy (HIL), examining the rise of AI in health care, the intersection of AI and HIL and the imperative for promoting AI literacy and integrating it with HIL. By fostering collaboration, education and innovation, stakeholders can navigate the evolving health-care ecosystem with confidence and agency, ultimately improving health-care delivery and outcomes for all. Design/methodology/approach This paper adopts a conceptual approach to explore the intricate relationship between AI and HIL, aiming to provide guidance for health-care professionals navigating the evolving landscape of AI-driven health-care delivery. The methodology used in this paper involves a synthesis of existing literature, theoretical analysis and conceptual modeling to develop insights and recommendations regarding the integration of AI literacy with HIL. Findings Impact of AI on health-care delivery: The integration of AI technologies in health-care is reshaping the industry, offering unparalleled opportunities for improving patient care, optimizing clinical workflows and advancing medical research. Significance of HIL: HIL, encompassing the ability to access, understand and critically evaluate health information, is crucial in the context of AI-driven health-care delivery. It empowers health-care professionals, patients and the broader community to make informed decisions about their health and well-being. Intersection of AI and HIL: The convergence of AI and HIL represents a critical juncture, where technological innovation intersects with human cognition. AI technologies have the potential to revolutionize how health information is generated, disseminated and interpreted, necessitating a deeper understanding of their implications for HIL. Challenges and opportunities: While AI holds tremendous promise for enhancing health-care outcomes, it also introduces new challenges and complexities for individuals navigating the vast landscape of health information. Issues such as algorithmic bias, transparency and accountability pose ethical dilemmas that impact individuals’ ability to critically evaluate and interpret AI-generated health information. Recommendations for health-care professionals: Health-care professionals are encouraged to adopt strategies such as staying informed about developments in AI, continuous education and training in AI literacy, fostering interdisciplinary collaboration and advocating for policies that promote ethical AI practices. Practical implications To enhance AI literacy and integrate it with HIL, health-care professionals are encouraged to adopt several key strategies. First, staying abreast of developments in AI technologies and their applications in health care is essential. This entails actively engaging with conferences, workshops and publications focused on AI in health care and participating in professional networks dedicated to AI and health-care innovation. Second, continuous education and training are paramount for developing critical thinking skills and ethical awareness in evaluating AI-driven health information (Alowais et al., 2023). Health-care organizations should provide opportunities for ongoing professional development in AI literacy, including workshops, online courses and simulation exercises focused on AI applications in clinical practice and research. Originality/value This paper lies in its exploration of the intersection between AI and HIL, offering insights into the evolving health-care landscape. It innovatively synthesizes existing literature, proposes strategies for integrating AI literacy with HIL and provides guidance for health-care professionals to navigate the complexities of AI-driven health-care delivery. By addressing the transformative potential of AI while emphasizing the importance of promoting critical thinking skills and ethical awareness, this paper contributes to advancing understanding in the field and promoting informed decision-making in an increasingly digital health-care environment.