Psychological absorption and the maintenance of agency in AI-mediated cognition: a relapse prevention perspective—V4

The integration of Artificial Intelligence (AI) in healthcare is often hindered by a lack of trust among healthcare professionals, impacting the effectiveness of Human-AI interaction. This study examines how Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) influence trust in AI, with trust mediating the relationship between these constructs and Human-AI interaction in healthcare settings. Additionally, the researcher investigates the moderating role of Cognitive Load, positing that higher cognitive demands may weaken the positive effects of trust on Human-AI interaction. By surveying doctors, nurses, and medical technicians in Malaysia, the study aims to provide a comprehensive understanding of these dynamics. It is expected to find that increased PU and PEOU enhance trust, leading to improved Human-AI interaction, while Cognitive Load may diminish the strength of this relationship. Anticipated findings will underscore the importance of designing AI systems that are intuitive, beneficial, and mindful of cognitive demands to optimise healthcare outcomes and clinician support. This research holds significant commercialisation potential as healthcare organisations increasingly seek AI solutions that enhance trust and facilitate effective collaboration between humans and AI.

This article overviews a study on integrating generative AI tools into education. English language education is critical for AI integration due to its reliance on text-based learning, communication skills and adaptive instructional approaches. Employing a systematic literature review to analyse existing research on AI tools in English language education, it focuses on their benefits and challenges. Articles from Scopus-indexed journals and other scholarly sources published between 2021 and 2024 were reviewed. The study discusses four significant findings: the classification of generative AI, key research topics on AI, the role of AI in cognitive offloading and academic dishonesty and the phenomenon of AI-generated hallucinations. The findings highlight the need for educators to stay updated with AI advancements and adapt their teaching practices accordingly. While AI offers opportunities for personalised learning, it also raises concerns about academic integrity and the reliability of AI-generated content. The review is limited to studies published between 2021 and 2024 and may not encompass all relevant research. Additionally, the focus is primarily on English language education, which may not fully represent the impact of AI tools across other educational contexts. Future research could explore the broader implications of AI integration across different subject areas and examine its long-term effects on pedagogy and student learning outcomes.

The rapid integration of generative artificial intelligence (GenAI) into design practice has intensified debates surrounding authorship, agency, and ethics. Although AI systems are increasingly embedded in creative workflows, they are often framed as neutral tools or collaborative partners without sufficient attention to their socio-technical construction and the biases embedded in their training data. This paper critically examines the relationship between designers and AI systems through a theoretical and pedagogical lens. Drawing on Flusser’s concept of the apparatus and Floridi’s philosophy of information, it situates contemporary AI as a human-designed, data-driven system that mediates and restructures creative processes rather than functioning as an autonomous subject. The paper argues that clarifying the distinction between perceived agency and experiential subjectivity is essential for understanding accountability in AI-mediated design. In response, it proposes a practice-oriented educational framework aimed at cultivating ethical reflexivity and critical AI literacy among design students and professionals. By articulating a relational and socio-technical perspective on human-AI interaction, the paper contributes to ongoing discussions in design theory and education about responsibility, bias, and the distribution of agency in generative systems.

The purpose of the study is to identify, systematise and critically analyse the psycho-visual mechanisms used in contemporary graphic design to manage perception in conditions of information overload. Research methodology. An interdisciplinary approach was used, combining the principles of cognitive psychology, neuroaesthetics, Gestalt theory and design analytics. The primary research methods include: critical visual analysis, cognitive-perceptual interpretation, philosophical conceptualisation of visual experience, and content analysis of examples of visual communication in conditions of information saturation. Scientific novelty. The article offers an interdisciplinary analysis of psycho-visual mechanisms for managing perception through graphic design in conditions of information overload, examining the intersection of cognitive aesthetics and visual semiotics. The application of psycho-visual consistency as a factor in effective communication in overloaded conditions is clarified. A typology of cognitive functions of forms is proposed, which allows visual elements to be considered as carriers of perceptual influence. Psycho-visual mechanisms of perception control, facilitated by graphic design, are generalised in conditions of information overload. The results are of practical importance for the design of visual communications, can be used as analytical tools in the creation of visual strategies, and can be incorporated into the training of future graphic designers. Conclusions. Research into the psycho-visual mechanisms of perception control through graphic design confirms their leading role in structuring visual information in conditions of information overload. It is essential to focus not on visual complexity, but on the informativeness and controllability of attention, which provide grounds for creating adaptive and intuitive design solutions. The graphic designer acts as an intermediary between complex information flows and the user’s cognitive abilities, while graphic design performs regulatory and meaning-making functions. The effectiveness of graphic design depends on the graphic designer’s ability to apply Gestalt principles, visual accents, rhythm, and compositional balance, which ensure cognitive economy and facilitate perception.

Purpose This study aims to explore the interconnectedness between artificial intelligence (AI) integration, customer satisfaction, process task efficiency and organizational readiness within the hospitality and tourism sector, elucidating their combined influence on firm performance. Design/methodology/approach The research sample comprises 790 owners, supervisors, managers, customers and employees from 158 firms from hospitality and tourism firms in Guangzhou. This study uses a multimodel approach to analyze the relationships between AI integration, customer satisfaction, process task efficiency, organizational readiness and firm performance. Findings Model 1 indicates a positive correlation between AI integration and firm performance. Model 2 introduces customer satisfaction as a mediator, revealing its partial mediation effect on the relationship between AI integration and firm performance. Model 3 expands to demonstrate the moderating effect of process task efficiency on the AI integration–firm performance relationship. Finally, Model 4 incorporates organizational readiness as a predictor, enhancing the model’s fit and emphasizing its significance in driving firm performance alongside other factors. Research limitations/implications This study’s scope is limited to the hospitality and tourism sector in Guangzhou, potentially restricting the generalizability of findings to other industries or regions. Future research could explore diverse contexts to ascertain broader implications. Practical implications The findings underscore the multifaceted impact of AI integration on organizational outcomes, highlighting strategic opportunities for firms to enhance performance through investments in AI integration and organizational preparedness. Originality/value This study contributes to the understanding of how AI integration, along with factors like customer satisfaction, process task efficiency and organizational readiness, collectively shape firm performance within the hospitality and tourism sector, offering valuable insights for strategic decision-making and resource allocation.

Purpose This study examines how secondary school administrators can lead ethical artificial intelligence (AI) integration within environments demanding technological innovation and educational value preservation. Design/methodology/approach The study conducted a scoping review of literature (2018–2025) to analyze administrative functions across four established leadership dimensions: instructional, managerial, strategic, and relational. Sources were obtained from academic databases and grey literature, with 21 sources selected based on relevance to secondary education and administrative practice. Analysis is grounded in foundational leadership scholarship while examining contemporary AI integration challenges. Findings The analysis reveals a misalignment between AI's most frequent use (relational leadership functions) and where it may be most appropriately suited (managerial and strategic functions). AI integration creates distinct opportunities and risks across each leadership dimension, with equity concerns emerging consistently. Communication represents the primary AI use, despite being the most fundamentally human aspect of educational leadership. Cognitive offloading risks emerge when administrators delegate critical thinking tasks to AI systems, potentially attenuating leadership capabilities essential for educational effectiveness. Research limitations/implications This study relies on secondary data collection and English-language sources, creating Western-centric bias and limiting generalizability beyond North American contexts. The corpus of 21 sources reflects the nascent research state in this emerging field. The rapid evolution of AI capabilities means current findings may prove transitional as technology advances. Future empirical research should examine long-term cognitive effects of AI reliance on administrators, stakeholder trust implications when AI-mediated communications are detected, differential equity impacts across diverse school communities, cross-cultural implementation patterns, and effectiveness of hybrid governance approaches for AI integration in educational leadership. Practical implications Findings support implementing hybrid governance models that combine regulatory oversight with participatory decision-making between administrators and stakeholders. Professional development programs must balance AI literacy training with preserving human capabilities essential for authentic educational leadership. Administrator preparation programs require redesign to address cognitive offloading risks while maintaining relationship-building and cultural competence development. Educational leaders should prioritize AI applications in managerial and strategic functions while preserving human judgment in relational leadership contexts. Policy frameworks must address equity concerns and provide guidance for schools serving vulnerable populations who currently receive less AI implementation support. Social implications AI implementation without critical examination risks amplifying existing educational inequities, particularly affecting Indigenous, newcomer, and racialized communities. Democratic participation in AI boundary-setting becomes essential for maintaining institutional trust and stakeholder engagement. The misalignment between AI deployment and appropriate applications threatens the relational foundations of effective educational leadership. Originality/value The study provides the first systematic examination of AI integration across established educational leadership dimensions in secondary school contexts, addressing a critical research gap given that nearly 60% of K-12 principals use AI tools while fewer than 10% of schools have established AI policies.

The study is devoted to the issue of digital transformation of business structures and focuses on examining the conceptual foundations of the digital transformation of these entities. Digital transformation is one of the key trends in modern business. A vision of a paradigm shift in development regulation, based on the consideration of digital methods of delivering value to the market has been outlined in the research. The essence of the digital transformation process lies in the creation of a new mode of production, characterized by the integration of human and artificial intelligence. It is substantiated that digital transformation is a strategic goal of the company, which requires a holistic approach and leads to a change in the very economic paradigm. It is noted that digital transformation is accompanied by the application of advanced technologies that serve as its foundation, and the study identifies a list of such technologies. It is revealed that technologies do not operate in isolation but create a powerful synergistic effect. Digital transformation fundamentally alters the way companies create value and generate profit. Instead of traditional models, digital technologies enable the development of new, innovative approaches that redefine market entry strategies. It is identified the key components of corporate digital transformation in the study, namely: the human factor, technological foundations, organizational change, and strategic development. The need for digital transformation is shaped by both external and internal factors that compel organizations to reconsider their strategy and operations. It is emphasized that the most challenging aspect of transformation is not technological but human and organizational. Cultural adaptation, employee engagement, and the enhancement of digital skills are critically important for overcoming resistance and minimizing risks. Transformational changes in the digital economy make businesses more flexible, innovative, and data-driven, yet they require a balance between technologies and the human factor. It is further noted that the next stages of digital transformation – including hyper-automation, cognitive robotics, and further integration of artificial intelligence – will demand continuous adaptation and innovative thinking from organizations to remain leaders in the era of the digital economy.

The rapid integration of generative artificial intelligence (GenAI) in K-12 education presents both transformative opportunities (e.g., personalized learning and creative scaffolding) and significant cognitive and ethical risks (e.g., cognitive offloading, misinformation exposure, and the erosion of critical thinking). This perspective article proposes a developmentally-aligned, five-tiered framework (ages 3–18) to guide responsible GenAI use, moving beyond polarized debates of prohibition versus unrestricted adoption. Grounded in developmental psychology theories and empirical findings, the framework calibrates GenAI exposure to students’ cognitive and socio-emotional stages, mitigating risks like cognitive offloading and ethical disengagement while leveraging benefits such as personalized learning and creative scaffolding. Each tier specifies educational objectives, permitted activities (e.g., teacher-led demonstrations in early tiers, collaborative data analysis in later tiers), and cut-off values (e.g., zero direct use for ages 3–5). Emphasizing multi-stakeholder collaboration, it assigns actionable roles to policymakers, educators, technology developers, and parents to ensure equitable implementation. The author advocates for empirical research to validate the framework’s cut-off values and adapt it across diverse contexts, promoting AI-resistant pedagogies that prioritize student agency.

Purpose This paper aims to highlight the crucial role of strategic human resource management in addressing labour tensions that arise from the integration of artificial intelligence (AI) and automation in contemporary workplaces. Effective approaches to managing technological transformation while maintaining positive labour relations are also discussed. Design/methodology/approach The paper draws upon two labour dispute cases at supermarket giants, Woolworths and the aviation sector in Australia. The cases are analysed through the lens of strategic human resource management and discussed using previous studies, expert and industry insights. Findings This paper reveals that successful AI integration requires more than technological expertise – it also demands sophisticated people management strategies that can balance innovation with human concerns. To do so, there is a need for strategic workforce planning and AI integration, cultural transformation and change management, ethical considerations and worker well-being, balancing efficiency and human agency and leadership in AI transformation. Comprehensive strategy development, stakeholder engagement, governance structures and skills development are recommended for smooth AI integration in modern workplaces. Originality/value This paper uses two recent labour disputes in Australia to illuminate the critical role of strategic HR management in balancing AI integration with employee well-being and engagement. As AI and automation continue to reshape workplaces, technological transformation must serve both organisational objectives and worker interests. Lessons from this paper can guide future strategic HR approaches to AI integration in ways that promote sustainable and equitable workplace transformation.

This study examined artificial intelligence integration and business sustainability in Enugu State and its implications for co-operative enterprises. The specific objectives were to ascertain the extent the integration of artificial intelligence influence the sustainability of co-operative business enterprises and to evaluate the extent the challenges of co-operative business enterprises influence the integration of artificial intelligence. Descriptive survey research design was adopted for the study, with a population of 322, consequently adopted as the sample size owing to its manageable size. A structured questionnaire was used for data collection and validated through content and face validity. Cronbach’s Alpha was used to ascertain the reliability of the instrument, with reliability coefficient of 0.84, indicating that the instrument was reliable. Data were presented in tables and analyzed using simple percentages and means through a 5 points Likert Scale. Formulated hypotheses were tested using simple linear regression. Findings showed that the integration of artificial intelligence had significant positive influence on sustainability of co-operative business enterprises (r2 = 0.500, f = 0.206, p < 0.05); challenges of co-operative business enterprises had significant positive influence on the integration of artificial intelligence (r2 = 0.371, f = 4.394, p < 0.05). The study therefore concluded that the integration of artificial intelligence significantly boosts co-operative business sustainability and consequently driven by the challenges these enterprises face. Recommendations were made among others that Co-operatives should embrace AI smartly, train their teams, and use it to stay efficient long-term.

The integration of Artificial Intelligence (AI) in autonomous vehicles represents a transformative leap towards safer, more efficient, and technologically advanced transportation systems. This abstract provides a comprehensive overview of the dynamic landscape where AI converges with autonomous vehicles, examining the synergies, challenges, and far-reaching implications of this groundbreaking integration AI-Powered Perception and Decision-Making: Delving into the technological core, the abstract explores how AI empowers autonomous vehicles with sophisticated perception systems, such as computer vision and sensor fusion. It discusses the role of machine learning algorithms in real-time decision-making, enabling vehicles to adapt to dynamic road conditions and unforeseen circumstances. Challenges in Autonomy: Recognizing the complexity of autonomous systems, the abstract addresses challenges such as handling edge cases, ensuring robustness against adversarial attacks, and navigating regulatory and ethical considerations. It emphasizes the importance of addressing these challenges to foster public trust and acceptance of autonomous vehicles. Human-AI Interaction in Autonomous Vehicles: Examining the interface between humans and AI-driven vehicles, the abstract discusses the importance of designing intuitive and trustworthy communication channels. It explores advancements in natural language processing and gesture recognition, fostering seamless collaboration between humans and autonomous systems. Regulatory Landscape and Ethical Considerations: Recognizing the pivotal role of regulations, the abstract discusses the evolving regulatory landscape for autonomous vehicles. It delves into ethical considerations surrounding AI decisions in critical situations, underscoring the need for a harmonized approach to ensure responsible AI deployment in autonomous driving scenarios.

The integration of artificial intelligence (AI) in sports training has emerged as a transformative approach to enhancing individual performance, optimizing training strategies, and providing personalized insights for athletes and coaches. This article presents a comprehensive review of the applications, algorithms, challenges, and future directions of AI in individual sports training. We explore the utilization of AI algorithms and techniques, including machine learning, deep learning, and computer vision, in sports apps to personalize training programs, analyze performance, provide feedback, assess injury risks, and optimize training methodologies. The article examines the scientific foundations of AI-enhanced sports training, discussing the personalization and customization of individual training, performance analysis and feedback using AI-powered tools, injury prevention and risk assessment through AI models, user experience and interface design considerations, ethical implications and data privacy, case studies and empirical evidence, challenges, and recommendations for further research. We highlight the potential of AI in transforming the way athletes train, providing tailored interventions, and optimizing performance outcomes. The article concludes by identifying areas for future research, including advanced data analytics, explainable AI models, ethical considerations, collaboration, longitudinal studies, optimization of training programs, human-AI interaction, and generalization to diverse populations. By addressing these research avenues, the field of AI-enhanced sports training can continue to evolve, supporting athletes and coaches in achieving their goals and unlocking new dimensions of performance optimization.

The integration of artificial intelligence (AI) into daily cognitive tasks has transformed traditional cognitive offloading—the delegation of mental processes to external tools—into a dynamic partnership with intelligent systems. This article examines the dual role of AI-driven cognitive offloading, exploring its potential to enhance efficiency and creativity while posing risks to memory consolidation, critical thinking, and intellectual autonomy. Grounded in extended mind theory (Clark & Chalmers, 1998) and empirical studies like the Google Effect (Sparrow et al., 2011), the analysis reveals how AI's generative capabilities (e.g., ChatGPT, Midjourney) shift offloading from passive storage to delegated thinking, where users adopt AI outputs with minimal scrutiny, fostering automation bias (Logg et al., 2019). In educational contexts, AI tools risk undermining deep learning by reducing retrieval practice and encouraging superficial engagement, as illustrated by a case study of Hatt University, where AI-assisted essays distorted grading systems and eroded student critical analysis. Similarly, in healthcare and finance, overreliance on AI recommendations may compromise professional judgment. To mitigate these risks, the article proposes strategies such as metacognitive training, explainable AI design (Sundar, 2020), and curriculum reforms prioritizing active engagement with AI outputs. Ethical and policy interventions are urged to address epistemic opacity, intellectual property, and the cultural redefinition of authorship. The study underscores the need for balanced AI integration—one that harnesses its benefits while safeguarding human cognitive autonomy.

The relevance of this study stems from the rapid development of artificial intelligence (AI), particularly large language models and generative technologies, which are profoundly transforming professional activity. These changes significantly influence the formation of predictive competence – a crucial human capacity for reasoned forecasting and decision-making under uncertainty. Empirical data confirm the high accuracy of AI-generated forecasts, sometimes surpassing that of humans, and indicate improved professional productivity when AI is used effectively. At the same time, diverse adaptation patterns to AI use necessitate a rethinking of the role of human judgement and raise concerns about technological dependency, algorithmic bias, and unequal access to innovation. These challenges call for a reorientation of educational approaches, placing emphasis on critical thinking and skills for effective human-AI interaction. The purpose of the study is to conduct a comprehensive analysis and theoretical substantiation of the impact of modern AI technologies – particularly large language models and generative AI – on the development and transformation of professionals’ predictive competence. Research objectives are as follows: to conceptualise predictive competence within the context of digital transformation; to analyse structural shifts in its key components (cognitive, regulatory, and communicative); to explore the mechanisms of AI’s influence on cognitive predicting processes; to systematise potential advantages and risks associated with the integration of AI in professional contexts. The study employs theoretical methods such as analysis, synthesis, and generalisation of findings from interdisciplinary research, as well as conceptual and comparative analysis of human-AI interaction models and the evolving essence of predictive competence. AI demonstrably increases the efficiency of forecasting processes but simultaneously transforms their nature – from autonomous human-generated predictions to the management of hybrid human-machine systems. This shift requires professionals to acquire new skills, including critical evaluation and validation of AI outputs, prompt engineering, and the integration of AI-generated insights into complex decision-making. The most significant transformations influence the cognitive, regulatory, and communicative components of predictive competence. The dual nature of AI’s impact is evident – offering enhanced analytical capabilities while posing risks of hallucinations, cognitive inertia, and increased digital inequality. Accordingly, the professional role evolves from that of executor to analyst, moderator, and ethical regulator of forecasting processes. Conclusions. Artificial intelligence is irreversibly reshaping the landscape of professional activity, par- ticularly in the domain of forecasting. Its influence on predictive competence is deep, multifaceted, and at times contradictory. Maximising its benefits while mitigating associated risks requires a proactive, critical, and adaptive attitude from professionals and educators alike. To this end, educational programmes should be enriched with: practice-oriented integration of AI tools into professional curricula; targeted development of skills for evaluating AI outputs; competence in prompt engineering for forecasting; the promotion of metacognitive awareness. These measures will enable the preparation of specialists who do not merely un- derstand AI but can employ it purposefully, critically, and responsibly to enhance their predictive capacities.

Topicality. Contemporary business environments demand innovative management approaches that transcend traditional organizational paradigms through the integration of artificial intelligence and neurofeedback technologies. While conventional management focuses on hierarchical control structures and standardized processes, there is limited research on creative mechanisms that leverage real-time neurophysiological feedback for dynamic organizational optimization. This study presents a novel framework for creative business management utilizing brain-computer interfaces and adaptive machine learning algorithms to enhance decision-making quality, personnel productivity, and organizational synergy. Aim and tasks. The aim of this research is to develop and validate a comprehensive creative business management model based on the integration of artificial intelligence and neurofeedback technologies to optimize organizational effectiveness under conditions of uncertainty. Main tasks: (1) develop a mathematical model of AI-neurofeedback integration for organizational optimization; (2) validate model effectiveness through mathematical modeling and empirical analysis of neurophysiological patterns; (3) identify key dimensions of business process optimization through cognitive technology integration. Materials and Methods. A systematic approach was employed to design and validate the model through mathematical modeling and empirical analysis of neurophysiological patterns. The framework integrates non-invasive electroencephalography (EEG) monitoring with adaptive algorithms that track managers' cognitive states, creativity patterns, and emotional responses during strategic business operations. Machine learning models analyze multimodal biometric data to automatically adjust management strategies, optimize team configurations, and predict market behaviors according to individual and collective neural signatures. Research results. The system identifies six core dimensions: Overall Business Efficiency Optimization, Personnel Productivity Enhancement, Consumer Behavior Prediction, Dynamic Decision Quality Management, Organizational Structure Adaptation, and Synergistic Effect Maximization, collectively explaining optimal business outcomes. Validation through mathematical modeling demonstrated significant improvements in overall efficiency (58.6% gain), decision quality optimization (peak performance at 15:00), and purchase prediction accuracy (up to 92.3%). The integrated model showed high reliability in personalizing business experiences (correlation r=0.81 between creativity and risk readiness) and effectively adapting to diverse organizational contexts. Conclusion. Results confirm that creative management mechanisms based on AI-neurofeedback integration enhance traditional business approaches by providing real-time cognitive optimization and predictive strategic pathways, establishing foundations for next-generation adaptive organizational systems.