The proliferation of artificial intelligence (AI) tools has transformed numerous aspects of daily life, yet its impact on critical thinking remains underexplored. This study investigates the relationship between AI tool usage and critical thinking skills, focusing on cognitive offloading as a mediating factor. Utilising a mixed-method approach, we conducted surveys and in-depth interviews with 666 participants across diverse age groups and educational backgrounds. Quantitative data were analysed using ANOVA and correlation analysis, while qualitative insights were obtained through thematic analysis of interview transcripts. The findings revealed a significant negative correlation between frequent AI tool usage and critical thinking abilities, mediated by increased cognitive offloading. Younger participants exhibited higher dependence on AI tools and lower critical thinking scores compared to older participants. Furthermore, higher educational attainment was associated with better critical thinking skills, regardless of AI usage. These results highlight the potential cognitive costs of AI tool reliance, emphasising the need for educational strategies that promote critical engagement with AI technologies. This study contributes to the growing discourse on AI’s cognitive implications, offering practical recommendations for mitigating its adverse effects on critical thinking. The findings underscore the importance of fostering critical thinking in an AI-driven world, making this research essential reading for educators, policymakers, and technologists.

In this comprehensive study, insights from 1389 scholars across the US, UK, Germany, and Switzerland shed light on the multifaceted perceptions of artificial intelligence (AI). AI’s burgeoning integration into everyday life promises enhanced efficiency and innovation. The Trustworthy AI principles by the European Commission, emphasising data safeguarding, security, and judicious governance, serve as the linchpin for AI’s widespread acceptance. A correlation emerged between societal interpretations of AI’s impact and elements like trustworthiness, associated risks, and usage/acceptance. Those discerning AI’s threats often view its prospective outcomes pessimistically, while proponents recognise its transformative potential. These inclinations resonate with trust and AI’s perceived singularity. Consequently, factors such as trust, application breadth, and perceived vulnerabilities shape public consensus, depicting AI as humanity’s boon or bane. The study also accentuates the public’s divergent views on AI’s evolution, underlining the malleability of opinions amidst polarising narratives.

In recent years, a new type of influencer has emerged in the field of social media marketing: virtual influencers. Though it is spreading fast, the trend is still new and, therefore, limited research has been conducted on the topic. This study aims to investigate the impact of virtual influencers on customers and whether there is a direct impact on human influencers due to the rise of virtual influencers in the industry. The study employed a questionnaire-based survey method to collect and analyse responses from a sample of 357 participants. The questions focus on trust, credibility, expertise, and contribution to purchase intention by the virtual influencers. The results indicate that customers are increasingly attracted to virtual influencers and that virtual influences are perceived as more trustworthy, credible, and relevant to customers’ preferences, leading to an increase in purchase intention. The study also discusses the implications of these findings for managers designing marketing campaigns.

This study analyses the dimensions of trust in artificial intelligence (AI), focusing on why a significant portion of the UK population demonstrates a higher level of trust in AI compared to humans. Conducted through a mixed-methods approach, this study gathered 894 responses, with 451 meeting the criteria for analysis. It utilised a combination of a six-step Likert-scale survey and open-ended questions to explore the psychological, sociocultural, and technological facets of trust. The analysis was underpinned by structural equation modelling (SEM) and correlation techniques. The results unveil a strong predilection for trusting AI, mainly due to its perceived impartiality and accuracy, which participants likened to conventional computing systems. This preference starkly contrasts with the scepticism towards human reliability, which is influenced by the perception of inherent self-interest and dishonesty in humans, further exacerbated by a general distrust in media narratives. Additionally, this study highlights a significant correlation between distrust in AI and an unwavering confidence in human judgment, illustrating a dichotomy in trust orientations. This investigation illuminates the complex dynamics of trust in the era of digital technology, making a significant contribution to the ongoing discourse on AI’s societal integration and underscoring vital considerations for future AI development and policymaking.

The rapid adoption of generative AI raises questions not only about its transformative potential but also about its cognitive and societal risks. This study contributes to the debate by presenting cross-country experimental data (n = 150; Germany, Switzerland, United Kingdom) on how individuals engage with generative AI under different conditions: human-only, human + AI (unguided), human + AI (guided with structured prompting), and AI-only benchmarks. Across 450 evaluated responses, critical reasoning was assessed via expert rubric ratings, while perceived reflective engagement was captured through self-report indices. Results show that unguided AI use fosters cognitive offloading without improving reasoning quality, whereas structured prompting significantly reduces offloading and enhances both critical reasoning and reflective engagement. Mediation and latent class analyses reveal that guided AI use supports deeper human involvement and mitigates demographic disparities in performance. Beyond theoretical contributions, this study offers practical implications for business and society. As organisations integrate AI into workflows, unstructured use risks undermining workforce decision making and critical engagement. Structured prompting, by contrast, provides a scalable and low-cost governance tool that fosters responsible adoption, supports equitable access to technological benefits, and aligns with societal calls for human-centric AI. These findings highlight the dual nature of AI as both a productivity enabler and a cognitive risk, and position structured prompting as a promising intervention to navigate the emerging challenges of AI adoption in business and society.

This research critically examines the underlying anxieties surrounding artificial intelligence (AI) that are often concealed in public discourse, particularly in the United Kingdom. Despite an initial reluctance to acknowledge AI-related fears in focus groups, where 86% of participants claimed no significant concerns, further exploration through anonymous surveys and interviews uncovered deep anxieties about AI’s impact on job security, data privacy, and ethical governance. The research employed a mixed-methods approach, incorporating focus groups, a survey of 867 participants, and 53 semi-structured interviews to investigate these anxieties in depth. The study identifies key sources of concern, ranging from the fear of job displacement to the opacity of AI systems, particularly in relation to data handling and the control exerted by corporations and governments. The analysis reveals that anxieties are not evenly distributed across demographics but rather shaped by factors such as age, education, and occupation. These findings point to the necessity of addressing these anxieties to foster trust in AI technologies. This study highlights the need for ethical and transparent AI governance, providing critical insights for policymakers and organisations as they navigate the complex socio-technical landscape that AI presents.