Trust, resistance, and transformation: A Q‐methodological study of teachers' perspectives on AI‐generated feedback in second language writing

The integration of Artificial Intelligence (AI) in healthcare has been impeded by a significant issue: a lack of trust among healthcare professionals, stemming from the opacity of AI decision-making processes and a general unfamiliarity with AI technologies. This study investigates the impact of AI's explainability and healthcare professionals' familiarity with AI on their trust in AI applications within healthcare settings. Adopting a quantitative research methodology, the study utilized a structured questionnaire to gather data from a diverse group of healthcare professionals, including doctors, nurses, and administrators, across various hospitals and healthcare institutions in Pakistan. The research employed a stratified random sampling approach to ensure a comprehensive and representative data set. The results indicated a positive and significant relationship between AI explainability and trust in AI (Path Coefficient: 0.62, t-Value: 5.20), suggesting that clearer and more transparent AI decision-making processes enhance healthcare professionals' trust., Similarly, familiarity with AI was found to positively influence trust in AI (Path Coefficient: 0.48, t-Value: 4.35), highlighting the importance of exposure and understanding of AI systems among healthcare professionals. These findings have crucial implications for both AI developers and healthcare administrators. For AI developers, the emphasis must be on creating more transparent and interpretable AI systems. For healthcare administrators, the results suggest the need to invest in training and educational programs to increase professionals' familiarity with AI, thereby enhancing trust and acceptance. The study significantly contributes to the existing literature by empirically validating the importance of AI explainability and familiarity in building trust in AI within the healthcare context, especially in a developing country setting. For policymakers, these insights are critical in guiding strategies and policies aimed at effectively integrating AI into healthcare systems. By addressing the identified factors, healthcare sectors can better leverage AI's potential, leading to improved patient care and more efficient healthcare operations.

Ethical oversight of Artificial Intelligence in Nigerian Healthcare: A qualitative analysis of ethics committee members' perspectives on integration and regulation.

The integration of artificial intelligence (AI) into health care has become a crucial element in the digital transformation of health systems worldwide. Despite the potential benefits across diverse medical domains, a significant barrier to the successful adoption of AI systems in health care applications remains the prevailing low user trust in these technologies. Crucially, this challenge is exacerbated by the lack of consensus among experts from different disciplines on the definition of trust in AI within the health care sector. We aimed to provide the first consensus-based analysis of trust in AI in health care based on an interdisciplinary panel of experts from different domains. Our findings can be used to address the problem of defining trust in AI in health care applications, fostering the discussion of concrete real-world health care scenarios in which humans interact with AI systems explicitly. We used a combination of framework analysis and a 3-step consensus process involving 18 international experts from the fields of computer science, medicine, philosophy of technology, ethics, and social sciences. Our process consisted of a synchronous phase during an expert workshop where we discussed the notion of trust in AI in health care applications, defined an initial framework of important elements of trust to guide our analysis, and agreed on 5 case studies. This was followed by a 2-step iterative, asynchronous process in which the authors further developed, discussed, and refined notions of trust with respect to these specific cases. Our consensus process identified key contextual factors of trust, namely, an AI system's environment, the actors involved, and framing factors, and analyzed causes and effects of trust in AI in health care. Our findings revealed that certain factors were applicable across all discussed cases yet also pointed to the need for a fine-grained, multidisciplinary analysis bridging human-centered and technology-centered approaches. While regulatory boundaries and technological design features are critical to successful AI implementation in health care, ultimately, communication and positive lived experiences with AI systems will be at the forefront of user trust. Our expert consensus allowed us to formulate concrete recommendations for future research on trust in AI in health care applications. This paper advocates for a more refined and nuanced conceptual understanding of trust in the context of AI in health care. By synthesizing insights into commonalities and differences among specific case studies, this paper establishes a foundational basis for future debates and discussions on trusting AI in health care.

In the context of the rapid integration of artificial intelligence (AI) into daily life, this study explores underexamined factors influencing adolescents’ intentions to adopt AI technology. Based on the stimulus-organism-response (SOR) framework, the study uses a structural equation model to investigate the relationships between parental mediation, AI literacy, trust in AI, perceived creepiness of AI, and intention to use AI. A sample of 853 adolescents participated in this study. The findings reveal that active parental mediation significantly enhances AI literacy, fosters trust in AI, and mitigates perceived creepiness of AI, thereby increasing adolescents’ intention to use AI technology. In contrast, restrictive parental mediation does not exhibit any significant effects on AI literacy or usage intentions. The findings highlight the critical role of family influence in adolescents’ adoption of AI, advocating for parents to take an active parental mediation to help adolescents benefit in the AI era.

The integration of Artificial Intelligence (AI) into personal finance and wealth management has fundamentally reshaped financial behaviors and decision-making processes. The primary objective of this study is to evaluate the role of AI in influencing personal financial behaviors and wealth management outcomes. Specifically, it aims to determine how AI adoption, investment, and usage impact personal savings and net worth. This study adopts a quantitative approach, utilizing secondary data from trusted sources such as Our World in Data and the Federal Reserve Bank of St. Louis. The dataset spans from 2010 to 2022, capturing trends over a significant period of AI development and adoption. A multivariate regression model is employed to examine the relationships between the dependent variables, Personal Savings Rate and Change in Net Worth, and independent variables such as AI adoption rate, AI investment, and household debt-to-income ratio. Descriptive statistics, correlation analysis, and stationarity tests are conducted to ensure data reliability and model validity. Diagnostic checks, including heteroskedasticity tests and Durbin-Watson statistics, further validate the robustness of the results. The study reveals that AI adoption positively influences personal savings by encouraging disciplined financial behaviors, consistent with the findings of prior research. However, its impact on wealth accumulation is less direct, with AI investment showing a surprising negative association with changes in net worth. This indicates inefficiencies in resource allocation or lag effects in the benefits of large-scale AI investments. Traditional economic factors, such as household debt and spending habits, continue to play significant roles in shaping financial outcomes, highlighting the enduring influence of non-technological determinants. The study also underscores the role of macroeconomic variables, such as unemployment, in moderating AI’s impact, with precautionary savings behaviors emerging during periods of economic uncertainty. Based on the findings, several actionable recommendations emerge. For individuals, the adoption of AI-driven tools that promote financial literacy and track spending can enhance savings and improve overall financial health. Financial institutions should prioritize user-centric designs in AI platforms, ensuring accessibility and functionality for diverse demographics. Policymakers are encouraged to support initiatives that bridge disparities in AI adoption, such as digital literacy programs and affordable access to financial technologies. Moreover, strategic investment in AI tools that address wealth management complexities, such as portfolio optimization and risk assessment, is critical for improving long-term financial outcomes. Originality This study contributes to the growing body of literature on AI in finance by offering a dual focus on personal savings and wealth management. Unlike previous studies that often treat these domains independently, this research provides an integrated perspective, highlighting both the synergies and divergences in AI’s impact. The findings on the nuanced relationship between AI investment and financial outcomes offer a fresh lens for evaluating the effectiveness of technological advancements. Furthermore, the study’s emphasis on traditional economic factors alongside AI-related variables underscores its originality in bridging the gap between technological innovation and foundational economic principles. This approach provides a robust framework for future research and practical applications in finance.

Artificial Intelligence (AI) has the potential to reshape family medicine by enhancing clinical, educational, administrative, and research operations. Despite AI's transformative potential, its adoption is inconsistent, and strategic frameworks remain limited. This study explores current AI adoption, organizational policies, integration priorities, and budget allocations within family medicine departments. A survey of 218 family medicine department chairs in the US and Canada was conducted via SurveyMonkey from August 13 to September 20, 2024, as part of the Council of Academic Family Medicine (CAFM) Educational Research Alliance (CERA) omnibus project. Survey questions assessed current and planned AI utilization, presence of formal departmental or organizational policies (defined as written guidelines, strategic plans, or frameworks), integration priorities, and budget allocations. Data were analyzed using Chi-square tests, Wilcoxon Rank Sum tests, and Kruskal-Wallis tests, with a primary focus on bivariate comparisons. The survey achieved a 50.9% response rate (111/218). Current AI use was reported by 56.9% (62/109), while 37.6% (41/109) indicated formal organizational policies. Primary goals for AI integration included improving clinical operations (52.3%), administrative streamlining (16.5%), educational applications (11.9%), and research (4.6%). Budget allocations were minimal (median, 0%; mean 2.4%), though departmental budgets likely underestimate actual institutional investment in AI. Departments reporting AI use had significantly more full-time equivalent faculty (median, 40.0 vs 25.5, P = .023). Geographic and chair demographics were not significantly associated with differences in AI adoption. AI integration in family medicine departments is viewed as essential, though current adoption is limited by uncertain strategic planning and minimal departmental budget allocations, potentially reflecting reliance on centralized institutional information technology (IT) investments. While AI is widely viewed as important, structured policy frameworks and implementation strategies are still developing. Further research is essential to guide policy development and strategic investment to ensure AI's safe, efficient, and effective integration into family medicine.

The use of artificial intelligence (AI) in companies is advancing rapidly. Consequently, multidisciplinary research on AI in business has developed dramatically during the last decade, moving from the focus on technological objectives towards an interest in human users’ perspective. In this article, we investigate the notion of employees’ trust in AI at the workplace (in the company), following a human-centered approach that considers AI integration in business from the employees’ perspective, taking into account the elements that facilitate human trust in AI. While employees’ trust in AI at the workplace seems critical, so far, few studies have systematically investigated its determinants. Therefore, this study is an attempt to fill the existing research gap. The research objective of the article is to examine links between employees’ trust in AI in the company and three other latent variables (general trust in technology, intra-organizational trust, and individual competence trust). A quantitative study conducted on a sample of 428 employees from companies of the energy and chemical industries in Poland allowed the hypotheses to be verified. The hypotheses were tested using structural equation modeling (SEM). The results indicate the existence of a positive relationship between general trust in technology and employees’ trust in AI in the company as well as between intra-organizational trust and employees’ trust in AI in the company in the surveyed firms.

The integration of artificial intelligence (AI) into cosmetic medicine promises to revolutionize the field by enhancing diagnosis, treatment planning, and patient care. This manuscript explores the current adoption and perceptions of AI among professionals in the realm of cosmetic dermatology and plastic surgery, utilizing insights from the IMCAS Congress 2024 attendees. A survey employing a digital questionnaire with 14 questions was distributed among attendees of the IMCAS Congress 2024 to evaluate their familiarity with AI, usage in clinical practice, perceived advantages, and concerns regarding data privacy and security. The survey revealed that a majority of respondents are familiar with AI's potential in cosmetic medicine, yet there is a notable discrepancy between awareness and actual application in practice. Concerns over data privacy and a pronounced need for further training were also highlighted. Despite recognizing AI's benefits in cosmetic medicine, significant barriers such as data privacy concerns and the need for more comprehensive training resources must be addressed. Enhancing education on AI-applications and developing strategies to mitigate privacy risks are imperative for leveraging AI's full potential in improving patient care and outcome in cosmetic medicine.

The integration of artificial intelligence (AI) in healthcare, particularly in digital cytology, has the potential to enhance diagnostic accuracy and workflow efficiency. However, AI adoption remains limited due to technological and human-related barriers. Understanding the perceptions and experiences of healthcare professionals is essential for overcoming these challenges and facilitating effective AI implementation. This study aimed to assess AI integration in digital cytology workflows by evaluating professionals' perspectives on its benefits, challenges, and requirements for successful adoption. A survey was conducted among 150 professionals working in public and private healthcare settings in Italy, including laboratory technicians (35%), medical doctors (25%), biologists (20%), and specialists in diagnostic technical sciences (20%). Data were collected through a structured Computer-Assisted Web Interview (CAWI) and a Virtual Focus Group (VFG) to capture quantitative and qualitative insights on AI familiarity, perceived advantages, and barriers to adoption. The findings indicated varying levels of AI familiarity among professionals. While many recognized AI's potential to improve diagnostic accuracy and streamline workflows, concerns were raised regarding resistance to change, implementation costs, and doubts about AI reliability. Participants emphasized the need for structured training and continuous support to facilitate AI adoption in digital cytology. Addressing barriers such as resistance, cost, and trust is essential for the successful integration of AI in digital cytology workflows. Tailored training programs and ongoing professional support can enhance AI adoption, ultimately optimizing diagnostic processes and improving clinical outcomes.

Purpose This study aims to investigate the interplay between artificial intelligence (AI) integration, organizational digital culture, human resource management (HRM) practices and employee sustainable performance in luxury hotels in Malaysia. It seeks to elucidate how AI adoption influences organizational dynamics, shapes HRM practices and impacts employee sustainable performance over time. Design/methodology/approach Using a quantitative approach, survey questionnaires derived from prior research were utilized. Analysis using G*Power software determined an appropriate sample size, with psychometric evaluation validating scale development. Statistical analyses using Statistical Package for Social Sciences (SPSS) 28.0 and SmartPLS 4 confirmed data reliability and validity. Findings Out of the five hypotheses, three were supported. A positive relationship was found between AI adoption and employee sustainable performance, highlighting AI’s potential to enhance productivity and job satisfaction. However, the relationship between AI adoption and organizational digital culture was not supported. On the other hand, HRM practices positively influenced employee sustainable performance. In addition, organizational digital culture was positively associated with employee sustainable performance, underscoring the role of digital fluency in driving workforce productivity. Conversely, AI failed to moderate the relationship between HRM practices and employee sustainable performance. Research limitations/implications The study’s focus on luxury hotels in Malaysia and its reliance on cross-sectional data, suggesting the need for longitudinal designs and diverse organizational contexts in future research. Comparative studies across sectors and countries could offer insights into variations in AI adoption practices and their impact on organizational performance. Originality/value This study contributes to theoretical frameworks by empirically examining complex relationships between AI integration, HRM practices, organizational digital culture and employee performance, emphasizing the importance of considering organizational context and cultural factors in understanding the implications of AI adoption for sustainable performance enhancement.

This study explores Indian library and information science (LIS) professionals’ perspectives on the integration of artificial intelligence (AI) in academic libraries in India. It aims to evaluate their comprehension of AI, determine their perspectives, investigate AI utilization, assess advantages, identify influencing factors, and examine attitudes towards AI adoption. A quantitative research approach was employed, utilizing a structured questionnaire designed based on study objectives and reviewed by subject matter experts. Purposive sampling targeted individuals with relevant LIS knowledge. Data were collected through Google Forms from 259 respondents and analysed using descriptive and inferential statistics. Respondents generally exhibited positive perceptions towards AI integration in libraries. High mean scores were observed for statements such as “AI can bridge librarian performance gaps” and “AI does not make library staff lazy.” Librarians expressed willingness to learn about AI, interest in its ethical implications, and confidence in its potential to improve library services. The study highlights a cautious optimism towards AI adoption in Indian academic libraries, with recognition of its potential benefits tempered by concerns about employment and resource allocation. Librarians demonstrate proactive attitudes towards engaging with AI technology and understanding its implications for library services, indicating a readiness to embrace AI within the profession.

Background/Aim: From accurate diagnostics to personalised treatment plans, artificial intelligence (AI) has the potential to revolutionise healthcare. The abundance of medical data has led to AI being employed for accurate diagnoses, treatment protocols and patient care. Students' perception of AI integration in medical education is crucial for its successful implementation. This study aimed to assess the acceptance and understanding of AI integration among students in medical education across different regions of India through a cross-sectional observation. Methods: A pan-India survey was conducted among medical students between 1 August 2023 to 20 August 2023 with a pre-validated questionnaire covering AI awareness and understanding through Google Form, circulated via WhatsApp. Results: A total of 730 medical students completed the survey of which 58.6 % were males and 41.4 % were females. Most students (80.7 %) knew about AI, but 53.6 % had limited awareness of AI in medicine. Opinions on AI integration was diverse, with 46.8 % in favour. Workshops (45.2 %) and lectures (31.1 %) were preferred learning formats. Students were interested in various AI topics and expect AI to positively impact medicine (45.9 %). Radiology, surgery and general medicine were predicted to be most influenced by AI. Concerns about overreliance on AI (49.2 %) and lack of empathy (43.7 %) were highlighted. Conclusions: Medical students in India display a keen interest in AI and its integration into medical education. To fully harness AI's potential in healthcare, comprehensive AI curricula and faculty training are needed. Students are aware of the challenges and opportunities, emphasising the importance of balanced AI adoption in medical practice and education.

Artificial intelligence (AI) is transforming oncology and surgery by improving diagnostics, personalizing treatments, and enhancing surgical precision. Patients appreciate AI for its potential to provide accurate prognoses and tailored therapies. However, AI's implementation raises ethical concerns, data privacy issues, and the need for transparent communication between patients and health care providers. This study aims to understand patients' perspectives on AI integration in oncology and surgery to foster a balanced and patient-centered approach. The study utilized a comprehensive literature review and analysis of existing research on AI applications in oncology and surgery. The focus was on examining patient perceptions, ethical considerations, and the potential benefits and risks associated with AI integration. Data was collected from peer-reviewed journals, conference proceedings, and expert opinions to provide a broad understanding of the topic. The perspectives of patients was also emphasized to highlight the nuances of their acceptance and concerns regarding AI in their health care. Patients generally perceive AI in oncology and surgery as beneficial, appreciating its potential for more accurate diagnoses, personalized treatment plans, and improved surgical outcomes. They particularly value AI's role in providing timely and precise diagnostics, which can lead to better prognoses and reduced anxiety. However, concerns about data privacy, ethical implications, and the reliability of AI systems were prevalent. Consequently, trust in AI and health care providers was deemed as a crucial factor for patient acceptance. Additionally, the need for transparent communication and ethical safeguards was also highlighted to address these concerns effectively. The integration of AI in oncology and surgeryholds significant promise for enhancing patient care and outcomes. Patients view AI as a valuable tool that can provide accurate prognoses and personalized treatments. However, addressing ethical concerns, ensuring data privacy, and building trust through transparent communication are essential for successful AI integration. Future initiatives should focus on refining AI algorithms, establishing robust ethical guidelines, and enhancing patient education to harmonize technological advancements with patient-centered care principles.

Purpose This study investigates the individual-level determinants influencing the adoption of Artificial Intelligence (AI) in the accounting profession within an emerging economy context. It explores how perceptions such as usefulness, ease of use, trust, threat, and susceptibility shape AI adoption, and examines the mediating role of accounting profit. The research is grounded in an extended Technology Acceptance Model and contextualized for Bangladesh to address the gap in AI adoption literature in developing countries. Design/methodology/approach A structured online survey was administered to 478 accounting professionals across various sectors in Bangladesh. The questionnaire included established measures adapted from prior studies and was analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The model assessed both direct and mediated paths from individual perceptions to AI adoption, with accounting profit as the mediating variable. Bias testing, robustness checks, and validity assessments were also conducted to ensure reliability of the findings. Findings Perceived usefulness, trust in AI, and perceived threat significantly influenced AI adoption. Perceived ease of use and perceived susceptibility did not show direct significance. Accounting profit was found to mediate the relationships between perceived usefulness, perceived threat, and trust with AI adoption. These results indicate that while individual perceptions matter, financial feasibility plays a critical role in actual adoption decisions within accounting contexts in emerging economies. Practical implications Accounting firms and policymakers in emerging markets must prioritize financial readiness alongside technology training. To enhance AI adoption, strategies should focus on building trust, communicating the tangible value of AI, and ensuring that AI investments align with profit goals. The findings also suggest firms should promote perceived usefulness and address risk concerns when introducing AI into accounting systems. Social implications The study highlights the socio-economic barriers that affect technology diffusion in emerging economies. As accounting professionals face trust and threat perceptions, along with resource limitations, the integration of AI requires institutional support, inclusive digital training, and clear cost-benefit communication. Broader AI adoption could enhance transparency, efficiency, and job transformation in financial services if these conditions are addressed. Originality/value This study offers a novel integration of individual-level behavioural constructs and financial performance (accounting profit) to explain AI adoption in accounting. By focussing on Bangladesh, it extends Technology Acceptance Model literature into a highly relevant yet underexplored emerging market context. The research also contributes to accounting technology literature by showing how strategic perceptions and profitability jointly shape innovation decisions in the profession.

Purpose: The general objective of the study was to investigate the role of Artificial Intelligence in revenue management and pricing strategies in hotels. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the role of Artificial Intelligence in revenue management and pricing strategies in hotels. Preliminary empirical review revealed that the integration of artificial intelligence (AI) into revenue management and pricing strategies significantly enhanced the financial performance and operational efficiency of hotels. AI's ability to process large datasets in real-time improved demand forecasting and dynamic pricing, leading to increased revenue per available room (RevPAR) and average daily rate (ADR). Additionally, AI facilitated personalized guest experiences, boosting customer satisfaction and loyalty. Despite these benefits, the study identified challenges such as high implementation costs, data privacy concerns, and the need for robust data infrastructure. Addressing these issues through strategic planning and continuous staff training was deemed essential for maximizing AI's potential in the hotel industry. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations theory, Technology Acceptance Model (TAM) and Resource Based View (RBV) may be used to anchor future studies on the role of AI in revenue management and pricing strategies in hotels. The study concluded that integrating AI into hotel revenue management and pricing strategies significantly enhances performance, contributing to both theoretical and practical advancements. It enriched the Diffusion of Innovations Theory by demonstrating factors influencing AI adoption in hospitality. Practically, it provided actionable insights for hotel managers on leveraging AI for improved key performance indicators and balancing dynamic pricing with customer satisfaction. Policy recommendations included establishing guidelines for AI implementation, enhancing data infrastructure, fostering a culture of innovation, and addressing skills gaps through training and development programs. The study emphasized the need for robust data management systems and regulatory support to facilitate AI adoption.