استكشاف مستقبل نماذج قبول التكنولوجيا في عصر الذكاء الاصطناعي

As artificial intelligence (AI) becomes increasingly integral to our lives, ensuring these systems are trustworthy and transparent is paramount. The concept of explainability has emerged as a crucial element in fostering trust within AI systems. Nevertheless, the dynamics between explainability and trust in AI are intricate and not fully comprehended. This paper delves into the nexus between explainability and trust in AI, offering perspectives on crafting AI systems that users can rely on. Through an examination of existing literature, we investigate how transparency, accountability, and human oversight influence trust in AI systems and assess how various explainability approaches contribute to trust enhancement. Utilizing a set of experiments, our research examines how different explanatory models impact users' trust in AI systems, revealing that the nature and quality of explanations have a significant influence on trust levels. Additionally, we scrutinize the balance between explainability and accuracy in AI systems, discussing its implications for the development of reliable AI. This study underscores the critical role of explainability in engendering trust in AI systems, providing guidance on the development of AI systems that are both transparent and trustworthy, thereby fostering confidence among users.

Artificial intelligence (AI) is increasingly integrated into healthcare decision-making. Public trust in AI remains a critical determinant of its acceptance and effective use. Evidence on the factors shaping trust in AI within Middle Eastern contexts, particularly Saudi Arabia, remains limited. Therefore, we aimed to identify the determinants of trust in AI for health-related decision-making and to examine a theory-informed mediation pathway in which patient satisfaction mediates the association between patient-doctor relationships and trust in AI. We conducted a cross-sectional, facility-based survey of adults in Saudi Arabia, using an electronic questionnaire distributed in four primary healthcare centers. We performed multiple linear regression to assess the association of trust in AI for health-related decision-making with patient satisfaction, patient-doctor relationships, sociodemographic characteristics, and healthcare-related factors. A mediation analysis was also employed to evaluate the indirect and direct association linking patient-doctor relationships, patient satisfaction, and trust in AI. Our findings showed that patient satisfaction was positively associated with trust in AI (β = 0.54, 95% CI: 0.18-0.90), while patient-doctor relationships showed an inverse association (β = -0.34, 95% CI: -0.48 to -0.20), possibly reflecting a greater reliance on physicians' clinical judgment and a reduced perceived need for AI-supported decision-making. Trust in AI varied across age groups, with a lower trust observed in older age categories compared with younger adults. No strong associations were observed for sex, education, body mass index, or healthcare-related factors. Patient-doctor relationship quality was indirectly associated with trust in AI via patient satisfaction (ACME = 0.138, 95% CI: 0.043-0.246), alongside a direct association with trust in AI (ADE = -0.313, 95% CI: -0.456 to -0.160). This means that patient-doctor relationships influenced trust in AI both directly and indirectly through patient satisfaction, suggesting that, while interpersonal care may reduce the reliance on AI (direct effect), enhancing patient satisfaction can partially offset this effect and promote trust in AI (indirect effect). These findings highlight that fostering patient-centered care and satisfaction may be crucial for promoting public trust in AI, which has important implications for AI governance, ethical deployment, and the design of AI-supported healthcare systems.

The rapid evolution of artificial intelligence (AI) and cloud-based accounting systems (CBAS) presents transformative opportunities for small and medium enterprises (SMEs), yet adoption remains inconsistent in developing economies due to resource constraints, infrastructural gaps, and cultural skepticism. This study examines how AI practices influence intentions to adopt CBAS among SME accountants and owners in Southern districts Khyber Pakhtunkhwa (KP) Province, Pakistan, with a focus on the moderating role of trust in AI. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and trust theory, the research proposes that AI practices (e.g., automation efficiency, predictive analytics) positively drive adoption intentions (H1), while trust in AI amplifies this relationship (H2). A cross-sectional survey of 401 SME professionals was conducted, utilizing validated scales to measure AI practices, trust in AI, and CBAS adoption intentions. Data analysis revealed a small but significant direct effect of AI practices on adoption intentions. Critically, trust in AI emerged as a significant moderator, strengthening the positive impact of AI practices on adoption decisions. This underscores trust’s role in mitigating perceived risks (e.g., data breaches, ethical concerns) and bridging the gap between technological utility and socio-cultural reservations in KP’s context. The findings extend UTAUT by integrating trust as a contextual moderator, offering a localized framework for AI adoption in high-stakes, resource-constrained environments. Practically, the study highlights the need for AI implementation strategies that prioritize transparency, ethical governance, and capacity-building initiatives tailored to SMEs’ socio-technical realities. Keywords: Artificial intelligence, cloud-based accounting, trust, SME adoption, UTAUT, Pakistan.

Trust in artificial intelligence (AI) has emerged as a critical factor influencing students' interactions with AI-supported learning environments. However, validated instruments for measuring student trust in AI within educational contexts remain limited. This study developed and validated a multidimensional scale to assess student trust in AI systems in online distance education. Scale development followed a sequential multi-stage design, including item generation based on literature review, expert evaluation for content validity, and psychometric validation through exploratory and confirmatory factor analyses. A total of 837 distance learning students participated in the study (EFA = 412; CFA = 307; validation sample = 118). Exploratory factor analysis supported a five-factor structure explaining 63.20% of the total variance. Confirmatory factor analysis with an independent sample indicated good model fit ([Formula: see text]/df = 2.17; CFI = .95; TLI = .94; RMSEA = .06). The scale demonstrated high internal consistency (α = .94; ω = .94). Multi-group confirmatory factor analysis supported metric invariance across gender, indicating comparable measurement across groups. No significant gender differences were observed, and trust in AI was not significantly associated with grade point average or system engagement frequency. These results suggest that trust represents a distinct perception that may influence how students engage with AI-supported learning environments. The resulting 21-item scale provides a reliable instrument for investigating student trust in AI and offers practical implications for the design and implementation of AI-supported learning in higher education.

The Meaning of “Explainability Fosters Trust in AI”

The rise of Artificial Intelligence (AI) in our society is becoming ubiquitous and undoubtedly holds much promise. However, AI has also been implicated in high profile breaches of trust or ethical standards, and concerns have been raised over the use of AI in initiatives and technologies that could be inimical to society. Public trust and perceptions of AI trustworthiness underpin AI systems’ social licence to operate, and a myriad of company, industry, governmental and intergovernmental reports have set out principles for ethical and trustworthy AI. To guide the responsible stewardship of AI into our society, a firm foundation of research on trust in AI to enable evidence-based policy and practice is required. However, in order to inform and guide future research, it is imperative to first take stock and understand what is already know about human trust in AI. As such, we undertake a review of 100 papers examining the relationship between trust and AI. We found a fragmented, disjointed and siloed literature with an empirical emphasis on experimentation and surveys relating to specific AI technologies. While findings suggest some convergence on the importance of explainability as a determinant of trust in AI technologies, there are still gaps between conceptual arguments and what has been examined empirically. We urge future research to take a more holistic approach and investigate how trust in different referents impacts on attitudinal and behavioural intentions. Doing so will facilitate a more nuanced understanding of what it means to develop trustworthy AI.

Artificial intelligence (AI) systems for decision-support are being introduced across multiple domains. One precondition for successful implementation is that those affected by the decisions trust the AI system. In the literature, many drivers of trust in AI have been proposed. In this study, we investigate the relative importance of six potential drivers of trust across three different decision-making domains, i.e., finance, legal, and healthcare in the Danish population. Based on an initial literature review and focus groups we designed a discrete choice study using conjoint analysis. Respondents were presented with a decision involving an AI system and asked to choose which of three possible AI systems they would trust the most, each system being characterized by a combination of levels of six attributes. All participants were presented with six choice situations. The decisions presented in the three domains were chosen to have approximately equal importance based on the focus group discussions. The three decisions were decision-making about a loan for a house purchase, decision-making about guilt in a case of assault, and diagnosis of skin cancer in primary care. Three samples of approximately 300 respondents each, representative of the Danish adult population were recruited through the panel provider Wilke. The main findings are that the most important drivers of trust differ between domains, with professional responsibility for the final decision being most important in finance and legal contexts, and precision being most important in the healthcare domain. Across all three domains, the absence of a particular trust-making feature has a strong negative effect, stronger than the presence of the same feature.

Artificial intelligence (AI) is rapidly expanding in myriad industries and systems. This study sought to investigate public trust in using AI in the criminal court process. While previous research has identified factors that influence trust in AI, such as perceived accuracy and transparency of algorithms, less is known about the role of influential leaders—such as judges—in shaping public trust in new technology. This study examined the relationship between locus of control, anthropomorphism, cultural values, and perceived trust in AI. Participants completed a survey assessing their perceptions of trust in AI in determining bail eligibility, bail fines and fees, sentencing length, sentencing fines and fees, and writing legal documents (e.g., findings and disposition). Participants were more likely to trust AI performing financial calculations rather than determining bail eligibility, sentence length, or drafting legal documents. Participants’ comfort with AI in decision-making also depended on their perceptions of judges’ trust in AI, and they expressed concerns about AI perpetuating bias and the need for extensive testing to ensure accuracy. Interestingly, no significant association was found with other participant characteristics (e.g., locus of control, anthropomorphism, or cultural values). This study contributes to the literature by highlighting the role of judges as influential leaders in shaping public trust in AI and examining the influence of individual differences on trust in AI. The findings also help inform the development of recommended practices and ethical guidelines for the responsible use of AI in the courts.

As artificial intelligence (AI) becomes ubiquitous across various fields, understanding people’s acceptance and trust in AI systems becomes essential. This review aims to identify quantitative measures used to measure trust in AI and the associated studied elements. Following the PRISMA guidelines, three databases were consulted, selecting articles published before December 2023. Ultimately, 45 articles out of 1283 were selected. Articles were included if they were peer-reviewed journal publications in English reporting empirical studies measuring trust in AI systems with multi-item questionnaires. Studies were analyzed through the lenses of cognitive and affective trust. We investigated trust definitions, questionnaires employed, types of AI systems, and trust-related constructs. Results reveal diverse trust conceptualizations and measurements. In addition, the studies covered a wide range of AI system types, including virtual assistants, content detection tools, chatbots, medical AI, robots, and educational AI. Overall, the studies show compatibility of cognitive or affective trust focus between theorization, items, experimental stimuli, and level of anthropomorphism of the systems. The review underlines the need to adapt measurement of trust in the specific characteristics of human–AI interaction, accounting for both the cognitive and affective sides. Trust definitions and measurement could be chosen depending also on the level of anthropomorphism of the systems and the context of application.

Artificial intelligence (AI) is rapidly integrating into infrastructure planning, design, construction, management, and operation. This encompasses the use of AI-powered, intelligent systems to process vast amounts of data and support human decision-making concerning urban development, architecture, transportation systems, housing, energy efficiency, and sustainability of the built environment. AI integration into the built environment has also introduced new challenges with respect to transparency, accountability, fairness, and reliability in machine decision-making, and led to concerns about algorithmic bias, privacy, and ethical deployment of technology. A key barrier to formalizing trust in AI lies in the absence of consistent definitions of the main constructs of trust, and their interplay in trust formation and calibration. Trust formation involves the initial establishment of confidence in AI systems, while calibration refers to the ongoing alignment of trust with system performance and user experiences. Understanding how these processes interact within a decision context is crucial for developing robust frameworks that enhance trust in AI. In the built environment domain, in addition to technical capabilities of the AI systems, it is also essential to consider social, ethical, and legal dimensions, to ensure that the outcomes serve the best interests of humanity. This paper provides an overview of trust in AI systems, followed by describing findings from the literature through the lens of several built environment decision-making scenarios with the goal of creating a common understanding to support future research on human trust in intelligent systems, and how it may influence the quality and timeliness of resulting decision outcomes.

Artificial Intelligence (AI) transforms Education by enhancing personalized learning, automating administrative tasks, and improving student engagement. Despite its growing presence, AI adoption among university students in Malaysia remains limited. This study investigates the level of AI application usage, identifies key factors influencing adoption, and examines the relationship between these factors and actual AI usage. Using the Technology Acceptance Model (TAM), the study focuses on perceived usefulness (PU), perceived ease of use (PEOU), and technological skills as primary determinants of AI adoption. A quantitative research approach was employed, with data collected through a structured survey from 115 undergraduate students at a university in Kedah, Malaysia. After data screening, 105 valid responses were analyzed using descriptive statistics, correlation analysis, and multiple regression analysis. The findings reveal that AI-powered chatbots and academic support tools are the most frequently used applications due to their perceived usefulness and ease of use. However, AI adoption remains moderate, primarily due to limited technological skills and concerns about AI complexity and ethical implications. The study highlights the need for AI literacy programs, improved user-friendly AI designs, and clear ethical policies on AI-assisted learning. Universities should integrate AI training into curricula, and developers should ensure AI accessibility for students with varying technical proficiencies. Future research should explore cultural attitudes, ethical concerns, and trust in AI within educational settings. By addressing these challenges, institutions can enhance AI adoption, ultimately improving student learning outcomes and academic experiences.

Patient Perspectives on Artificial Intelligence in Radiology

Artificial intelligence (AI) technologies are becoming a common trend in educational research. To reach the full potential of these technologies, a comprehensive overview is needed to establish a theoretical basis for empirical studies. Guided by the SWOT framework, Positive and negative factors of using AI technologies in K–12 education are described in the current study. The results reveal multiple strengths and opportunities. Indeed, AI excels at analyzing vast volumes of educational data, and it is adaptable enough to fit in with pre-existing frameworks of technology integration in education. AI opportunities include the possibility of creating new learning experiences without space-time limits, and skills development such as curiosity, collaboration, and critical thinking. The literature review also identifies negative factors linked to the the application of AI technologies in K–12 education, namely, weaknesses related to the lack of organizational support, practitioners’ trust in AI, and the veracity of the results. The threats revealed by the selected literature are generally linked to ethics. Based on these results, a SWOT matrix has been developed and a set of recommendations for the benefit of educators and policy-makers has been provided.

Artificial intelligence (AI) can provide many benefits in healthcare, including rapid and effective treatment options. However, previous research on human–computer interactions has demonstrated that people are reluctant to accept AI. This study compared individuals’ trust in AI with their trust in doctors regarding medical treatment and explored whether people trust an AI system that understands and suggests their desired treatments. We conducted an online experiment (N = 415) adopting a scenario method. The participants were asked to imagine receiving a medical examination and being prescribed medicine by a physician or an AI system. Participants trusted the AI system less than a doctor, even when the AI system learned and suggested their desired treatment. This finding confirms that people are reluctant to trust AI even if it performs at the level of a human doctor, thereby strengthening the existent literature that shows people prefer human doctors.

The rapid diffusion of artificial intelligence (AI) in marketing has reshaped how consumers interact with digital content and evaluate ethical aspects of firms. The present study examines how familiarity with and trust in AI shape consumers’ acceptance of AI-based advertising and, in turn, their ethical purchasing behavior. Data were collected from 505 Greek consumers through an online survey and analyzed using hierarchical and logistic regression models. Reliability and validity tests confirmed the robustness of the measurement instruments. The results show that familiarity with AI technologies significantly enhances trust and ethical confidence toward AI systems. In turn, trust in AI strongly predicts the consumers’ acceptance of AI-driven advertising, while acceptance positively affects ethical consumption intentions. The findings also confirm a mediating relationship, indicating that acceptance of AI-based advertising transmits the effect of AI rust to ethical consumption. By integrating ethical and technological dimensions within a single behavioral model, the study provides a more comprehensive view of how consumers form attitudes toward AI-enabled marketing. Overall, the findings highlight that transparent and responsible AI practices can strengthen brand credibility, foster ethical engagement, and support more sustainable consumer choices.