AI’s Capability in Understanding Text Context: Students’ Experiences

SummaryThe recent sale of an artificial intelligence (AI)-generated portrait for $432,000 at Christie's art auction has raised questions about how credit and responsibility should be allocated to individuals involved and how the anthropomorphic perception of the AI system contributed to the artwork's success. Here, we identify natural heterogeneity in the extent to which different people perceive AI as anthropomorphic. We find that differences in the perception of AI anthropomorphicity are associated with different allocations of responsibility to the AI system and credit to different stakeholders involved in art production. We then show that perceptions of AI anthropomorphicity can be manipulated by changing the language used to talk about AI—as a tool versus agent—with consequences for artists and AI practitioners. Our findings shed light on what is at stake when we anthropomorphize AI systems and offer an empirical lens to reason about how to allocate credit and responsibility to human stakeholders.

Artificial intelligence (AI) promises to change health care, with some studies showing proof of concept of a provider-level performance in various medical specialties. However, there are many barriers to implementing AI, including patient acceptance and understanding of AI. Patients' attitudes toward AI are not well understood. We systematically reviewed the literature on patient and general public attitudes toward clinical AI (either hypothetical or realised), including quantitative, qualitative, and mixed methods original research articles. We searched biomedical and computational databases from Jan 1, 2000, to Sept 28, 2020, and screened 2590 articles, 23 of which met our inclusion criteria. Studies were heterogeneous regarding the study population, study design, and the field and type of AI under study. Six (26%) studies assessed currently available or soon-to-be available AI tools, whereas 17 (74%) assessed hypothetical or broadly defined AI. The quality of the methods of these studies was mixed, with a frequent issue of selection bias. Overall, patients and the general public conveyed positive attitudes toward AI but had many reservations and preferred human supervision. We summarise our findings in six themes: AI concept, AI acceptability, AI relationship with humans, AI development and implementation, AI strengths and benefits, and AI weaknesses and risks. We suggest guidance for future studies, with the goal of supporting the safe, equitable, and patient-centred implementation of clinical AI.

Since the pandemic, the study purposed to derive improvements of the educational program through pilot operation for youth in Korea before local application to disseminate artificial intelligence smart poultry farm in Tanzania. For this purpose, we developed an artificial intelligence smart poultry farm appropriate technology camp program, and 49 high school students participated in this pilot study for two days. After the pilot operation, a questionnaire was conducted to measure students' perception and experience of artificial intelligence through this appropriate technology camp. The questionnaire consisted of 22 questions measuring the perception of artificial intelligence by the Likert scale and 1 question describing the participation experience. Students' perception of artificial intelligence was primarily composed of three areas: value perception (6 questions), efficacy (4 questions), and risk perception (12 questions). As a result, students' value perception (M=4.30, SD=.491) and efficacy (M=4.42, SD=.487) to artificial intelligence were high. However, the risk perception (M=3.42, SD=1.023) was relatively low. In addition, through this appropriate technology camp, students' motivation to learn artificial intelligence improved, their interest in careers and jobs related to artificial intelligence increased, and they had a negative perception and a positive impact on society due to artificial intelligence.

A Review of: Subaveerapandiyan, A., Sunanthini, C., & Amees, M. (2023). A study on the knowledge and perception of artificial intelligence. IFLA Journal, 49(3), 503–513. https://doi.org/10.1177/03400352231180230 Objective – To assess the knowledge, perception, and skills of library and information science (LIS) professionals related to artificial intelligence (AI). Design – 45 statements were distributed to 469 LIS professionals via Google Forms to collect primary data. 245 participants responded to the structured questionnaire. Setting – University and college libraries in Zambia. Subjects – Zambian library and information science professionals.Methods – A descriptive approach was employed for the study. Data was gathered via a questionnaire. “The objective was to assess the statistical relationship between the knowledge, perception, and skills of LIS professionals (the independent variables) and AI (the dependent variable)” (Subaveerapandiyan et al., p. 506). The survey used a 5-point Likert scale with (1) strongly disagree being the lowest score and (5) strongly agree the highest. Means and standard deviations are included in data display tables. Thematic analysis was employed to analyze the data. SPSS was used for data analysis.Main Results – Survey results are presented in three tables. Table 1, “Awareness of AI among LIS professionals,” contains 21 statements related to AI use in various library environments and services, including reference (finding articles and citations, content summarization, detecting misinformation), circulation of library materials, security and surveillance, character recognition and document preservation, research data management, language translation, and others. The authors note that 44.1 percent of the respondents agreed that “AI is essential for the effectiveness and efficiency of library service delivery, enabling libraries to enhance and offer dynamic services for their users” (Subaveerapandiyan et al., 2023, p. 506). Table 2, “Perception of AI among LIS professionals,” contains 10 statements. Over 85 percent of respondents either strongly agreed or agreed that AI “makes library staff lazy” while 58.1 percent either strongly agreed or agreed that AI is a “threat to librarians’ employment” (Subaveerapandiyan et al., 2023, p. 506). The authors note that the “respondents also indicated barriers to the adoption of AI in libraries, such as the lack of LIS professionals’ skills and budgetary constraints” (Subaveerapandiyan et al., 2023, p. 506). Table 3 lists 13 competencies required by library professionals in the AI era. The majority of the respondents (an average of 65 percent) were in strong agreement that “electronic communication, hardware and software, Internet applications, computing and networking, cyber security and network management, data quality control, data curation, database management … are necessary competencies required by LIS professionals for them to be proficient in AI” (Subaveerapandiyan et al., 2023, p. 506).

This study aims to examine neonatal intensive care unit (NICU) nurses' perceptions of artificial intelligence (AI) technologies, particularly language models, and their impact on nursing practices. AI is rapidly spreading in healthcare, transforming nursing practice. Understanding the role of AI in NICUs in the discharge process is crucial for understanding nurses' perceptions of these technologies. The qualitative, phenomenological study used semi-structured interviews. Data were collected in a public hospital in Gaziantep from January to June 2024. Fifteen NICU nurses participated. Data were analyzed using content analysis. Most nurses found AI to be a valuable tool for saving time and simplifying information delivery in clinical processes. However, concerns were raised about AI potentially reducing human interaction and weakening the use of professional judgment. Serious concerns about AI's reliability and ethical implications were also expressed. AI is considered a potentially supportive tool in nursing practice, but its integration must consider the ethical implications and impact on the use of professional judgment. Nursing is based on human interactions and AI should be considered an additive tool to enhance care. AI integration in nursing requires careful and balanced implementation. Future research should delve deeper into the ethical dimensions of AI and its long-term effects on nursing practices.

ObjetivoLa IA y el análisis de big data pueden potenciar aún más las características automatizadas e inteligentes de los servicios de turismo y hostelería. Sin embargo, también plantea nuevos retos a la gestión de los recursos humanos. Este estudio pretende explorar los efectos directos e indirectos de la percepción de la IA por parte de los empleados sobre la resiliencia profesional y el aprendizaje informal, así como el efecto mediador de la resiliencia profesional.Diseño/metodología/enfoqueEn este trabajo se propone un modelo teórico de percepción de la IA, resiliencia profesional y aprendizaje informal con la IA percibida como variable antecedente, la resiliencia profesional como variable mediadora y el aprendizaje informal como variable endógena. Dirigidos a los empleados que trabajan con IA, se recogieron un total de 472 datos válidos. Los datos se analizaron mediante un modelo de ecuaciones estructurales (SEM) con el software AMOS.ResultadosLos Resultados indicaron que la percepción de la IA por parte de los empleados contribuye positivamente a la resiliencia profesional y al aprendizaje informal. Aparte del efecto directo sobre el aprendizaje informal, la resiliencia profesional también media en la relación entre la percepción de la IA y el aprendizaje informal.Originalidad/valorLos Resultados de la investigación proporcionan implicaciones tanto teóricas como prácticas al revelar el impacto de la percepción de la IA en el desarrollo profesional de los empleados, las actividades de aprendizaje, explicar cómo la IA transforma la naturaleza del trabajo y el desarrollo profesional, y arrojar luz sobre la gestión de los recursos humanos en el ámbito del turismo y la hostelería.

Introduction: As artificial intelligence (AI) becomes increasingly integrated into various professional fields, understanding its impact on pharmacy education is crucial. This study explores pharmacists' perceptions of AI's role in enhancing educational and professional practices, particularly focusing on the generation of educational content and analytical tasks. Objectives: The primary objective was to assess pharmacists' concerns and perceived benefits regarding the use of AI in pharmacy education, examining variations across different age groups and years of practice. Methods: A cross-sectional survey was completed by 446 pharmacists who actively precept pharmacy residents and students. Respondents practiced across 35 states with over half (53.4%) being in Ohio. The survey included items on concerns about AI's quality and accuracy, human interaction, plagiarism, and its potential benefits in data analysis and research literature summarization. Responses were analyzed to identify trends across demographic categories, including age and years in practice. Results: Of the respondents, 67.9% expressed concerns about the quality and accuracy of AI-generated content, while 50.9% were concerned about plagiarism. Younger pharmacists (73.8% of those aged 20-29) showed heightened concern about accuracy compared to older groups (56.8% of those aged 60+). In contrast, 57.8% of respondents recognized AI's potential benefits for data analysis, with experienced pharmacists (>20 years in practice) being more likely to see these advantages (62.2%). Conclusion: The findings indicate a need for targeted educational strategies to address AI literacy and ethical use in pharmacy education. Integrating AI tools that support educational objectives while addressing these concerns could enhance the efficacy and acceptance of AI in pharmacy practice. Further research should explore the development of training programs that align with the evolving expectations and technological competencies of different pharmacist demographics.

As artificial intelligence (AI) transforms society, understanding factors that influence AI receptivity is increasingly important. The current research investigates which types of consumers have greater AI receptivity. Contrary to expectations revealed in four surveys, cross-country data and six additional studies find that people with lower AI literacy are typically more receptive to AI. This lower literacy–greater receptivity link is not explained by differences in perceptions of AI's capability, ethicality, or feared impact on humanity. Instead, this link occurs because people with lower AI literacy are more likely to perceive AI as magical and experience feelings of awe in the face of AI's execution of tasks that seem to require uniquely human attributes. In line with this theorizing, the lower literacy–higher receptivity link is mediated by perceptions of AI as magical and is moderated among tasks not assumed to require distinctly human attributes. These findings suggest that companies may benefit from shifting their marketing efforts and product development toward consumers with lower AI literacy. In addition, efforts to demystify AI may inadvertently reduce its appeal.

Purpose : Artificial intelligence (AI) has potentials to significantly transform a role of nurses and revolutionise practices of healthcare systems. The aim of this study is to identify influences of AI Knowledge, Perception, and Acceptance Attitude on nursing students’ Intentions to use AI-based healthcare technologies. Methods : The participants included 241 nursing students in Gyeonggi-do and Jeollanam-do, with data collected from 30 May to 30 June 2022 using self-reported questionnaires. The data were analyzed using the SPSS/WIN 25.0 program, with independent t-tests, one-way ANOVA, Pearson’s correlations, and multiple linear regression. Results : The results revealed that AI Knowledge positively correlated with AI perception(r＝.40, p＜.001), and Acceptance Attitude toward AI(r＝.17, p＝.007). AI perception positively correlated with AI Acceptance Attitude(r＝.53, p ＜.001), and Intentions to use AI(r＝.46, p＜.001). AI Acceptance Attitude also positively correlated with Intentions to use AI(r＝.52, p＜.001). The factors influencing Intentions to use AI-based healthcare technologies included AI Perception(β＝.30) and Acceptance Attitude AI Acceptance Attitude(β＝.38). The adjusted R2 was .318. Conclusions : It is necessary to develop systematic educational programs on AI technologies and form an organizational culture to improve nursing clinical competency and professionalism for nursing students in healthcare setting.

Patient perspectives on artificial intelligence in healthcare: A global scoping review of benefits, ethical concerns, and implementation strategies.

Apart from the fact that human-like robots are still one of the most interesting topics in science fiction, artificial intelligence (AI) continues to develop rapidly as a popular phenomenon for all sectors. Although the idea that this rapid rise of AI means the rise of humanity has been voiced by many, the point of how AI will affect humanity continues to raise doubts in certain parts of the society. In this study, it is aimed to determine the perceptions of middle school students, which are a part of the future of humanity, towards the concept of AI, on which many discussions have been made, through metaphors. The sample consisted of 339 seventh and eighth grade students of four secondary schools in the central districts of Afyonkarahisar and Izmir in the 2019-2020 academic year. This study used a qualitative approach utilizing metaphor analysis as a research tool to investigate phenomena. Participants were asked the complete the sentence “Artificial intelligence is like.................., because ..................” Data were analyzed using content analysis. Participants’ metaphors for AI were grouped under 11 categories: smart, brain, nature, security, humanistic, the dilemma of good and evil, service, object, technology, life, and time. The data obtained showed that the participants generally used positive metaphors to describe AI, that is, they had positive perceptions about AI. However, in this study, which focused on the AI perceptions of middle school students, all of the metaphors collected under 11 conceptual categories containing positive and negative perceptions were examined and interpreted separately. It is thought that educational computer systems can be designed to shape students' perceptions of AI. Teachers can consider students' perceptions of AI by using AI-assisted teaching and designing content to enhance students' learning skills.

Artificial intelligence (AI) has alarmed the society of Taiwan believing it is responsible for potential surveillance, data theft and abuse, and other privacy infringements. By adopting the theory of motivated reasoning, this study explores how Taiwanese people’s perceptions of AI are affected by their institutional trust, attitudes toward the government and corporations, which are the two most common sponsors of scientific development. First, findings establish that respondents’ science trust in AI is made up of perceptions of AI and its science community, and they have lower faith in the AI science community than in AI alone. Second, the perceptions of both AI and its science community are positively associated with trust in government and corporations. Third, scientific news has a direct bearing on AI trust, but not on either government or corporation trust. By contrast, political news has no effect on either trust in AI or its science community, yet trust in government and corporations mediates the influence of political news on trust in AI and its science community. Finally, demographic variables hardly predict trust in AI, AI science community, government, and corporations, but education and gender are directly related to news consumption, which further influences institutional and science trust.

The successful adoption of artificial intelligence (AI) in healthcare relies on healthcare professionals' perceptions of its usefulness and their preparedness to integrate it into their practice. This study explores factors influencing these perceptions, focusing on demographic characteristics, computer skills, and AI knowledge. A cross-sectional study was conducted among healthcare professionals in Saudi Arabia between October 2023 and May 2024. Data were collected using a questionnaire that assessed perceptions of AI's professional impact (FACTOR 1) and preparedness to use AI (FACTOR 2) by using the Shinners Artificial Intelligence Perception (SHAIP) scale. Mann-Whitney tests examined differences in FACTOR 1 and FACTOR 2 by computer skills and AI knowledge. Multivariable linear regression identified predictors of these perceptions. Of the 359 participants, 76.60% reported high computer skills, while 62.12% reported low AI knowledge. Participants with higher computer skills and greater AI knowledge scored significantly higher on both FACTOR 1 and FACTOR 2 (p < 0.05). Gender, involvement in health informatics, and experience with healthcare technology emerged as significant predictors. Female participants reported significantly lower perceptions of AI's professional impact compared to males (β = -0.253, p = 0.020). Participants working in health informatics demonstrated a significantly better perception of AI's professional impact, while professionals with more than five years of experience using healthcare technology scored higher on both factors. In conclusion, digital competencies and AI knowledge are critical for shaping healthcare professionals' perceptions of AI. Targeted interventions and policy to enhance these skills are essential to promote equitable and effective AI adoption in healthcare settings.

Numerous studies over the past 30 years have examined the relationship between social capital (SC) and information and communication technology (ICT). However, few studies have examined the association between artificial intelligence (AI) and SC. This study addresses this gap using a Web survey (n = 5000) carried out in the Tokyo metropolitan area in Japan in 2018. The survey included questions on ICT literacy and SC (networks, trust, norms of reciprocity), as well as questions on perceptions of AI including its impact on society. Based on the survey, we extracted four SC factors: cognitive SC, and three forms of structural SC, namely contacts with others, group participations, and SC at work place. We found a statistically significant positive association between SC and positive perceptions of AI through ITC literacy. SC is indirectly associated with AI perception by enhancing ICT literacy, and then ICT literacy enhances AI perception. This indirect effect seems to be mainly caused by two types of structural SC: SC through group participations and SC at work place. Besides this indirect effect, SC has direct effect on AI perception. Cognitive SC has direct positive association with AI perception, whereas structural SC in the form of contacts with others was negatively associated with AI perception. Thus, structural SC has an ambivalent effect on AI perception. Structural SC through group participation as well as SC at work place may work for the positive perceptions of AI through ICT literacy, while those with higher level of contacts with others tend to be cautious toward AI. Both cognitive SC and structural SC assume important roles for the smooth transition into the AI era. Policy makers should be aware of the difference in the way each of these SC forms affects AI perception. SC seems to have mainly promotional impact on the AI perception. However, the precautionary function of SC should not be put on the back burner for the sound social acceptance of AI. In any case, SC assumes an important role in the creation of AI perception.

BackgroundGlobally, young adults with mental health problems struggle to access appropriate and timely care, which may lead to a poorer future prognosis. Artificial intelligence (AI) is suggested to improve the quality of mental health care through increased capacities in diagnostics, monitoring, access, advanced decision-making, and digital consultations. Within mental health care, the design and application of AI solutions should elucidate the patient perspective on AI.ObjectiveThe aim was to explore the perceptions of AI in mental health care from the viewpoint of young adults with experience of seeking help for common mental health problems.MethodsThis was an interview study with 25 young adults aged between 18 and 30 years that applied a qualitative inductive design, with content analysis, to explore how AI-based technology can be used in mental health care.ResultsThree categories were derived from the analysis, representing the participants’ perceptions of how AI-based technology can be used in care for mental health problems. The first category entailed perceptions of AI-based technology as a digital companion, supporting individuals at difficult times, reminding and suggesting self-care activities, suggesting sources of information, and generally being receptive to changes in behavior or mood. The second category revolved around AI enabling more effective care and functioning as a tool, both for the patient and health care professionals (HCPs). Young adults expressed confidence in AI to improve triage, screening, identification, and diagnosis. The third category concerned risks and skepticism toward AI as a product developed by humans with limitations. Young adults voiced concerns about security and integrity, and about AI being autonomous, incapable of human empathy but with strong predictive capabilities.ConclusionsYoung adults recognize the potential of AI to serve as personalized support and its function as a digital guide and companion between mental health care consultations. It was believed that AI would function as a support in navigating the help-seeking process, ensuring that they avoid the “missing middle” service gap. They also voiced that AI will improve efficiency in health care, through monitoring, diagnostic accuracy, and reduction of the workload of HCPs, while simultaneously reducing the need for young adults to repeatedly tell their stories. Young adults express an ambivalence toward the use of AI in health care and voice risks of data integrity and bias. They consider AI to be more rational and objective than HCPs but do not want to forsake personal interaction with humans. Based on the results of this study and young adults’ perceptions of the monitoring capabilities of AI, future studies should define the boundaries regarding information collection responsibilities of the health care system versus the individuals’ responsibility for self-care.