Artificial Intelligence Usage Research Articles

Artificial Intelligence

Applications of artificial intelligence (AI) are able to optimise our lives par excellence, and it is clear that this will only increase as time passes. In many ways, this is very promising, but the forms that AI takes in our society have also sparked many concerns about dehumanisation. What is often recognised is that AI systems implicitly exert social power relations—whether intentionally or not, as might be the case with bias—such that the danger would be gone if only we improved our models and uncovered this hidden realm of intentional oppression. However, these views overlook the possibility that detrimental consequences may also arise precisely because AI is able to attain favourable goals flawlessly. This problem of adverse side effects, which are strictly accidental to the goals we set for AI to effectuate, is explored through the notion of “non-intentional dehumanisation”. To articulate this phenomenon, this essay consists of two parts. The first part will establish how naive AI usage presents a paradigmatic case of this problem. In the second part, we will argue that these issues occur in a two-fold fashion; not only does AI risk inducing harm to the “used-upon”, but also to the user. It is with this conceptual model that awareness may be brought to the counter side of our ready acceptance of AI solutions.

Cheat sites and artificial intelligence usage in online introductory physics courses: What is the extent and what effect does it have on assessments?

As a result of the pandemic, many physics courses moved online. Alongside, the popularity of Internet-based problem-solving sites and forums rose. With the emergence of large language models, another shift occurred. One year into the public availability of these models, how has online help-seeking behavior among introductory physics students changed, and what is the effect of different patterns of online resource usage? In a mixed-method approach, we investigate student choices and their impact on assessment components of an online introductory physics course for scientists and engineers. We find that students still mostly rely on traditional Internet resources and that their usage strongly influences the outcome of low-stake unsupervised quizzes. We empirically found distinct clusters of help-seeking and resource-usage patterns among the students; the impact of students’ cluster membership on the supervised assessment components of the course, however, is nonsignificant. Published by the American Physical Society 2024

The great detectives: humans versus AI detectors in catching large language model-generated medical writing

BackgroundThe application of artificial intelligence (AI) in academic writing has raised concerns regarding accuracy, ethics, and scientific rigour. Some AI content detectors may not accurately identify AI-generated texts, especially those that have undergone paraphrasing. Therefore, there is a pressing need for efficacious approaches or guidelines to govern AI usage in specific disciplines.ObjectiveOur study aims to compare the accuracy of mainstream AI content detectors and human reviewers in detecting AI-generated rehabilitation-related articles with or without paraphrasing.Study designThis cross-sectional study purposively chose 50 rehabilitation-related articles from four peer-reviewed journals, and then fabricated another 50 articles using ChatGPT. Specifically, ChatGPT was used to generate the introduction, discussion, and conclusion sections based on the original titles, methods, and results. Wordtune was then used to rephrase the ChatGPT-generated articles. Six common AI content detectors (Originality.ai, Turnitin, ZeroGPT, GPTZero, Content at Scale, and GPT-2 Output Detector) were employed to identify AI content for the original, ChatGPT-generated and AI-rephrased articles. Four human reviewers (two student reviewers and two professorial reviewers) were recruited to differentiate between the original articles and AI-rephrased articles, which were expected to be more difficult to detect. They were instructed to give reasons for their judgements.ResultsOriginality.ai correctly detected 100% of ChatGPT-generated and AI-rephrased texts. ZeroGPT accurately detected 96% of ChatGPT-generated and 88% of AI-rephrased articles. The areas under the receiver operating characteristic curve (AUROC) of ZeroGPT were 0.98 for identifying human-written and AI articles. Turnitin showed a 0% misclassification rate for human-written articles, although it only identified 30% of AI-rephrased articles. Professorial reviewers accurately discriminated at least 96% of AI-rephrased articles, but they misclassified 12% of human-written articles as AI-generated. On average, students only identified 76% of AI-rephrased articles. Reviewers identified AI-rephrased articles based on ‘incoherent content’ (34.36%), followed by ‘grammatical errors’ (20.26%), and ‘insufficient evidence’ (16.15%).Conclusions and relevanceThis study directly compared the accuracy of advanced AI detectors and human reviewers in detecting AI-generated medical writing after paraphrasing. Our findings demonstrate that specific detectors and experienced reviewers can accurately identify articles generated by Large Language Models, even after paraphrasing. The rationale employed by our reviewers in their assessments can inform future evaluation strategies for monitoring AI usage in medical education or publications. AI content detectors may be incorporated as an additional screening tool in the peer-review process of academic journals.

Diversity, Equity, and Inclusion, and the Deployment of Artificial Intelligence Within the Department of Defense

Artificial Intelligence (AI) adoption has seen substantial growth across industries. This paper explores the escalating use of AI within the United States Department of Defense (DoD) and the implications that diversity, equity, and inclusion (DEI) have on Service members and Civilians across the Department. More specifically, this paper explores the DEI considerations within AI technologies on individual, team, and Department readiness. The DoD's AI usage spans various strategic and operational capabilities, however this paper explores two critical domains: healthcare and recruitment. In healthcare, AI offers the promise of early disease detection, enhanced diagnostic capabilities, and streamlined administrative processes. However, potential biases stemming from homogenous training data threaten the accuracy and reliability of these systems, jeopardizing Service member health and eroding trust in AI-assisted medical decision-making and potentially the DoD at large. In recruitment, while AI promises efficiency in identifying ideal candidates, its deployment can perpetuate biases, especially when the training data used is not representative of all demographics. Despite efforts to design "unbiased" systems by excluding demographic data, such strategies may inadvertently overlook the unique challenges faced by marginalized communities, further entrenching existing disparities. Both case studies underscore the importance of considering DEI in the development and deployment of AI systems. As the DoD continues to integrate AI into its operations, this paper’s recommendations stress the necessity of continuous DEI assessment to ensure that AI serves as an asset rather than a liability. The authors recommend the following: 1. Data diversity & review 2. Continuous monitoring and calibration 3. Stakeholder engagement 4. Adoption of DEI requirements within Ethical AI Frameworks 5. Further research

Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior

Although previous studies have highlighted the problematic artificial intelligence (AI) usage behaviors in educational contexts, such as overreliance on AI, no study has explored the antecedents and potential consequences that contribute to this problem. Therefore, this study investigates the causes and consequences of AI dependency using ChatGPT as an example. Using the Interaction of the Person-Affect-Cognition-Execution (I-PACE) model, this study explores the internal associations between academic self-efficacy, academic stress, performance expectations, and AI dependency. It also identifies the negative consequences of AI dependency. Analysis of data from 300 university students revealed that the relationship between academic self-efficacy and AI dependency was mediated by academic stress and performance expectations. The top five negative effects of AI dependency include increased laziness, the spread of misinformation, a lower level of creativity, and reduced critical and independent thinking. The findings provide explanations and solutions to mitigate the negative effects of AI dependency.

Artificial Intelligence and Business School Students’ Performance

This study is intended to find a relationship between Artificial Intelligence and Business School Students’ Performance. Data was collected from 150 Business School students at KIMEP University using questionnaires in Spring semester of 2024. Questionnaires were distributed two times: at the early beginning of the semester and at the end of the semester. During the semester, students thoroughly studied Artificial Intelligence and its functions and capacity. Additional classes on Artificial Intelligence tools were offered to students. Based on findings, the study proposes to address some positive implications of Artificial Intelligence integration in university teaching and learning globally. Artificial Intelligence has positive impact on students’ learning process. However, special ethical rules and standards must be developed and introduced due to the fact that there are increasing concerns over the risks associated with Artificial Intelligence usage and how it might affect human being further.

Primary school students’ perceptions of artificial intelligence – for good or bad

AbstractSince the end of 2022, global discussions on Artificial Intelligence (AI) have surged, influencing diverse societal groups, such as teachers, students and policymakers. This case study focuses on Swedish primary school students aged 11–12. The aim is to examine their cognitive and affective perceptions of AI and their current usage. Data, comprising a pre-test, focus group interviews, and post-lesson evaluation reports, were analysed using a fusion of Mitcham’s philosophical framework of technology with a behavioural component, and the four basic pillars of AI literacy. Results revealed students’ cognitive perceptions encompassing AI as both a machine and a concept with or without human attributes. Affective perceptions were mixed, with students expressing positive views on AI’s support in studies and practical tasks, alongside concerns about rapid development, job loss, privacy invasion, and potential harm. Regarding AI usage, students initially explored various AI tools, emphasising the need for regulations to slow down and contemplate consequences. This study provides insights into primary school students perceptions and use of AI, serving as a foundation for further exploration of AI literacy in education contexts and considerations for policy makers to take into account, listening to children’s voices.

Abstract PO3-19-11: CINDERELLA Clinical Trial (NCT05196269): using artificial intelligence-driven healthcare to enhance breast cancer locoregional treatment decisions

Abstract Background. Breast cancer treatment has improved overall survival rates, with different locoregional approaches offering patients similar locoregional control but variable aesthetic outcomes that may lead to disappointment and poor quality of life (QoL). There are no standardized methods for informing patients of the different therapies prior to intervention, nor validated tools for evaluation of aesthetics and patients' expectations. The CINDERELLA Project is based on years of research and developments of new healthcare technologies by various partners, aimed to provide an artificial intelligence (AI) tool to aid shared decision-making by showing breast cancer patients the predicted aesthetic outcomes of their locoregional treatment. The clinical trial will evaluate the use of this tool within an AI cloud-based platform approach (CINDERELLA App) versus a standard approach. We anticipate that the CINDERELLA App will lead to improved satisfaction, psychosocial well-being and health-related QoL while maintaining the quality of care and providing environmental and economic benefits. Trial design. CINDERELLA is an international multicentric interventional randomized controlled open-label clinical trial. Using the CINDERELLA App, the AI and Digital Health arm will provide patients with complete information about the proposed types of locoregional treatments and photographs of similar patients previously treated with the same techniques. The Control arm will follow the standard approach of each clinical site. Randomization will be conducted online using the digital health platform CANKADO, ensuring a balanced distribution of participants between the two groups. CANKADO is the underlying platform through which physicians control the patients' app content and conduct all data collection. Privacy, data protection and ethical principles in AI usage were taken into account. Eligibility criteria. Patients diagnosed with primary breast cancer without evidence of systemic disease. All patients must sign an informed consent and be able to use a web-based app autonomously or with home-based support. Specific aims. Primary objective: to assess the levels of agreement among patients' expectations regarding the aesthetic outcome before and 12 months after locoregional treatment. The trial will also evaluate the aesthetic outcome level of agreement between the AI evaluation tool and self-evaluation. Secondary objectives: health-related QoL (EQ-5D-5L and BREAST-Q ICHOM questionnaires) and resource consumption (e.g., time spent in the hospital, out-of-pocket expenses). The questionnaires and photographs will be applied prior to any treatment, at wound healing, at 6 and 12 months following the completion of locoregional therapy. Statistical methods. Wilcoxon signed rank test will be used to assess the intervention's impact on the agreement level between expectations and obtained results. Weighted Cohen's kappa will be calculated to measure the improvement in classifying aesthetic results with intervention. Statistical tests and/or bootstrap techniques will compare results between arms. A similarity measure will be calculated between self-evaluation and outcome obtained with the AI tool for each participant, and a beta regression model will be used to analyze the intervention's effect. Secondary objectives will be evaluated by scoring questionnaires based on provided guidelines. Target accrual. The clinical trial, led by Champalimaud Clinical Centre, will enroll a minimum of 515 patients in each arm between July 2023 and January 2025. Recruitment is currently open at five study sites in Germany, Israel, Italy, Poland and Portugal. The clinical trial is still open for further international study sites. Funding. European Union grant HORIZON-HLTH-2021-DISEASE-04-04 Agreement No. 101057389. Citation Format: Eduard-Alexandru Bonci, Orit Kaidar-Person, Marília Antunes, Oriana Ciani, Helena Cruz, Rosa Di Micco, Oreste Davide Gentilini, Nicole Rotmensz, Pedro Gouveia, Jörg Heil, Pawel Kabata, Nuno Freitas, Tiago Gonçalves, Miguel Romariz, Helena Montenegro, Hélder P. Oliveira, Jaime S. Cardoso, Henrique Martins, Daniela Lopes, Marta Martinho, Ludovica Borsoi, Elisabetta Listorti, Carlos Mavioso, Martin Mika, André Pfob, Timo Schinköthe, Giovani Silva, Maria-Joao Cardoso. CINDERELLA Clinical Trial (NCT05196269): using artificial intelligence-driven healthcare to enhance breast cancer locoregional treatment decisions [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-19-11.

Embracing AI with Integrity: Recommendations for Authors and Reviewers at American Business Review

As Editors of the American Business Review (ABR), we are navigating a complex landscape as the rapid integration of Artificial Intelligence (AI) into academic research unfolds. This digital transformation era offers remarkable opportunities yet poses significant challenges, particularly in educational contexts. As teachers, we've all observed a surge in AI usage among students where outputs often appear coherent initially but may lack depth or relevance to the class content. Many of these instances underscore critical aspects of AI, such as the "black box" problem, where the decision-making processes of AI systems are opaque, making it difficult for users to understand how conclusions are drawn.

Navigating Intimate Partner Violence: A Comparative Analysis Between ACOG and ChatGPT on Patient Education [ID 2683552

INTRODUCTION: With the surge in artificial intelligence usage, there is an increasing need to assess its applicability in conveying public health information. We compare the readability and quality of responses between ChatGPT and the American College of Obstetricians and Gynecologists (ACOG) in answering frequently asked questions (FAQs) on intimate partner violence (IPV). METHODS: Twelve questions from ACOG's “Intimate Partner Violence” FAQs were posed to ChatGPT-3.5 (July 19 Version). Readability and grade-level scores were determined. The quality of responses were also graded by two obstetrician–gynecologists using a 1–4 scale, where 1 represents a comprehensive response and 4 indicates an incorrect response. Statistical analysis utilized a two-tailed t-test. A weighted Cohen’s kappa coefficient evaluated interrater reliability. RESULTS: Mean readability favored ACOG over ChatGPT, but only the Coleman Liau Index was statistically significant (ACOG: 12.71; ChatGPT: 15.76; P=.003). Other readability measures demonstrated no significant differences. The ACOG responses were graded with an average of 1.29, alongside a Cohen’s kappa coefficient of 0.375, implying fair agreement between graders. All ChatGPT’s responses were graded a 1 with a Cohen’s kappa coefficient of 1, indicating perfect agreement. The difference in grades between ACOG and ChatGPT was statistically significant (P=.013). CONCLUSION: The ACOG FAQ responses are relatively equivalent in readability when compared to ChatGPT-generated responses with only one statistically significant difference across the indices. However, ChatGPT's responses were more comprehensive and accurate. Although patients can use ACOG as a source, ChatGPT provides equivalently clear and more comprehensive information on IPV.

Artificial Intelligence in the Classroom: Revolutionizing English Language Teaching

This qualitative study investigates the integration of Artificial Intelligence (AI) in English Language Teaching (ELT) within university settings, with a specific focus on the viewpoints and experiences of university-level English teachers in Nepal. The research was conducted in Lumbini Province, Nepal, where 14 university-level English teachers were purposively selected from seven constituent campuses. Through unstructured smartphone interviews, participants shared insights on AI integration in ELT. The findings reveal a diverse range of expectations and concerns among university-level English teachers regarding AI’s role in language instruction. While participants expressed optimism about AI’s potential to revolutionize language learning through personalized experiences and immediate feedback, they also voiced apprehensions. These concerns encompassed job displacement, potential erosion of human interaction, and ethical implications related to AI usage. To address these challenges, participants employed various strategies. They navigated ethical considerations by raising awareness, engaging in reflection, and advocating for ethical guidelines. The study emphasizes the importance of a balanced approach to AI integration-one that harnesses its promises while addressing potential pitfalls. Responsible and inclusive AI usage in language education necessitates thoughtful consideration of both benefits and challenges.

تطبيقات الإدارة للذكاء الاصطناعي في اتخاذ القرارات الإدارية دراسة تطبيقية على شركة الاتصالات السعودية

The study aimed to investigate the impact of artificial intelligence, with its dimensions (genetic algorithms, intelligent agents, expert systems), on managerial decision-making, with its aspects (decision-making stages, decision-making determinants, decision-making elements, participation in decision-making) in Saudi Telecommunications Company. To achieve the study objectives, a descriptive-analytical approach was followed. The study population included all employees within Saudi Telecommunications Company. Due to the difficulty of conducting a comprehensive census of all individuals in the study population, the study used a simple random sampling method by distributing the questionnaire link across all social media platforms used by the study population. The study sample comprised 464 individuals who responded to the questionnaire. The study results revealed a statistically significant positive impact of the level of artificial intelligence usage on managerial decision-making in Saudi Telecommunications Company. A strong positive correlation was found between the usage of artificial intelligence and managerial decision-making in the company. The study recommended focusing on providing financial resources, logistical capabilities, and support for the development of artificial intelligence applications that can help maintain the outstanding level of artificial intelligence application usage in Saudi Telecommunications Company. Additionally, efforts should be directed towards providing the technological infrastructure that contributes to enhancing the company's ability to utilize artificial intelligence applications.

Review of trends regarding artificial intelligence and its prospects for procedural decisions during criminal proceeding

Discussions on the advantages and risks of artificial intelligence (AI) in various fields are among the most relevant today. It is evident that proponents and opponents of its implementation have existed, exist, and will continue to exist. However, it is undeniable that with each scientific and technological advancement, humanity has the opportunity to improve or worsen the quality of its existence. Therefore, it is crucial to establish rules for developers and users who must be aware of the existing threats posed by the use of a particular tool, despite or in defiance of these limitations. This overview aims to explore how various aspects of our lives, including the legal sphere, are changing due to the widespread availability and increasing accessibility of artificial intelligence (AI). It seeks to clarify the role of the state as a regulator and implementer of relevant functions, the rules developers and users must adhere to, the aspects of AI usage already accessible to the legal sector, and how AI can assist in decision-making during criminal proceedings (while still leaving the leading role in decision-making and choice of alternatives to humans). It also discusses the prospects ahead and the myths that need to be overcome for progress. Through analysis and synthesis, it has been observed that threats from harmful AI are present, especially when it is deployed not for socially beneficial or human-centric purposes. Regulation of the AI sphere is crucial in all its aspects, starting from ethical considerations and ensuring an anthropocentric approach to AI development and usage, and ending with cutting-edge developments, including AI applications in various fields, such as the creation of new product designs like confectionery. Despite potential risks and threats, the use of artificial intelligence can have advantages, but careful regulation of this area is necessary. It is also noted that regulating AI to the detriment of progressive development should be avoided. It is argued that in the legal and law enforcement sectors, AI assistance can bring many benefits, but its use must be accompanied by a full awareness of the axiom that “AI systems for justice and law enforcement are considered ‘high risks’.” Therefore, before actively applying AI, especially in this realm, it is essential to establish clear boundaries and rules, and importantly, accountability for any harm caused. Such sensitive aspects as the integrity of human privacy, respect for human rights, and the like should prevail in this mechanism.

Unveiling Students’ Experiences and Perceptions of Artificial Intelligence Usage in Higher Education

This study explores the utilization and perception of Artificial Intelligence (AI) tools among students in higher education. With the growing accessibility of AI technologies, their integration into educational settings presents a new frontier for enhancing learning experiences. This research adopts a mixed-methods approach, including surveys and interviews, to delve into how students employ AI tools and their perceived benefits and drawbacks of AI usage in the context of entrepreneurship education in a business school. The findings reveal a diverse range of AI applications, highlighting benefits such as increased productivity, personalized learning, and enhanced linguistic capability. However, concerns regarding academic integrity, over-reliance on AI, and the need for clear usage guidelines are also identified. This study contributes to the understanding of AI's role in higher education and provides much-needed empirical evidence of AI usage from students’ perspectives. Our findings underscore the importance of balanced, informed, and ethical use of AI tools in higher education.

Reducing the adverse effects of compulsory citizenship behaviour on employee innovative behaviour via AI usage in China

ABSTRACT Artificial intelligence (AI) plays a crucial role in the workplace by freeing up the hands and brains of employees. According to affective events theory (AET), this research assumes that AI can mitigate the negative effects of compulsory citizenship behaviour (CCB). From a mixed-method study involving both a survey (targeting 246 Chinese employees) and interviews (with a further 20 employees from China), results indicate that CCB can increase employees’ anger and hostility and ultimately negatively affect employees’ innovative behaviour. However, AI usage is shown to have the capacity to mitigate such adverse effects. This research offers both theoretical and practical insights for CCB research.

The Adoption of Artificial Intelligence in Serbian Hospitality: A Potential Path to Sustainable Practice

This study investigates the perceptions of employees in the hotel industry of the Republic of Serbia regarding the acceptance and importance of artificial intelligence (AI). Through a modified UTAUT model and the application of structural equation analysis (SEM), we investigated the key factors shaping AI acceptance. Research results show that behavioral intention and habit show a significant positive impact on AI usage behavior, while facilitating conditions have a limited but measurable impact on behavioral intention. Other factors, including social influence, hedonic motivation, performance expectancy, and effort expectancy, have minimal influence on the examined variables. The analysis reveals the crucial mediating role of behavioral intention, effectively bridging the gap between various predictors and AI usage behavior, thereby highlighting its significance in the broader context of technology adoption in the hotel industry. The primary goal of the study, which closes significant research gaps, as well as the manner in which it uses a specific model and statistical analysis to accomplish this goal, shows how innovative the work is. This method not only broadens the field’s understanding but also offers valuable insights for shaping sustainable development practices in the hospitality sector in the Republic of Serbia.

Effects of AI understanding-training on AI literacy, usage, self-determined interactions, and anthropomorphization with voice assistants

Intelligent voice assistants (IVAs) such as Alexa or Siri are voice-based Artificial Intelligence systems that help users with various everyday tasks using simple voice commands. However, users often only have a superficial understanding of how the Artificial Intelligence (AI) integrated into IVAs works, which leads to misunderstandings and potential risks of use. To promote self-determined interaction with IVAs, the development of specific AI-related skills, such as a comprehensive understanding of AI, is crucial. Based on learning psychology and media pedagogy principles, two online training modules were developed to deepen the understanding of AI concerning IVAs and enable self-determined interaction. A total of 99 participants took part in the training. The results show that the training promotes both understanding of AI and AI literacy. It also increases the intention to use IVAs, promotes a positive attitude, and enhances the willingness for self-determined interaction. In addition, the training contributes to a more realistic assessment of the IVAs' capabilities and reduces anthropomorphic perceptions. Overall, the study emphasizes the relevance of specific AI skills and shows how targeted training can contribute to improving these skills. Thus, the present work contributes to improving the availability of digital education programs.

Optimizing Artificial Intelligence Usage among Academicians in Higher Education Institutions

Optimizing Artificial Intelligence Usage among Academicians in Higher Education Institutions

Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning

The advancement of artificial intelligence (AI) and the ubiquity of social media have become transformative agents in contemporary educational ecosystems. The spotlight of this inquiry focuses on the nexus between AI and social media usage in relation to academic performance and mental well-being, and the role of smart learning in facilitating these relationships. Using partial least squares–structural equation modeling (PLS-SEM) on a sample of 401 Chinese university students. The study results reveal that both AI and social media have a positive impact on academic performance and mental well-being among university students. Furthermore, smart learning serves as a positive mediating variable, amplifying the beneficial effects of AI and social media on both academic performance and mental well-being. These revelations contribute to the discourse on technology-enhanced education, showing that embracing AI and social media can have a positive impact on student performance and well-being.

IA e patentes: reflexões sobre o impacto da IA na análise da atividade inventiva

The article aims to reflect on the impacts of using Artificial Intelligence (AI) on the requirement of inventive step, viewed through the lens of the skilled person in the art, considering the legal framework in place. To do so, it revisits the concepts of inventive step and the skilled person in the art, conceived in a reality where creative acts were exclusively the result of human activity. With the advancement of AI usage, there is a shift in the landscape of research and development of technical solutions: the human mind no longer works alone in the inventive process. Depending on the level of AI involvement in the creation of the invention, it may either serve as an instrumental layer in the skilled person’s analysis of patentability or the claimed subject matter is ineligible for patent protection.