Practical Intelligence Research Articles

THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE STANDARDIZATION AND IMPROVEMENT OF NURSING CARE

Background. The rapid advancement of artificial intelligence technologies and their implementation in medical practice create new opportunities for enhancing the quality of patient care. In the context of growing healthcare service demands and a shortage of qualified personnel, artificial intelligence plays a crucial role in optimizing nursing processes, improving diagnostic accuracy and forecasting, as well as increasing the efficiency of healthcare staff. Research objective. To examine nurses’ perceptions of artificial intelligence technologies and their readiness to integrate artificial intelligence into nursing practice in order to enhance the efficiency of patient care and optimize work processes. Materials and methods. An analysis of publications on the research topic was conducted using sources such as PubMed, Google Scholar, Web of Science, and Scopus. The results of a literature review are presented, focused on studying the use and understanding of artificial intelligence among nurses. Research results. A literature review has demonstrated that artificial intelligence holds significant potential to transform healthcare by enhancing diagnostic accuracy, personalizing treatment, improving administrative efficiency, and optimizing healthcare staff coordination. Artificial intelligence contributes to the automation of routine tasks, supports clinical decisionmaking, predicts outcomes, improves patient monitoring, and reduces workload and burnout risk among nurses. However, the implementation of artificial intelligence in medical practice requires enhanced education and training for healthcare personnel to overcome acceptance barriers, address ethical considerations, and ensure data security. Conclusion. Artificial intelligence has a significant impact on healthcare by improving the quality of care and optimizing workflows. Nurses play a crucial role in its integration, which requires enhancing their knowledge and training. Studies indicate the need for developing educational programs to improve nurses’ competencies in using artificial intelligence, ensuring more effective and safer implementation of technologies into practice.

Embracing artificial intelligence (AI) in occupational therapy practice: Bridging workforce gaps and redefining care

Embracing artificial intelligence (AI) in occupational therapy practice: Bridging workforce gaps and redefining care

The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review.

Artificial intelligence (AI) has been increasingly used in delivering mental healthcare worldwide. Within this context, the traditional role of mental health nurses has been changed and challenged by AI-powered cutting-edge technologies emerging in clinical practice. The aim of this integrative review is to identify and synthesise the evidence of AI-based applications with relevance for, and potential to enhance, mental health nursing practice. Five electronic databases (CINAHL, PubMed, PsycINFO, Web of Science and Scopus) were systematically searched. Seventy-eight studies were identified, critically appraised and synthesised following a comprehensive integrative approach. We found that AI applications with potential use in mental health nursing vary widely from machine learning algorithms to natural language processing, digital phenotyping, computer vision and conversational agents for assessing, diagnosing and treating mental health challenges. Five overarching themes were identified: assessment, identification, prediction, optimisation and perception reflecting the multiple levels of embedding AI-driven technologies in mental health nursing practice, and how patients and staff perceive the use of AI in clinical settings. We concluded that AI-driven technologies hold great potential for enhancing mental health nursing practice. However, humanistic approaches to mental healthcare may pose some challenges to effectively incorporating AI into mental health nursing. Meaningful conversations between mental health nurses, service users and AI developers should take place to shaping the co-creation of AI technologies to enhance care in a way that promotes person-centredness, empowerment and active participation.

Assessing the effect of a flipped classroom on students’ learning performance

This research investigates the effect of the flipped classroom on learning performance in management classes at vocational and technical colleges concentrating on cognitive style as a mediating factor. The researcher examines the difference in learning achievements, successful intelligence (analytical, creative, and practical intelligence), and learning attitudes among students who have experienced the flipped classroom method and those who receive traditional instruction through a quasi-experimental design implemented for students from 5 vocational and technical colleges. The study uses Sternberg's successful intelligence theory to determine how this pedagogical approach affects various aspects of intelligence. Key findings indicate that the flipped classroom approach is more effective than the traditional one in improving cognitive learning achievement, analytical, creative and practical intelligence, and a positive learning attitude. However, cognitive style, particularly visual versus verbal preferences showed limited impact on the effectiveness of flipped classroom learning. Therefore, flipped classrooms benefit learning performance but these benefits work irrespective of the student's cognitive style. Thus, the research is of great importance to vocational education practitioners in general and promotes the concept of flipped classrooms which might positively affect the quality of management learning.

Development of an Automated Diagnostic System using Genetic Algorithms in Electroneurodiagnostic Data

This project aims to develop an automated diagnostic system that leverages genetic algorithms (GAs) for analyzing electroneurodiagnostic (END) data, including electroencephalograms (EEG) and electromyograms (EMG). The growing complexity of END data poses significant challenges for accurate diagnosis and timely intervention in neurological disorders. By utilizing genetic algorithms, we aim to enhance the feature selection process, optimizing the identification of relevant patterns associated with various neurological conditions. The system will undergo rigorous training using a diverse dataset, allowing it to recognize and classify abnormalities effectively. Initial results indicate that GAs can significantly improve diagnostic accuracy compared to traditional methods, reducing the likelihood of misdiagnosis and facilitating early intervention. The project also aims to establish a user-friendly interface for clinicians, enabling them to interpret results intuitively. This innovative approach enhances diagnostic capabilities and contributes to neuroinformatics, promoting the integration of artificial intelligence in clinical practice.

Exploring the Practice of Artificial Intelligence Empowering Primary School Chinese Reading Instruction

Exploring the Practice of Artificial Intelligence Empowering Primary School Chinese Reading Instruction

“Everybody knows what a pothole is”: representations of work and intelligence in AI practice and governance

Abstract In this paper, we empirically and conceptually examine how distributed human–machine networks of labour comprise a form of underlying intelligence within Artificial Intelligence (AI), considering the implications of this for Responsible Artificial Intelligence (R-AI) innovation. R-AI aims to guide AI research, development and deployment in line with certain normative principles, for example fairness, privacy, and explainability; notions implicitly shaped by comparisons of AI with individualised notions of human intelligence. However, as critical scholarship on AI demonstrates, this is a limited framing of the nature of intelligence, both of humans and AI. Furthermore, it dismisses the skills and labour central to developing AI systems, involving a distributed network of human-directed practices and reasoning. We argue that inequities in the agency and recognition of different types of practitioners across these networks of AI development have implications beyond RAI, with narrow framings concealing considerations which are important within broader discussions of AI intelligence. Drawing from interactive workshops conducted with AI practitioners, we explore practices of data acquisition, cleaning, and annotation, as the point where practitioners interface with domain experts and data annotators. Despite forming a crucial part of AI design and development, this type of data work is frequently framed as a tedious, unskilled, and low-value process. In exploring these practices, we examine the political role of the epistemic framings that underpin AI development and how these framings can shape understandings of distributed intelligence, labour practices, and annotators’ agency within data structures. Finally, we reflect on the implications of our findings for developing more participatory and equitable approaches to machine learning applications in the service of R-AI.

Monitoring, governmental data access and the invocation of Article 8 ECHR by legal persons

ABSTRACT The European Convention on Human Rights (ECHR) grants the rights enshrined in Article 8 to ‘everyone’. The European Court of Human Rights (ECtHR) has interpreted legal persons as falling within the scope of ‘everyone’ in various judgments, primarily concerning governmental searches and seizures. With the rise of digital technology, corporations now hold vast amounts of personal data, creating growing pressure to disclose such data to governmental surveillance agencies. This article examines ECtHR case law and applies theories of legal personality to assess the extent to which legal persons may invoke Article 8 in cases of governmental interferences affecting associated natural persons' rights. After outlining the legal framework, the article applies it to two use cases: governmental control of fishing vessels to identify regulatory infringements and signal intelligence practices affecting legal persons operating telecommunication infrastructure. These examples illustrate two types of governmental interference; methods impacting natural persons through data held by legal persons and interferences directed at the legal person itself. The article seeks to clarify the scope of rights under Article 8 for legal persons. The question of whether such interferences are proportionate is beyond the article's scope and remains a topic for future research.

An Exploratory Study on the Artificial Intelligence Practices on the Human Capital Development within Tourism Village Business Sustainability in Indonesia

This exploratory study investigates the role of artificial intelligence (AI) practices in enhancing human capital development and promoting business sustainability within tourism villages in Indonesia. As tourism villages become increasingly significant to Indonesia’s rural economy, the need to foster sustainable business models that can maintain cultural heritage while promoting economic growth has gained attention. AI technologies, including machine learning, data analytics, and automation, present opportunities for improving human capital development by enhancing skill-building, training, and decision-making processes. This study examines how AI tools are being integrated into training programs, recruitment processes, and daily operations of tourism village businesses to support workforce growth, improve service quality, and ensure long-term sustainability. Through qualitative data collected from tourism village stakeholders in Bali and Java, the research explores AI’s impact on local workforce capabilities, productivity, and innovation in tourism-related services. The study finds that AI practices are helping to bridge skill gaps, enhance workforce efficiency, and optimize resource management. However, challenges such as limited access to technology, digital literacy, and the need for infrastructure development hinder broader AI implementation. The paper concludes by proposing recommendations for leveraging AI practices to further strengthen human capital and business sustainability in tourism villages, thus contributing to the sector’s competitive edge in the global market.

An Empirical Study on the Artificial Intelligence Practices on the Digital Marketing Effectiveness within Tourism Village in Bali, Indonesia

This empirical study examines the influence of artificial intelligence (AI) practices on the effectiveness of digital marketing within tourism villages in Bali, Indonesia. The research investigates how AI-powered tools and techniques—such as predictive analytics, recommendation systems, automated customer service, and content personalization—enhance marketing outcomes in these unique cultural destinations. Through a mixed-method approach combining surveys, interviews, and analysis of digital marketing performance metrics, data were collected from local tourism operators, marketers, and stakeholders. The findings demonstrate that the adoption of AI significantly improves audience targeting, engagement rates, and return on investment (ROI) for digital marketing efforts. Additionally, AI facilitates efficient resource allocation and provides actionable insights, enabling small tourism businesses to adapt to market dynamics and elevate their competitiveness in the global tourism industry. However, the study also highlights barriers to AI implementation, including financial constraints, technological literacy gaps, and the need to balance automation with preserving local cultural authenticity. This research provides valuable implications for policymakers and practitioners aiming to leverage AI for sustainable tourism development in Bali. It calls for collaborative efforts to enhance digital literacy and promote equitable access to AI technologies in rural tourism settings. Future research could explore the long-term societal and economic impacts of AI-driven marketing within the context of cultural tourism.

AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation.

The primary practice of healthcare artificial intelligence (AI) starts with model development, often using state-of-the-art AI, retrospectively evaluated using metrics lifted from the AI literature like AUROC and DICE score. However, good performance on these metrics may not translate to improved clinical outcomes. Instead, we argue for a better development pipeline constructed by working backward from the end goal of positively impacting clinically relevant outcomes using AI, leading to considerations of causality in model development and validation, and subsequently a better development pipeline. Healthcare AI should be "actionable," and the change in actions induced by AI should improve outcomes. Quantifying the effect of changes in actions on outcomes is causal inference. The development, evaluation, and validation of healthcare AI should therefore account for the causal effect of intervening with the AI on clinically relevant outcomes. Using a causal lens, we make recommendations for key stakeholders at various stages of the healthcare AI pipeline. Our recommendations aim to increase the positive impact of AI on clinical outcomes.

Toward an evolving framework for responsible AI for credit scoring in the banking industry

PurposeThe aim of this research is to conduct a systematic review of the literature on responsible artificial intelligence (RAI) practices within the domain of AI-based Credit Scoring (AICS) in banking. This review endeavours to map the existing landscape by identifying the work done so far, delineating the key themes and identifying the focal points of research within this field.Design/methodology/approachA database search of Scopus and Web of Science (last 20 years) resulted in 377 articles. This was further filtered for ABDC listing, and augmented with manual search. This resulted in a final list of 53 articles which was investigated further using the TCCM (Theory, Context, Characteristics and Methodology) review protocol.FindingsThe RAI landscape for credit scoring in the banking industry is multifaceted, encompassing ethical, operational and technological dimensions. The use of artificial intelligence (AI) in banking is widespread, aiming to enhance efficiency and improve customer experience. Based on the findings of the systematic literature review we found that past studies on AICS have revolved around four major themes: (a) Advances in AI technology; (b) Ethical considerations and fairness; (c) Operational challenges and limitations; and (d) Future directions and potential applications. The authors further propose future directions in RAI in credit scoring.Originality/valueEarlier studies have focused on AI in banking, credit scoring in isolation. This review attempts to provide deeper insights, facilitating the development of this key field.

AI Thinking: a framework for rethinking artificial intelligence in practice.

Artificial intelligence is transforming the way we work with information across disciplines and practical contexts. A growing range of disciplines are now involved in studying, developing and assessing the use of AI in practice, but these disciplines often employ conflicting understandings of what AI is and what is involved in its use. New, interdisciplinary approaches are needed to bridge competing conceptualizations of AI in practice and help shape the future of AI use. I propose a novel conceptual framework called AI Thinking, which models key decisions and considerations involved in AI use across disciplinary perspectives. AI Thinking addresses five practice-based competencies involved in applying AI in context: motivating AI use, formulating AI methods, assessing available tools and technologies, selecting appropriate data and situating AI in the sociotechnical contexts it is used in. A hypothetical case study is provided to illustrate the application of AI Thinking in practice. This article situates AI Thinking in broader cross-disciplinary discourses of AI, including its connections to ongoing discussions around AI literacy and AI-driven innovation. AI Thinking can help to bridge between the work of diverse disciplines, contexts and actors in the AI space, and shape AI efforts in education, industrial development and policy.

Big Supply Chain Analytics Enhances Decision-Making: A Literature Review Approach

The way organizations function in today's dynamic market landscape has been changed by the integration of data analytics inside supply chain management. Within the supply chain domain, data analytics pertains to be methodical procedure of gathering, analyzing, and interpreting diverse data kinds produced along the chain in order to extract significant understandings and practical intelligence. It entails applying methods from the fields of descriptive, predictive, and prescriptive analytics to boost operational effectiveness, optimize decision-making, and raise overall performance in the ecosystem of the supply chain. Organizations may make well-informed decisions, spot trends, and proactively handle problems by analyzing data from a variety of sources, including suppliers, manufacturers, distributors, and retailers. This results in more efficient resource allocation and streamlined procedures. Data analytics is playing a more and more important role as businesses look to improve operational efficiency, cut costs, and successfully satisfy customer needs. This study examines the role that data analytics plays in supply chain optimization, helping firms to increase the precision of their forecasts, simplify their logistical procedures, and make well-informed decisions through literature review. Most of the literature that is currently available agrees that big data analytics goes far beyond simply reimagining the supply chain. It may help the next generation of multinational corporations function more adaptably in a setting that is becoming more demanding and unpredictable. This conceptual paper applies relevant review literature and draw conclusions. DOI: https://doi.org/10.52783/pst.1315

MOOD OF THE DOCTORS: PROBLEMS, CHALLENGES AND PERSPECTIVES OF AI USE IN MEDICINE

Aim of the study: Artificial intelligence (AI) is gaining increasing and obvious implications in medical practice. The novelty of the research paper consists in evaluating the implicative relationship doctor-artificial intelligence (AI) in medical practice, a hot and current topic, with implications on physicians' receptivity to the use and perspectives of AI in medicine, which derive from the mood, introverted and extroverted components of physicians. Materials and methods: The aim of the research is to reveal the challenges and prospects of using AI in medicine in relation to the mood of doctors, namely, the classification of emotions, feelings as part of the introvert and attitude, as the extrovert part of doctors, in practical interaction with AI. This includes providing an aesthetic and ethical experience in the doctor-AI relationship within medical practice. .Results: The holistic approach to the doctor-patient-AI relationship is identified as a solution for increasing the attention and involvement of doctors in the use of high-performance technologies. This approach supports medical practice outcomes and aims to improve the quality of life of patients. Conclusions: The findings suggest that enhancing the doctor-AI relationship through a focus on emotions and attitudes fosters receptivity among physicians, leading to better integration of AI technologies and improved patient care.

“DE MÃOS ATADAS” – ESTRATÉGIAS DE MEDIAÇÃO DO SOFRIMENTO DE PSICÓLOGOS ORGANIZACIONAIS NO SERVIÇO PÚBLICO FEDERAL BRASILEIRO

Organizational and work psychologists have a strong potential to contribute to workers' health, given the nature of their activity. Despite this, the literature lacks investigations on their subjective work experience. The objective of this study was to analyze their strategies of mediation of suffering in the face of the resistance of the real of work. A qualitative research was carried out through semi-structured interviews with nine organizational and work psychologists working in Brazilian federal agencies. The data were submitted to content analysis based on two theoretical categories arising from the psychodynamics of Dejours' work, which guided the study. The results demonstrate that POT servers are in a defensive movement, significantly adapted and, to some extent, contributing to the adaptation of the others. Denial and rationalization are the most used defense strategies, in addition to the search for support in the collective and the use of jokes in the workplace. To face reality, they use practical intelligence to transgress and divert from the direct attendance of the impositions of the organization of work; they collaborate with each other much more to resist than to transform the elements of the organization of work that give rise to suffering; they use the public space to exchange and find solutions, without rebelling against the potentially sickening elements of the organization of work. In addition, the recognition of beauty by peers is incipient, with a greater record of recognition of usefulness by managers and clients. Subjective mobilization appears limited by managerial precariousness and, thus, the strengthening of work collectives for a counterpsychology that confronts the managerialism that crosses contemporary work is called.

A risk science perspective on how to evaluate the quality of intelligence assessments

ABSTRACT Recent articles on intelligence theory have shown that guidelines for evaluating and expressing the quality of intelligence assessments are based on weak theoretical justifications and lack a proper theoretical foundation. Risk and intelligence analysts face similar issues related to understanding, assessing, and communicating threats and uncertainties. This article draws on risk science’s advances in evaluating the strength of the knowledge used in risk assessments. Using a fictitious arms trafficking case, we demonstrate the compatibility of a strength-of-knowledge approach to quality with the current understanding of confidence in intelligence studies. The aim is to show that risk science’s theoretical framework and formalized language is applicable to the evaluation of intelligence assessment quality as well as to intelligence theory in general. Implications for intelligence practice and research are discussed.

Creation and validation of a tool to identify knowledge, attitudes and practices of Competitive Intelligence in Microentrepreneurs

One out of every two microenterprises survive during the first 5 years of its operation, due to factors such as lack of knowledge about the business environment and its administrative and financial structures, on the part of its managers and employees. In this scenario, competitive intelligence (CI) represents an alternative solution in the midst of the current changing and accelerated pace of doing business, including those associated with the Covid19 pandemic. However, the existing competitive intelligence implementation methodologies are currently very complex and costly for smaller companies, which is why this non-experimental cross-sectional research is based on the creation and systematic validation of an agile and practical tool to identify knowledge, attitudes and practices (KAP) on CI, starting with a systematic review of the state of the art related to the concepts of microenterprise and CI, a review of Competitive Intelligence questionnaires and Knowledge, Attitudes and Practices (KAP) questionnaires; and then a content validation by experts, and culminating with a pilot application to measure its reliability through Cronbach's Alpha index; resulting in a tool that favors methodologies or managerial, scientific, commercial or governmental actions that promote the competitive permanence of microenterprises, and therefore, the sustainable economic development of the regions

Technology intelligence practices in SMEs: Evidence from Estonia

Technology intelligence is regarded as a strategic tool to support open innovation to identify promising niches of technologies, opportunities and threats, potential partners, future customers and markets. However, it has often been neglected by SMEs due to their constraints in money, time, skills and competences. Hitherto, the literature documented very few cases of the operationalization of technology intelligence practices by small firms of catching-up economies. To remedy this gap, this paper investigates the case of three Estonian SMEs in the manufacturing, information technology and life-sciences industries. Our analysis reveals that elements of technology intelligence in large and small companies are similar. The three medium and small sized companies investigated in this study adopted these elements to their specific context orchestrating their organizational and cultural characteristics. This study details these elements and allows us to understand more precisely the process underlying the phenomenon of technology intelligence in small companies. The major finding of this paper is that a unique set of technology intelligence does not exist. It is important to tailor different elements of technology intelligence to determined needs. It is crucial in the case of SMEs in order to address the limitations mentioned above.

Affordance of Internet of Things for educational resources management for E-learning domain

The Internet of Things (IoT) offers dual dimensions of affordance for educational resources management (ERM), particularly in a Comprehensive open distance e-learning institution in South Africa. The affordance of IoT for educational resources management is not well articulated in a CODEL institution. The study aimed to establish the role of IoT in the administration and management of educational resources. Technology affordance theory is used to establish the role, perceptions, and causality of IoT affordance in ERM. The research opted for the qualitative approach to establish the role and causality of the IoT affordance of ERM. The study found that the CODEL institution is IoT-driven when handling ERM. The main contribution relates to two propositions such as South African higher education institutions, including the CODEL institution, require the articulation and realignment of an IoT-driven business enterprise system for the implementation of OER in tuition; and in an IoT-driven context such as CODEL, where OER are implemented, there is a motive for academics and institutions to develop an Artificial Intelligence or interactive robot for creating and locating OER. It suggests that CODEL should realign its business enterprise system with IoT-driven infrastructure and adopt artificial intelligence practices for OER advancement. Future research should investigate the availability of IoT in all South African higher education institutions.