Artificial Intelligence in Legal Frameworks: Conceptual Foundations and Implications

Artificial Intelligence (AI) technologies are rapidly evolving and their integration into psychological practices has progressively expanded, offering new tools for diagnosis, treatment and therapeutic monitoring. This review examines the transformative role of AI, particularly Natural Language Processing (NLP) systems, in reshaping clinical psychology and digital mental health interventions (DMHIs). In particular, it explores how AI and NLP can facilitate human-machine interaction in therapy by analysing how language is used within clinical conversations and providing personalized, real-time interventions. Following PRISMA guidelines, a systematic review of literature from 2019 to 2025 identified 17 studies that met inclusion criteria, emphasizing AI's use in psychological assessment and intervention. The review focuses on two key aspects: the functions and applications of NLP-based systems in clinical practice and the advantages and benefits they offer for both psychologists and patients. Findings suggest that NLP-driven AI systems enhance both patient engagement and clinician efficiency, offering scalable, cost-effective solutions that improve access and personalization. However, challenges remain, including ethical concerns around data privacy, lack of standardization, limited generalizability across disorders and reduced human empathy. Moreover, current systems are primarily designed for well-defined conditions like anxiety and depression, with limited applicability to complex or comorbid psychological presentations. This review underscores the importance of supervised, ethically governed AI implementation. While AI holds substantial promise in augmenting clinical psychology, its success depends on maintaining human oversight, ensuring transparency and establishing shared scientific and ethical standards across the psychological community.

Artificial intelligence (AI) technologies, such as big data analysis, machine learning, and natural language processing, have the potential to make language education more accessible, personalized, and efficient. This study analyzed applications of AI in language teaching and assessment by reviewing 59 articles. The scope included exploring emerging ethical, pedagogical, and technological considerations related to AI-driven tools used for language teaching, assessment, and learner support. The review focused on English language contexts, particularly studies involving AI-powered tools in EEL/ESL settings, such as natural language processing systems, automated assessment, machine learning analytics, and intelligent tutoring systems. Literature searches were conducted across major databases, including Scopus, Web of Science, ERIC, ScienceDirect, SpringerLink, and Google Scholar. The collected data were analyzed following PRISMA guidelines and with the support of MAXQDA software. The findings indicate that AI-driven language tutoring systems can provide targeted interventions, facilitate interactive learning activities, and assess learners' learning styles and proficiency levels. However, challenges include potential biases, inequitable learning environments, and unequal access to technology. Ethical concerns involve data privacy, discrimination, algorithm transparency, and accountability in AI-based tools. To ensure effective integration, pedagogical strategies should balance automated evaluation with real-world discussions and interactions to promote comprehensive language development.

This theses explores the transformative potential of artificial intelligence (AI) in the auditing industry, focusing on its implications for audit quality, efficiency, and the evolving role of human auditors. A comprehensive review of relevant literature highlights the integration of AI-driven tools such as machine learning, deep learning, and natural language processing in audit processes, emphasizing their capabilities in enhancing risk assessment, fraud detection, and data analytics. The experimental design features an innovative approach to compare the efficacy of AI and human auditors in conducting risk analysis for three companies--Blockbuster, Enron, and Lehman Brothers--presented anonymously with pre-bankruptcy financial information. The findings reveal that while AI models provided consistent and comprehensive risk assessments, human auditors displayed a tendency towards conservative evaluations. This underlines the critical role of AI in standardizing audit processes and identifying subtle risk indicators that might be overlooked by human judgment alone. The analysis underscores the necessity of combining AI's computational strengths with human auditors' strategic insights to improve audit outcomes. Ethical, regulatory, and practical considerations surrounding the adoption of AI in auditing are also examined, advocating for robust frameworks to ensure data privacy, algorithmic transparency, and audit independence. This theses contributes to the understanding of AI's role in modern auditing, advocating for its responsible integration to enhance audit quality and reliability. As the auditing profession stands at the brink of a technological revolution, this research offers valuable insights into leveraging AI to achieve more accurate, efficient, and insightful audits.

Artificial Intelligence (AI) is transforming the landscape of public health surveillance across the world, allowing real-time detection of diseases, prediction of outbreaks, and data-based response. While the EU has progressed considerably in ensuring AI integration by establishing a strong digital infrastructure, regulatory frameworks (e.g., General Data Protection Regulation, GDPR, and the EU AI Act), Africa still experiences major setbacks, such as poor infrastructure, little AI literacy, and lacking legal and ethical frameworks. Based on the current literature, the paper has studied the role of AI technologies, especially machine learning and natural language processing, in supporting real-time health monitoring. The analysis reveals that in as much as the EU presents a model through which effective and ethical integration of AI can be achieved, the Africa region lags in developing the digital health ecosystems to facilitate the full potential of AI. The study proposes to close this gap by shoring up regulatory systems, investing in digital plumbing, advancing AI literacy, investing in privacy-preserving technologies and multilateral engagement. The need to level these discrepancies is critical to the resilience of public health in the region as well as to the global health equity in the era of smart technologies.

In the digital age, cyber threats have become increasingly sophisticated, necessitating innovative approaches to bolster security measures. Artificial Intelligence (AI) has emerged as a formidable tool in the realm of cyber security, offering advanced capabilities in threat detection, anomaly detection, and response automation. This article provides an overview of AI applications in cyber security, highlighting its role in mitigating risks and fortifying defense mechanisms. AI techniques such as machine learning, deep learning, and natural language processing empower security systems to analyze vast amounts of data in real-time, identifying patterns indicative of malicious activities. Through the utilization of AI-driven algorithms, cyber security platforms can proactively detect and neutralize cyber threats before they inflict substantial damage. Moreover, AI enables the automation of incident response processes, reducing response times and minimizing the impact of security breaches. Case studies from leading cyber security firms from the integral part of the studies and demonstrate the practical implementation of AI-driven solutions in safeguarding critical infrastructures against cyber threats. Resilience towards cyber-attacks and safeguarding sensitive data assets through leveraging AI technologies has been focused in the study.

Artificial Intelligence (AI) improves resource management and energy conservation through machine learning, improved forecasting, and quicker data processing. Artificial intelligence (AI) and machine learning (ML) have become essential enablers of this integration in this context. Machine learning and artificial intelligence are necessary for an organization’s survival and expansion, not just a choice. This research explores the growing significance of machine learning and artificial intelligence in resource management. The proposed method, deep reinforcement learning (DRL) based on machine learning is an essential tool for addressing the issues of resource management. Resource management can ensure that certain QoS criteria are met for various applications, such as information or real-time video transmission. Reducing total transmit power allows optimal resource management, while following Quality of Service (QoS) criteria permits a better relay choice. In this paper, the numerical results show that it performs competitively in terms of bandwidth of 97.5%, power consumption of 96%, spectrum efficiency of 91%, and network lifespan of 93 percent. Costs are decreased and environmental responsibility is enhanced through resource management. This research examines how AI might increase bandwidth, power consumption, spectrum efficiency, and resource management, with a particular emphasis on automation, efficiency, and prediction via quality of service.

This entry explores the integration of Artificial Intelligence (AI) tools into the teaching and research of Languages for Specific Purposes (LSP), examining its current applications, affordances, and challenges for educators and learners alike. It discusses the pedagogical particularities of LSP and connects them to the potentialities of AI‐powered tools. Drawing on recent developments in natural language processing, machine learning, and intelligent tutoring systems, and connecting them to the current research insights, the entry analyzes how AI‐enhanced tools—such as adaptive language platforms, automated writing evaluation systems, and conversation agents—are transforming LSP pedagogy. Particular attention is paid to the affordances of AI for tailoring instruction to specific professional linguistic needs, fostering learner autonomy, and boosting motivation and engagement. Through a critical review of research studies and a discussion of ethical and pedagogical considerations, the entry argues that while AI presents significant opportunities for innovation in LSP contexts, its adoption must be guided by domain‐specific expertise, transparency, and pedagogical soundness.

Artificial intelligence (AI) plays a key role in knowledge economies, because it can be used to develop systems that think like humans, act like humans, think rationally, and act rationally (Russell & Norvig, 2010). In this study, we divide AI into four sub-technological fields: Problem reasoning and solving, machine learning, network structures, and knowledge processing systems. This study investigates three main issues related to the technology development of AI. First, the aggregate technology development of AI is examined, and the four sub-technological fields of AI are compared. Second, we employ measures of patent quantity and patent quality to demonstrate the technology development of AI in different countries. Finally, we investigate the technology positions of different countries in the four sub-technological fields of AI. By analyzing a patent and citation dataset comprised of all patents granted by the United States patent and trademark office from 1976 to 2010, we obtain empirical findings that help us understand the technology development of AI in different countries. The major contributions of this study are four measures of patent quantity (PCA, PCI, SHAI, and SHIA) and three measures of patent quality (citation ratios, CII, and TCT). These measures are helpful in understanding technological development of AI in different counties. Moreover, we use patent citation data and investigate the technology flow in AI, in order to determine the technology position of different countries in the four sub-technological fields of AI.

Colorectal cancer remains a leading cause of cancer associated death in the United States and colonoscopy the primary screening strategy for prevention. Rates of adenomatous and serrated neoplasia detection are inversely associated with postcolonoscopy colorectal cancer. This crucial quality metric depends on accurate ascertainment of colorectal neoplasia findings from both endoscopy and histopathology records. We aimed to assess the feasibility of a random forest machine learning model to rapidly and accurately categorize colorectal neoplasia from electronic health record data. A retrospective cohort study compared neoplasia detection rates among individuals undergoing colonoscopy at a large academic institution to develop a rule-based algorithm to categorize colorectal neoplasia from endoscopy reports and pathology systematized nomenclature of medicine - clinical terms. This cohort provided a large training set to develop a natural language processing system using a random forest approach to automatically classify unstructured pathology findings into adenoma, serrated, or advanced neoplasms. This system was manually validated through an independent holdout set. The training set comprised 35,953 unstructured pathology reports with matched systematized nomenclature of medicine - clinical terms from 95,188 unstructured colonoscopy reports. The final model was assessed on an independent holdout set of 337 manually annotated procedures obtaining an area under the receiver operating characteristic curve of 0.997 (confidence interval [CI] 0.994-1), 0.99 (CI 0.98-1), and 0.99 (CI 0.98-0.99) for prediction of adenoma, serrated, and advanced lesions, respectively. The random forest-based hybrid natural language processing system for classification of colonoscopy results was both accurate and explainable. NLP combined with effective machine learning algorithms can provide a scalable strategy for colonoscopy quality monitoring.

Background: Unprecedented growth in artificial intelligence (AI) is changing the way people interact with computers- the concept known as human-computer interaction (HCI), several AI approaches like machine learning or natural language processing have boosted the user experience (UX). Nevertheless, there are some ethical concerns and differences in users’ awareness of AI systems that make it difficult to consider the general advantages of AI in HCI. Objective: Therefore, this research seeks to explore the impact of enhanced AI techniques on the level of UX in HCI with a focus on the facet of user satisfaction, trust, and usefulness of AI-based personalization techniques. It also solves the ethical problem associated with the AI systems. Methods: A quantitative research method was used with the use of structured questionnaires with 250 participants. It also captured the user engagement with AI systems, as well as their satisfaction and trust levels as well as their concerns relating to AI ethics. Descriptive analysis was used to examine the collected data through statistical tests such as; the Shapiro-Wilk test for normality and Cronbach’s Alpha for reliability. Results: It is seen that most of the users are happy with the AI technologies integrated into their lives, with special emphasis on the aspect of personalization. However, the data was nonparametric (Nomography = 0 05) and so was the internal consistency (Cronbach alpha -0. 033 on the Likert scale items were needed for survey tools of higher specific focus. The results of the survey showed that ethical considerations running across the respondents as the most significant factor of discrepancy between satisfaction and trust in areas of tension. Conclusion: In the context of AI methodologies, most are perceived to have positive effects in enhancing the UX regarding individualized engagements. However, ethical issues should be met and addressed while determining the reliability of the tools used in ascertaining the impact of AI. There is a need for future studies to address these issues and improve the techniques used for evaluating AI’s diverse position in HCI.

Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts.Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information.However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations.The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.

Aim: This paper proposes a novel paradigm of Artificial Intelligence (AI) grounded in the epistemological process of converting tacit knowledge into explicit knowledge. Drawing on the foundational philosophies of science, particularly the works of Popper, Kuhn, Lakatos, and Gospodarek, the study conceptualizes AI not merely as a computational tool but as a systemic method for epistemic transformation. The paradigm is structured as a Lakatosian Research Programme, with a clearly defined hard core asserting that AI enables the symbolic representation of internalized, experiential knowledge. Surrounding this core is a protective belt of auxiliary hypotheses derived from general systems theory, cybernetics, machine learning, and symbolic processing. The programme's heuristics guide theoretical and technological advancements while preserving its epistemological foundation. By formalizing the tacit-to-explicit knowledge conversion, this paradigm repositions AI as a critical instrument for knowledge creation, management, and application in digital and socio-technical systems. This allows one to build measures and values of generative and language models, which is important from an economic point of view. This research tries to clarify the framework of use AI models for converting tacit knowledge inside a learning data of neural network systems to explicit information requested by the asking. It is important for economic evaluation of AI systems where accuracy considered utility as a criterion. Design / Research methods: Research programme in Lakatos’ sense and multidisciplinary heuristic related to the theory of systems. Conclusions / findings: Artificial Intelligence should be understood not only as a technological artefact but as a systemic method for transforming tacit knowledge into explicit knowledge. The proposed AI paradigm adheres to the structure of a Kuhnian paradigm and a Lakatosian research programme. Its hard core is defined by the thesis that AI operationalizes the conversion of experiential, intuitive, or unconscious knowledge into symbolic, formalized, and actionable representations. Lakatosian protective belt as a dynamic epistemic layer. This AI paradigm offers a progressive problem-shift capacity by enabling novel ways of organizing, analyzing, and applying knowledge in digital and socio-technical environments. It also provides a coherent framework for developing AI systems that are more aligned with human cognitive and organizational processes. Originality / value of the article: This paper introduces: new concepts of usefulness of AI systems, new definition of AI systems based on conversion of the knowledge, original conversion paradigm and research program in Lakatos sense. It is original conceptional heuristic based on philosophy of science in relation to economic usefulness of view AI systems. JEL: C67, C18.

In this article, I discuss the use of artificial intelligence (AI) in evaluation and its relevance to the evolution of the field. I begin with a background on how AI models are developed, including how machine learning makes sense of data and how the algorithms it develops go on to power AI models. I go on to explain how this foundational understanding of machine learning and natural language processing informs where AI might and might not be effectively used. A critical concern is that AI models are only as strong as the data on which they are trained, and evaluators should consider important limitations when using AI, including its relevance to structural inequality. In considering the relationship between AI and evaluation, evaluators must consider both AI's use as an evaluative tool and its role as a new subject of evaluation. As AI becomes more and more relevant to a wider array of fields and disciplines, evaluators will need to develop strategies for how good the AI is (or is not), and what good the AI might (or might not) do.

This scoping review explores the potential of Artificial Intelligence (AI) to improve pilot training, a critical component in maintaining aviation safety. Traditional training methods, while foundational, are limited by cost, access to real-world scenarios, and adaptability to individual learning needs (AI Labs, n.d.). AI technologies, such as machine learning, computer vision, and natural language processing, offer innovative tools to enhance the cognitive and decision-making capabilities of pilots, enabling personalized learning, real-time feedback, and adaptive simulation environments (Kabashkin et al., 2023; Eddins, 2024). A comprehensive review of academic databases was conducted using specific keywords related to AI and pilot performance. Studies included in this review focused on AI applications that directly impact pilot training efficacy. Data were extracted and qualitatively analyzed to assess the scope and quality of findings across selected literature.Key findings reveal a shift from instructor-led and simulator-based training to more dynamic, AI-enhanced platforms that simulate real-time stressors and decision-making contexts. Additionally, AI supports safety outcomes by powering predictive analytics to identify potential risks before they occur (Mylrea & Robinson, 2023). Despite these advances, challenges remain. Ethical issues such as algorithmic bias and data privacy need to be addressed. Technical limitations, including the demand for high-fidelity simulation and AI reliability, also hinder widespread adoption. Human factors, notably pilot trust in AI systems, significantly affect implementation success (Korentsides et al., 2024). This review concludes that while AI presents transformative opportunities for pilot training, its effective integration requires addressing existing limitations, updating regulatory frameworks, and exploring synergies with technologies like virtual and augmented reality. Future research should emphasize collaborative AI systems and ethical safeguards to ensure both technical effectiveness and pilot acceptance.

Purpose: The study explores the transformative role of Artificial Intelligence (AI) in enhancing fraud detection and authenticity verification within global supply chains. It focuses on high-stakes industries such as pharmaceuticals, electric vehicles, consumer electronics, and agriculture, where counterfeiting poses significant risks to safety and consumer trust. By integrating AI technologies like machine learning, neural networks, and natural language processing (NLP), the study highlights how AI enhances supplier data analysis, physical verification through computer vision, and predictive analytics for fraud prevention. Methodology: The methodology combines case studies, industry reports, and the Gartner Five Levels of Supply Chain Maturity framework to examine AI's potential across siloed management, cross-functional integration, and ecosystem orchestration. Real-world use cases, including Siemens and IBM, demonstrate AI's success in fraud detection, cost savings, and improved product safety. Findings: The findings reveal that AI, in synergy with blockchain, ensures supply chain transparency while addressing ethical challenges like data privacy and environmental concerns. Unique Contribution to Theory, Practice and Policy: Recommendations emphasize ethical AI deployment, strategic investment in feasible technologies like IoT and biometric verification, and prioritization of regulatory compliance. The study concludes that AI adoption is critical for building resilient and trustworthy supply chains, offering strategic advantages in combating fraud and fostering consumer confidence.