AI-Assisted Sentencing Modeling Under Explainability Constraints: Framework Design and Judicial Applicability Analysis

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

This study investigates the influence of artificial intelligence (AI) integration on teachers’ professional identity and job satisfaction using a quantitative research design involving 251 respondents. Descriptive statistics showed relatively high levels of AI integration (M = 3.98, SD = 0.62) and professional identity (M = 4.12, SD = 0.58), indicating strong engagement with AI tools and a well-defined sense of professional role among teachers. Pearson correlation analysis revealed a moderately strong, statistically significant positive relationship between AI integration and professional identity (r = 0.612, p = 0.000), demonstrating that increased use of AI is associated with a strengthened professional identity. Mediation analysis further indicated that institutional factors significantly influence the relationship between AI integration and job satisfaction, with AI integration positively predicting institutional support (β = 0.54, p = 0.000) and institutional factors strongly predicting job satisfaction (β = 0.47, p = 0.000). Both a significant direct effect (β = 0.29, p = 0.001) and a strong indirect effect (β = 0.25, p = 0.000) were found, confirming partial mediation. These findings highlight that AI not only enhances teachers’ identity and satisfaction but that successful implementation relies heavily on institutional readiness and support. Overall, the results underscore the importance of adopting teacher-centered AI strategies that reinforce professional identity, reduce workload, and enhance well-being.

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

Abstract. Introduction. Today, characterized by the rapid digitalization and intellectualization of the global economy, the integration of artificial intelligence (AI) in transportation services is gaining ground. Consequently, maritime logistics, as part of international trade, places new demands on the training of highly qualified personnel capable of continuously acquiring professional competencies to optimize processes, improve safety, and enhance economic sustainability. This article examines the challenge of training future transport industry specialists capable of adapting to the continuous integration of innovative digital technologies and artificial intelligence in the maritime economic sector. The aim of this article is to develop a scientifically sound concept for training personnel for the transport industry that takes into account the specifics of integrating artificial intelligence into the maritime economic sphere. The methodological foundations of the study include comparative pedagogical and personal-activity approaches, systems analysis, and modeling methods. Advanced concepts and theories in the fields of artificial intelligence, pedagogy, and transport economics are used as a methodological basis. Research Results. The author attempts to present conceptual aspects of training specialists for the transport industry, taking into account the specifics of AI implementation in the maritime economic sector. Based on an analysis of the current state of AI integration in the maritime economic sector, a core set of competencies and skills necessary for working with AI in transport is identified and the structure of a training model for transport specialists is presented. The conclusion describes the potential impact of this model on the development of the maritime industry as a whole.

The integration of Artificial Intelligence (AI) into education offers transformative potential for personalized learning but raises critical ethical concerns regarding transparency, privacy, and bias. This study addresses the challenge of predicting and quantifying. The transparency score is a composite metric (scaled 0-100) that quantifies the degree to which AI-driven educational decisions are interpretable, explainable, and accountable to stakeholders, including students, educators, and administrators. It is computed based on factors such as algorithmic explainability, decision traceability, clarity of stakeholder communication, and compliance with ethical AI guidelines-a key factor for building stakeholder trust. We introduce EduTransNet, a novel deep neural network architecture designed to predict transparency scores based on student demographics, academic performance, and ethical perception features collected from 2,847 students across three Pakistani universities. EduTransNet incorporates fairness-aware mechanisms, including demographic parity constraints and interpretability layers, to ensure ethical predictions. Comparative evaluation against Support Vector Regression, Linear Regression, and Random Forest Regression demonstrates EduTransNet's superior performance (R² = 0.998, p < 0.001). These results indicate exceptional predictive accuracy, with the R-squared value of 0.998 demonstrating that EduTransNet explains 99.8% of the variance in transparency scores-substantially outperforming traditional regression models. The MSE is approximately 6.2 points on a 100-point scale, which is considered excellent for educational outcome prediction tasks, where typical models achieve MSEs exceeding 100. The model not only predicts student outcomes more accurately but also integrates fairness mechanisms to mitigate bias and enhance transparency, offering valuable insights for educational stakeholders, policymakers, and AI developers to enhance accountability and guide ethical AI implementation in educational contexts. This research offers a practical framework for ethical AI governance in education, providing guidelines for educators, policymakers, and AI developers to ensure the responsible implementation of intelligent educational technologies.

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

Disclosure: H.M. Heshmati: None. N. Zameni: None.Background: Robotic surgery is quickly replacing traditional surgery performed by humans. It has significantly influenced the field of endocrine surgery. This review presents an update on the use of robotic surgery and artificial intelligence for adrenal tumors. Methods: A systematic search of literature was conducted using the search terms robotic surgery, artificial intelligence, adrenal tumors, safety, outcome, and cost. Results: Adrenal tumors are found in 10% of people who have imaging of the adrenal gland. They are typically unilateral and benign in the majority of cases. Robotic systems such as da Vinci Surgical System provide surgeons with enhanced dexterity and 3D visualization. Autonomous surgical robots can be categorized into various levels of autonomy including supervised autonomy (e.g., performance of specific tasks with human oversight), collaborative autonomy (e.g., robots working alongside human surgeons, sharing tasks and responsibilities), and fully autonomous systems (e.g., robots performing entire surgical procedures independently with human intervention when necessary). Robotic surgery allows access through tight spaces to remove adrenal tumors with small incision and minimal impact on surrounding tissues. The review of 1,162 patients who underwent adrenalectomy (robotic surgery in 747 patients, laparoscopic adrenalectomy in 415 patients) showed comparable blood loss and complications with the two techniques but patients with robotic surgery had significantly shorter hospital stay. The future of robotic surgery can be revolutionized by the integration of autonomous robots and artificial intelligence (AI), a field rapidly advancing. These innovations promise to further enhance the precision, safety, and effectiveness of surgery involving the adrenal glands. AI-powered systems provide real-time guidance and feedback to surgeons, enhancing decision-making and surgical precision. Autonomous systems can ensure consistent execution of surgical tasks, reducing variability and enhancing patient safety. AI algorithms can analyze extensive surgical data to refine techniques and predict potential complications, improving surgical outcomes. AI can customize surgical plans based on individual patient anatomy and pathology, potentially leading to better outcomes. The current high cost of these new procedures is a potential limitation for their use. Conclusion: Robotic surgery represents significant progress in minimally invasive techniques for the removal of adrenal tumors. It has improved dexterity and precision, and is associated with shorter recovery time compared to laparoscopic adrenalectomy. Collaboration between human surgeons and robotic systems can improve patient care and surgical outcomes. The precision, safety, and effectiveness of the adrenal surgery can be further enhanced by the integration of AI and robotics.Presentation: Saturday, July 12, 2025

The integration of data science and Artificial Intelligence (AI) into geospatial analysis has revolutionized Earth observation, driving progress towards the Sustainable Development Goals (SDGs). Recent developments in data acquisition technologies like high-resolution satellites and sensors have generated vast and diverse datasets for monitoring environmental changes and managing natural resources. Concurrently, innovations in Machine Learning (ML) and AI have significantly enhanced the processing, analysis and interpretation of this geospatial data. Techniques such as deep learning, spatial data mining and automated feature extraction are now essential to deriving actionable insights from complex geospatial datasets. This paper reviews the latest trends and breakthroughs in the application of AI/ML to geospatial data for Earth observation, emphasizing their role in advancing the SDGs. Key areas of focus include improved algorithms for land cover classification, disaster prediction and climate monitoring. These technologies enable more precise and timely responses to environmental challenges, such as deforestation, urbanization and natural disasters, thereby supporting sustainable management and policymaking. Furthermore, the integration of AI with geospatial data enhances predictive modelling, scenario planning and decision support systems, which are critical for achieving SDG targets related to environmental sustainability and resilience. The synthesis of recent research and technological developments highlights the potential of AI/ML approaches for geospatial analysis and their alignment with global sustainability goals. The outcomes underline the requirement for continued innovation and collaboration across disciplines to fully leverage these advancements for effective Earth observation and sustainable development.

Background. In the context of the continuous shifts in the modern labour market and the escalating competitiveness among students of maritime specialties, there is an increasing demand for the training of highly qualified specialists in the maritime industry, who are able to interact effectively with international partners at a professional level. In this respect, the use of generative artificial intelligence (AI) to improve foreign language teaching methods for students of maritime subjects is especially relevant. This is fully justified under the conditions of globalisation and the digital transformation of the educational sphere. Purpose. The purpose is to determine the effectiveness of generative AI application in the process of building foreign language professional communicative competence of maritime students by modelling realistic scenarios of job interviews in shipping companies. Materials and methods. The present study adopted a comprehensive approach, integrating theoretical and empirical methods of analysis. The theoretical basis was established through a comprehensive review of normative and educational documents, with the aim of identifying the fundamental requirements for the educational process. This was complemented by a thorough examination of scientific and educational literature, as well as methodological literature, on the application of AI in education and linguodidactics, thus enabling the identification of current trends and the potential of technologies for teaching maritime students. The empirical element of the study involved modelling, describing and analysing professional interview scenarios using generative AI models, the observation of students' learning activities, and the conduction of interviews to obtain feedback on the perception and effectiveness of the new techniques. Results. The research yielded an innovative methodology for training students of maritime specialities at Sevastopol State University for professional interviews in international shipping companies. The developed three-stage training model, combining the creation of a database of questions, simulation of interviews using generative (AI) and practising skills through business games, demonstrated high efficiency in the formation of both linguistic and professional competences of students, in addition to soft skills deemed integral for a successful career in the maritime industry. The integration of generative AI within the training framework of maritime students has been found to expand the scope of opportunities for effective preparation for professional interviewing. The implementation of this methodology has been identified as a significant contributor to the modernisation of the educational process, rendering it more personalised, effective and aligned with the precise requirements of the contemporary labour market.

Integrating blockchain technology with artificial intelligence (AI) i.e., blockchain Intelligence makes an extremely powerful tool that solves many multidimensional problems in several domains. Blockchain technology has the potential to provide links to shared data, transactions, and records in a decentralized, safe, and reliable manner, including the information and decision-making capability of AI which makes machines similar as capable as humans. This study is intended to present an updated systematic review of the integration of Blockchain and AI in various application areas. We have studied and summarized more than 100 research papers to present an updated version of the review. We also discuss the future of Blockchain technologies with AI. By integrating these two technologies results increases the security, efficiency, and productivity of the applications. Past works feature a few possible advantages of integration of Blockchain and AI, yet just give a restricted hypothetical system to depict forthcoming certifiable combination instances of the two advances. We survey and orchestrate surviving exploration on the integration of AI and Blockchain are other ways around to thoroughly build up a future research plan on the fusion of the two innovations. We also proposed an agenda to develop a secure system of cyber threat intelligence information exchange by using features of blockchain and artificial intelligence. This paper mainly focusses on explaining how collaboration of blockchain and AI gives immense boost in latest domains like Cybersecurity, Healthcare, Supply Chain Management, Finance and Banking and Social Media Analytics.

Soil piping, an insidious form of internal erosion within soil structures, presents substantial challenges in land stability and infrastructure integrity. Detecting and pre- emptively managing soil piping require robust and non- destructive methodologies to identify vulnerable areas. Ground- penetrating radar (GPR) has emerged as a valuable tool in this context, offering promising insights into subsurface conditions and potential erosion pathways. This paper presents an overview of recent advancements in the integration of artificial intelligence (AI) techniques with GPR data processing and interpretation. The recent speedy development of AI technologies (machine learning, deep learning, etc.) provides a great opportunity to develop reliable, accurate and time- effective processing solutions to advance most of the current and emerging Earth observation and remote sensing technologies. By combining the prowess of GPR's non-destructive subsurface imaging with the intelligence of AI-driven data interpretation, we can better understand the underlying complexities of different materials and develop more efficient, accurate, and reliable solutions for piping.

The article comprehensively examines the profound impact of digital transformation on the processes of strategic decision-making within contemporary organizations, with a specific emphasis on the integration of artificial intelligence (AI) solutions and advanced big data analytics into managerial frameworks. The analysis delineates the principal directions of transformation in strategic management paradigms, substantiating the pivotal role played by intelligent digital instruments in strengthening organizational competitiveness, adaptability, and innovation capacity. A structured classification of essential data analytics tools employed in strategic decision support is presented, accompanied by meticulously selected practical case studies that demonstrate the successful application and tangible benefits of AI technologies across diverse sectors, including manufacturing, services, and public administration. Furthermore, the research critically assesses key obstacles to digital adaptation, such as data integrity and quality challenges, inadequate digital literacy among personnel, resistance to organizational change, and ethical dilemmas associated with algorithmic and automated decision-making processes. The proposed conceptual framework, termed the Digital Analytical Strategic Management Architecture (DASMA), encompasses multiple layers of analytics: descriptive, diagnostic, predictive, prescriptive, and cognitive. This model advocates decisionmaking logics that prioritize human-centricity, adaptability, and organizational transparency, particularly through the integration of explainable artificial intelligence and proactive strategic foresight. Notably, the DASMA model supports the iterative evolution of managerial approaches, enabling organizations to responsively navigate volatility and uncertainty in their operational environments.The results of this study make a significant contribution to the methodological foundation for the design and implementation of data-driven management strategies. The findings are intended to enhance executive-level decision-making, facilitate the operationalization of digital transformation frameworks, and foster sustainable organizational resilience and competitiveness in the face of ongoing digital disruption and complex, unpredictable market dynamics.

The article explores the role of digitalization in the development of public finance, highlighting its key advantages, challenges, and directions for implementing digital technologies in financial resource management. It is determined that digitalization is a key factor in enhancing the transparency, efficiency, and accountability of budgetary processes. The integration of big data analytics, artificial intelligence, and blockchain technologies contributes to process automation, improves revenue and expenditure forecasting, and enables the timely detection of deviations or violations.The study examines the implementation of digital platforms such as ProZorro and “Diia,” which have significantly increased the transparency and accountability of budget expenditures while strengthening public trust in government authorities. It has been established that expanding their functionality through the integration of artificial intelligence and automation of financial operations opens new opportunities for improving public finance management efficiency.At the same time, the digitalization process faces challenges, including the insufficient level of digital literacy among civil servants, uneven access to digital services across regions, high modernization costs for infrastructure, and the complexity of integrating new technologies with existing systems. Overcoming these barriers requires a systematic approach that includes developing digital infrastructure, reforming legislation, fostering financial technologies, and strengthening cybersecurity.The study analyzes international experience in the digitalization of public finance across various countries. Using China as an example, which demonstrates a high level of integration of information and communication technologies into financial management, it is shown that their application enhances transparency, reduces costs, detects deviations, improves imperative planning, and increases the accountability of public officials. Moreover, digitalization contributes to strengthening a country’s financial sovereignty. In particular, the use of automated control and monitoring systems significantly enhances the accountability of government agencies. China’s successful experience demonstrates the potential for effectively integrating digital technologies with public financial policy. For Ukraine, this opens up prospects for introducing a mixed economic system that combines market mechanisms with effective state regulation. The implementation of such approaches, coupled with profound social transformations, will contribute not only to the digitalization of public finance but also to strengthening social security and ensuring the long-term development of the country.The research findings have both theoretical and practical significance for the development of a national strategy for the digitalization of public finance. A set of measures is proposed to enhance public trust in government institutions, reduce financial losses, optimize expenditures, and improve Ukraine’s international competitiveness. The integration of digital technologies will not only enhance the transparency of budgetary processes but also ensure the long-term stability of the country’s financial system. Further research should focus on improving digital ecosystems, adapting international experience, and assessing the socio-economic impact of digitalization.

The increasing complexity and uncertain system of modern discrete event system (DES) challenge traditional model-based control approaches, while artificial intelligence (AI) techniques offer powerful data-driven decision-making capabilities but lack formal guarantees. This review surveys recent research on the integration of AI with DES and supervisory control theory. Following a systematic literature mapping methodology, the literature is organized using a taxonomy based on three orthogonal perspectives: control and decision paradigm, system capability and property, and application and operational objectives. The review highlights how learning-based methods enhance adaptability and performance in DES, while also exposing persistent challenges related to safety, nonblocking behavior, data efficiency, and interpretability. By structuring existing approaches and identifying open issues, this review provides a coherent overview of the current research landscape and outlines key directions for future work on AI-enabled DES.

The acceptance of, perceived advantages to, and skepticism toward the integration of artificial intelligence (AI) in undergraduate education is investigated in this paper. In this study, a total of 675 students from six Romanian universities answered a self-administered online questionnaire evaluating three main aspects: AI acceptance, AI benefits, and AI skepticism. While AI skepticism has a modest but substantial negative influence (β = −0.113, p = 0.001), results show that AI benefits favorably predict AI acceptance (β = 0.541, p = 0.001). Whereas AI skepticism negatively correlates with AI acceptance (r = −0.124, p = 0.001), correlational analysis reveals a high positive association between AI acceptance and AI benefits (r = 0.544, p = 0.001). Despite concerns about its limitations, the regression model suggests that students’ willingness to embrace AI in education is mostly driven by its perceived advantages. This explains 30.8% of the variance in AI acceptance (R2 = 0.308, F(2, 641) = 142.909, p &lt; 0.001). These results highlight the importance of techniques that improve perceived benefits while addressing uncertainty since they offer insightful analysis of student attitudes regarding artificial intelligence integration in higher education. By guiding policy decisions and educational activities meant to maximize AI-driven learning environments, this study adds to the current conversation on artificial intelligence adoption in education.