From Principles to Practice: An Operational Framework for Equitable Artificial Intelligence in Educational Assessment

Carotid plaque is a biomarker of generalized atherosclerosis, and may predict ischemic stroke. Carotid intima-media thickness (C-IMT) measurement with ultrasonography imaging could capture the condition of carotid plaque. However, manual measurement of C-IMT is observer- dependent, resulting in observer bias and low reproducibility. In this study, we develop artificial intelligence (AI) framework that could automatically measure the C-IMT, and compared it with C-IMT measured by board of expert. This is a retrospective study done in Dr. Moewardi General Hospital, Surakarta, Indonesia. Carotid B-mode ultrasonography images were measured by panel of expert and by AI. After annotation process on Neurabot platform, AI could detect region of interest (ROI), and would do segmentation on the area to measure C-IMT autonomously. Dependent T-test was used to evaluate validity, and Cronbach’s alpha was used to find the reliability of C-IMT measured by panel of expert and AI. There was strong correlation (r=0.874; p=0.014) on dependent t-test for C-IMT measured by AI with C-IMT measured by board of expert. The internal consistency reliability coefficients (Cronbach’s alpha) were 0.938 and 0.909, for pretest and posttest, respectively. We also analyzed the test-retest reliability by comparing pretest and posttest score with dependent t-test, and we observed strong correlation with r=0.871 (p=0.000). AI developed on Neurabot platform are valid and reliable to measure C-IMT.

This study aimed to develop and evaluate an artificial intelligence (AI) framework for detecting dental restorations and prosthesis devices on panoramic radiographs (PRs). Detecting these elements is essential for enhancing automated reporting, improving the accuracy of dental assessments, and reducing manual examination time. A fast region-based convolutional neural network (Fast R-CNN) was trained using 186 PRs for the training set and 42 for validation. The model's performance was assessed on an external test dataset of 1133 PRs. Seven dental restorations and prosthesis devices were targeted: appliance, bridge, endodontic filling, crown filling, implant, retainer, and single crown. Precision, recall, and F1-score were calculated for each element to measure detection accuracy. The AI framework achieved high performance across all categories, with precision, recall, and F1-scores as follows: appliance (0.79, 0.96, 0.87), bridge (0.91, 0.86, 0.89), endodontic filling (0.98, 0.98, 0.98), crown filling (0.95, 0.95, 0.95), implant (0.99, 0.97, 0.98), retainer (0.98, 0.98, 0.98), and single crown (0.94, 0.96, 0.95). The system processes one panoramic image in under 30 seconds. The AI framework demonstrated high recall and efficiency in detecting dental prosthesis and other dental restorations on PRs. Its application could significantly streamline dental diagnostics and automated reporting, enhancing both the speed and accuracy of dental assessments. This study highlights the potential of AI in automating the detection of multiple dental restorations and prosthesis on PRs, offering a valuable tool for dental professionals to improve diagnostic workflows.

The authors present a new Framework of Artificial Intelligence which analyzes the key elements of transformational AI in industry. The State of the Art of Artificial Intelligence is gleaned from an examination of what has been done in the past, presently in the last decade and what is predicted for future decades. The paper will highlight the biggest changes in AI, important influencers to adoption/diffusion and give examples of how these technologies have and will be applied in three key industrial sectors, including agriculture, education and healthcare. Next the research examines seven driving triggers of cost, speed, accuracy, diversity/inclusion, interdisciplinary research/collaboration and ethics/trustworthiness that are accelerating AI development and concludes with a discussion of what are the critical success factors for industry to be transformational in AI.

A Framework for Collaborative Artificial Intelligence in Marketing

Purpose The purpose of this paper is to propose a practical framework for embedding artificial intelligence (AI)-driven people analytics into business strategy to enhance workforce resilience and organizational agility. Across organisations, HR leaders are under pressure to make faster, smarter people decisions. AI and analytics promise sharper insight, yet many teams still treat data as a reporting task rather than a strategic discipline. Design/methodology/approach This paper presents a practical framework for embedding AI driven people analytics into business strategy to strengthen workforce resilience and agility. Findings Drawing on examples from logistics and higher education, it introduces three levers sensing workforce risks, predicting outcomes and reconfiguring capabilities. Originality/value Grounded in resource based and dynamic capabilities perspectives, the model shows how data can become foresight and foresight can drive decisive action. A managerial roadmap, ethical guidance and practitioner take away conclude the paper, helping leaders move from dashboards to direction.

With the change of the fourth generation of information technology represented by artificial intelligence, higher education is gradually stepping into the stage of intelligent development, and leisure sports majors as one of the majors that are booming, which puts forward higher requirements on the teaching power of teachers of leisure sports majors. Therefore, studying the current situation of talent cultivation of leisure sports majors under artificial intelligence plays a good role in promoting the overall improvement of the teaching quality of leisure sports majors. In this paper, the current situation of talent training and classroom teaching of leisure sports majors is studied based on a convolutional neural network in the artificial intelligence perspective, and the framework of artificial intelligence technology for leisure sports education is constructed. It can be seen that the attitude of leisure sports students towards theory class is better; 40.93% think theory class is very important, 38.13% think theory class is more important, and 19.27% think the importance of theory class is average. Students’ attitudes toward practical classes were better than those toward theoretical classes, with 63.71% considering practical classes very important and 26.85% considering them more important. From the analysis results, we can see that there are still problems of weak basic theory research, confusion of cultivation goals, lack of science in curriculum setting, and lack of teachers in cultivating talents in leisure sports majors. In summary, this paper argues that colleges and universities and college teachers should take the initiative to apply the technical framework of artificial intelligence in leisure sports education, strengthen theoretical research, make up for the shortcomings of teaching materials, as well as integrate training objectives, improve curriculum settings, enhance faculty strength, investigate the talent specification needs of the market in-depth, proactively meet the arrival of the era of artificial intelligence, and contribute to the cultivation of talents in leisure sports majors.

Large earthquakes (EQs) occur at surprising loci and timing, and their descriptions remain a long-standing enigma. Finding answers by traditional approaches or recently emerging machine learning (ML)-driven approaches is formidably difficult due to data scarcity, interwoven multiple physics, and absent first principles. This paper develops a novel artificial intelligence (AI) framework that can transform raw observational EQ data into ML-friendly new features via basic physics and mathematics and that can self-evolve in a direction to better reproduce short-term large EQs. An advanced reinforcement learning (RL) architecture is placed at the highest level to achieve self-evolution. It incorporates transparent ML models to reproduce magnitude and spatial location of large EQs (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$M_w \\ge $$\\end{document} 6.5) weeks before of the failure. Verifications with 40-year EQs in the western U.S. and comparisons against a popular EQ forecasting method are promising. This work will add a new dimension of AI technologies to large EQ research. The developed AI framework will help establish a new database of all EQs in terms of ML-friendly new features and continue to self-evolve in a direction of better reproducing large EQs.

Estimates on miss rates for upper gastrointestinal neoplasia (UGIN) rely on registry data or old studies. Quality assurance programs for upper GI endoscopy are not fully established owing to the lack of infrastructure to measure endoscopists' competence. We aimed to assess endoscopists' accuracy for the recognition of UGIN exploiting the framework of artificial intelligence (AI) validation studies. Literature searches of databases (PubMed/MEDLINE, EMBASE, Scopus) up to August 2020 were performed to identify articles evaluating the accuracy of individual endoscopists for the recognition of UGIN within studies validating AI against a histologically verified expert-annotated ground-truth. The main outcomes were endoscopists' pooled sensitivity, specificity, positive and negative predictive value (PPV/NPV), and area under the curve (AUC) for all UGIN, for esophageal squamous cell neoplasia (ESCN), Barrett esophagus-related neoplasia (BERN), and gastric adenocarcinoma (GAC). Seven studies (2 ESCN, 3 BERN, 1 GAC, 1 UGIN overall) with 122 endoscopists were included. The pooled endoscopists' sensitivity and specificity for UGIN were 82 % (95 % confidence interval [CI] 80 %-84 %) and 79 % (95 %CI 76 %-81 %), respectively. Endoscopists' accuracy was higher for GAC detection (AUC 0.95 [95 %CI 0.93-0.98]) than for ESCN (AUC 0.90 [95 %CI 0.88-0.92]) and BERN detection (AUC 0.86 [95 %CI 0.84-0.88]). Sensitivity was higher for Eastern vs. Western endoscopists (87 % [95 %CI 84 %-89 %] vs. 75 % [95 %CI 72 %-78 %]), and for expert vs. non-expert endoscopists (85 % [95 %CI 83 %-87 %] vs. 71 % [95 %CI 67 %-75 %]). We show suboptimal accuracy of endoscopists for the recognition of UGIN even within a framework that included a higher prevalence and disease awareness. Future AI validation studies represent a framework to assess endoscopist competence.

The article presents a review of the phenomenon of understanding the meaning of the natural language and, more broadly, the meaning of the situation in which the cognitive agent is located, considering the context. A specific definition of understanding is given, which is at the intersection of neurophysiology, information theory and cybernetics. The scheme of an abstract architecture of the cognitive agent (of arbitrary nature) is offered, which states that an agent with such architecture can understand in the sense described in the paper. It also provides a critique of J. Searle’s mental experiment “The Chinese Room” from the point of view of the construction of artificial cognitive agents within a hybrid paradigm of artificial intelligence. The novelty of the presented work is based on the application of the author’s methodological approach to the construction of artificial cognitive agents. It not only considers the perception of external stimuli from the environment, but also the philosophical problem of “understanding” by the artificial cognitive agent of its sensory inputs. The relevance of the work follows from the renewed interest of the scientific community in the theme of Strong Artificial Intelligence (or AGI). The author's contribution consists in comprehensive treatment from different points of view of the theme of understanding perceived by artificial cognitive agents. It involves the formation of prerequisites for the development of new models and the theory of understanding within the framework of artificial intelligence, which in the future will help to build a holistic theory of the nature of human mind. The article will be interesting for specialists working in the field of artificial intellectual systems and cognitive agents construction, as well as for scientists from other scientific fields – first of all, philosophy, neurophysiology and psychology.

The research aims to identify the patterns of the digital evolution of the visual language and the system of forming artistic images in the field of artificial intelligence, which has gone through a long path from the first experiments with abstract forms to the creation of photorealistic images, gradually forming a kind of “new” figurative system. The article provides an analytical overview of the main stages of the evolutionary development of visual language, which include the emergence of algorithmic art, the introduction of generative networks, and the formation of modern computer architectures. Artificial intelligence has not only transformed the traditional approach to creating unique visual content, but has also made it fast and accessible to a wide range of users. The scientific novelty of the research lies in using an interdisciplinary approach to search for key features of digital technologies that have found wide application both in art and in design as an accessible tool for experimenting with new forms, styles, and techniques. As a result of the research, key features of digital technologies within the framework of artificial intelligence were identified, which are used in modern art and design for the formation of visual content. Based on the conclusions given in the article, the authors believe that such technologies will continue to develop and improve, becoming an increasingly important component of the creative process.

Despite significant medical advancements, cancer remains the second leading cause of death in the US, causing over 600,000 deaths per year. One emerging field, pathway analysis, is promising but still relies on manually derived wet lab data, which is time-consuming to acquire. This work proposes an efficient, effective, end-to-end framework for Artificial Intelligence (AI)-based pathway analysis that predicts both cancer severity and mutation progression in order to recommend possible treatments. The proposed technique involves a novel combination of time-series machine learning models and pathway analysis. First, mutation sequences were isolated from The Cancer Genome Atlas (TCGA) Database. Then, a novel preprocessing algorithm was used to filter key mutations by mutation frequency. This data was fed into a Recurrent Neural Network (RNN) that predicted cancer severity. The model probabilistically used the RNN predictions, information from the preprocessing algorithm, and multiple drug-target databases to predict future mutations and recommend possible treatments. This framework achieved robust results and Receiver Operating Characteristic (ROC) curves (a key statistical metric) with accuracies greater than 60%, similar to existing cancer diagnostics. In addition, preprocessing played a key role in isolating a few hundred key driver mutations per cancer stage, consistent with current research. Heatmaps based on predicted gene frequency were also generated, highlighting key mutations in each cancer. Overall, this work is the first to propose an efficient, cost-effective end-to-end framework for projecting cancer prognosis and providing possible treatments without relying on expensive, time-consuming wet lab work.

IT-based tunnelling risk management system (IT-TURISK) – Development and implementation

The significance of the topic of digital human rights in the context of artificial intelligence and globalization lies in its examination of the intricate relationships between technological innovations, global dynamics, and human rights in the digital era. With the rapid advancement of artificial intelligence, digital rights demand heightened attention and adaptation. Purpose of the Study. The study aims to conceptualize digital human rights within the framework of artificial intelligence and globalization. The objectives are: 1. To analyze digital rights in the European Union context and define their core elements and importance. 2. To examine key aspects of digital rights protection in the context of globalization. 3. To clarify digital rights in light of the development of artificial intelligence (AI) and the emerging realities of the digital world. Study Findings. The study demonstrates that globalization contributes to the international standardization of digital rights, but also introduces challenges due to regional and national disparities in legal approaches. It evaluates the impact of AI on digital rights, including issues related to automated decision-making, algorithmic bias, and personal data monitoring. The research provides an overview of fundamental digital rights concepts, such as the right to privacy, freedom of expression, access to information, and non-discrimination in the digital environment. It also explores the ethical considerations of AI in relation to digital rights and examines new legal avenues, such as enhanced personal data protection and greater transparency of algorithms. The investigation into digital rights in the context of AI and globalization reveals the complex interplay between technology, human rights, and global processes. It emphasizes both the opportunities for progress and the challenges that modern society faces. Theoretical advancements in this field could help define new rights, such as the right to digital identity, protection of personal data, access to information, and freedom on the Internet. Furthermore, in the era of globalization, there is a pressing need to harmonize approaches to regulating digital rights on an international scale. To bolster cybersecurity, practical measures could include the development of technological solutions aimed at safeguarding personal data, as well as enhancing the security of digital infrastructure at both national and international levels.

This study examines the ethical and privacy challenges of integrating generative artificial intelligence (AI) into electronic health record (EHR) systems, focusing on Tanzania's healthcare context. It critically analyses the extent to which Tanzania's Policy Framework for Artificial Intelligence in the Health Sector (2022) addresses these challenges and proposes regulatory and practical safeguards for responsible generative AI deployment. A systematic scoping review was conducted using PubMed, IEEE Xplore, Scopus and Google Scholar to identify relevant studies published between 2014 and 2024. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines informed the search and selection process. Fourteen studies met the inclusion criteria and were thematically analysed to identify key ethical and privacy concerns of generative AI in healthcare. Moreover, a policy analysis of Tanzania's AI framework was conducted to assess its alignment with global best practices and regulatory preparedness. The review identified six key ethical and privacy challenges associated with generative AI in EHR systems: data privacy and security risks, algorithmic bias and fairness concerns, transparency and accountability issues, consent and autonomy challenges, human oversight gaps and risks of data re-identification. The policy analysis revealed that while Tanzania's AI framework aligns with national health priorities and promotes capacity building and ethical governance, it lacks generative AI-specific guidelines, regulatory clarity and resource mobilisation strategies necessary for healthcare settings. Integrating generative AI into Tanzania's EHR systems presents transformative opportunities and significant ethical and privacy risks. Tanzania's policy framework should incorporate AI-specific ethical guidelines, operationalise regulatory mechanisms, foster stakeholder engagement through participatory co-design and strengthen infrastructural investments. These measures will promote ethical integrity, enhance patient trust and position Tanzania as a regional leader in responsible AI use in healthcare.

This study introduces an adaptive edge-federated artificial intelligence (AI) framework that employs contactless biosensing and multimodal physiological intelligence to enable accurate, real-time, and privacy-preserving menstrual health prediction. Traditional menstrual cycle tracking methods, such as manual logs or wearable sensors, often rely on skin contact, are prone to measurement errors, and raise privacy concerns due to centralized data storage. To overcome these limitations, the proposed framework integrates radar-based respiration sensing, photoplethysmography (PPG), and LiDAR-assisted microvascular mapping to non-invasively monitor vital physiological signals including heart rate, vascular activity, and breathing rhythm. The multimodal biosignals are analyzed locally through an adaptive edge-learning engine that continuously updates and personalizes predictions without transferring raw data to central servers. A federated optimization mechanism enables secure, decentralized model training, ensuring that sensitive reproductive health data remains on user devices while still benefiting from global model improvements. By dynamically adjusting to individual physiological variations and irregular cycle patterns, the system enhances prediction accuracy, reduces latency, and preserves user privacy. This intelligent, non-invasive framework offers a next-generation approach to menstrual health management, combining edge intelligence, federated learning, and multimodal biosensing to deliver a discreet, adaptive, and trustworthy reproductive health solution.•Contactless biosensing using radar, PPG, and LiDAR for real-time menstrual health tracking.•Edge-federated learning enables decentralized, privacy-preserving AI model training.•Adaptive multimodal AI intelligence enhances prediction accuracy for irregular cycles.