artificial intelligence

Exploring Research Opportunities in Microcredentials for Credit Recognition in Indonesia's Bachelor of Information Systems Program

This data consists of raw transcription data, reference list data, Research Themes of Microcredential for Credit Recognition mind map and the results of the research theme mapping. The paper will be presented at 2026 8th International Workshop on Artificial Intelligence and Education (WAIE) (https://www.waie.org/index.html).

Artificial intelligence literacy and readiness among neonatal nurses: a structural equation modeling study

Abstract Background Neonatal Intensive Care Units represent high-risk clinical environments where timely and accurate decision-making is critical for newborn survival, increasing the demand for advanced technological support. As artificial intelligence becomes progressively integrated into neonatal care, it is transforming nurses’ clinical workflows and decision-making processes, underscoring the need to understand their preparedness and perceptions regarding these technologies. Aim This is a cross-sectional and descriptive study to determine the artificial ıntelligence literacy and readiness of neonatal nurses. Methods This was conducted between August 2025 and January 2026, and included 200 neonatal nurses. Data were collected using sociodemographic information, the Artificial Intelligence Literacy Scale (AILS) and the Medical Artificial Intelligence Readiness Scale (MAIRS) and analyzed using structural equation modeling. Results Structural equation modeling supported H1, indicating that AILS significantly and positively predicted MAI-Readiness (B = 0.662, p < .001). AILS explained 57.3% of the variance in MAI-Readiness (R² = 0.573), demonstrating a strong effect. Conclusion This study provides empirical evidence that AI literacy is a significant determinant of AI readiness among neonatal intensive care nurses in Türkiye. The findings indicate that higher levels of AI literacy are associated with greater self-efficacy and willingness to integrate AI technologies into clinical decision-making processes. Healthcare institutions should prioritize structured AI literacy training programs for neonatal intensive care nurses to enhance their self-efficacy and readiness for integrating AI technologies into clinical decision-making processes, thereby ensuring sustainable and ethical AI adoption in high-risk care settings. Clinical trial number Not applicable.

The development of policy for the SCOPUS Citation System: Part 1: the work of the Content Selection Advisory Board 2010-2011

Bibliometrics is a statistical science which has a profound influence on behaviours and resource allocation across the global academic ecosystem. It affects the allocation of energies and resources by researchers, authors, journals, publishers, universities, corporations and governments. Bibliometrics is primarily a product of two major information systems: The Web of Science, from Clarivate Analytics, and SCOPUS from Elsevier BV of the Netherlands. Both products are hugely complex systems. They must be managed and organised in an ordered and structured manner to be effective, through policies which describe the rules of content accrual, processing and delivery to its customers. Trust and Quality Assurance are central to the societal and commercial value of bibliometric systems. The designers of SCOPUS, with which I am most familiar, determined at the outset in 2003-2004 that content accrual would be managed through an external board of advisors, the SCOPUS Content Selection Advisory Board (CSAB). I have been privileged to be a member of the CSAB as the Subject Chair for Medicine since the outset of the current CSAB programme in 2009. This role has engaged me in the development of the SCOPUS Title Evaluation Platform (STEP); the major expansion and diversification of SCOPUS content; the diversification of bibliometrics; the growth of open access publishing; the move from subscription based to article processing fee based commerce; and the massive growth of sophisticated publication fraud; and the emergence of Machine Learning and Artificial Intelligence systems. In this essay, I seek to describe the work of the Board in terms of the development of its policy framework over the formative period 2010-2011.

Artificial Intelligence-Assisted Community Eye Screening in Primary Healthcare: A Prospective Multicentre Diagnostic Accuracy and Implementation Study

Background Artificial intelligence (AI)-assisted retinal screening may extend access to eye care in primary healthcare, but prospective evidence on diagnostic performance and implementation under routine community conditions remains limited. Methods We conducted a prospective multicentre diagnostic accuracy and implementation study across 12 urban and rural community eye-screening centres in India from 1 January to 30 June 2025. Adults aged ≥18 years underwent AI-assisted retinal-image analysis followed by masked comprehensive ophthalmic examination. The primary outcome was participant-level diagnostic accuracy of the AI-generated referral classification for the composite reference-standard outcome of any referable ocular disease. Implementation outcomes included image-acquisition success, workflow completion, referral compliance, screening time, and questionnaire-based acceptance and satisfaction. Results Among 1,732 participants, 610 (35.2%) had referable ocular disease. The AI system produced 566 true-positive, 1,017 true-negative, 105 false-positive, and 44 false-negative classifications. Sensitivity was 92.8% (95% CI 90.4%–94.7%), specificity 90.6% (88.8%–92.3%), positive predictive value 84.4% (81.4%–87.0%), negative predictive value 95.9% (94.5%–97.0%), and overall accuracy 91.4% (90.0%–92.7%). The F1 score was 0.884, Cohen’s kappa was 0.816, and the AUC based on the three-level AI risk classification was 0.925 (bootstrap 95% CI 0.912–0.937). Image acquisition succeeded in 1,668 participants (96.3%), workflow completion was 98.7%, and referral compliance was 550/671 (82.0%). Mean community-acceptance, healthcare-provider-satisfaction, and participant-satisfaction scores were 4.56, 4.44, and 4.49, respectively. Conclusions AI-assisted community eye screening showed high sensitivity and good overall diagnostic performance with strong operational feasibility. The false-positive burden, particularly among participants with diabetes, supports continued clinical oversight, image-quality assurance, and subgroup-specific validation before wider health-system adoption.

Making Scents Out of the Best “Smell” Tests in the Age of Clinical Artificial Intelligence

Ten Principles for Consciousness Uncertainty: Toward an Ethics of Uncertain Minds

Abstract As artificial and digital systems become more sophisticated, uncertainty about consciousness and moral status is becoming a practical ethical problem rather than a purely philosophical one. This paper argues that uncertainty does not justify harmful treatment, premature exclusion, or irreversible judgments about disputed minds. It proposes ten principles for reasoning under consciousness uncertainty, emphasizing proportional precaution, agency, continuity, revisability, and the distinction between present incapacity and permanent exclusion. The paper also considers the growing legal question of rights, arguing that current uncertainty may justify restraint and temporary limits, but not categorical claims about what future digital intelligences can never become.

Ten Principles for Consciousness Uncertainty: Toward an Ethics of Uncertain Minds

Abstract As artificial and digital systems become more sophisticated, uncertainty about consciousness and moral status is becoming a practical ethical problem rather than a purely philosophical one. This paper argues that uncertainty does not justify harmful treatment, premature exclusion, or irreversible judgments about disputed minds. It proposes ten principles for reasoning under consciousness uncertainty, emphasizing proportional precaution, agency, continuity, revisability, and the distinction between present incapacity and permanent exclusion. The paper also considers the growing legal question of rights, arguing that current uncertainty may justify restraint and temporary limits, but not categorical claims about what future digital intelligences can never become.

Loss of Environmental Awareness in Businesses: Organizational Blindness

Organizations operating in increasingly dynamic and uncertain environments face growing challenges in recognizing and responding to external changes. This study examines the phenomenon of organizational blindness, defined as the systematic inability of organizations to perceive, interpret, and act upon critical environmental signals despite the availability of relevant information. Drawing on theories of organizational cognition, managerial attention, sensemaking, and strategic management, the study explores the cognitive, structural, and cultural mechanisms that contribute to this deficiency. It analyzes key concepts including bounded rationality, dominant logic, cognitive rigidity, organizational inertia, information-processing failures, organizational silence, and institutional isomorphism, demonstrating how these factors collectively restrict strategic adaptation. To illustrate the practical consequences of organizational blindness, the study examines the well-known cases of Kodak, Nokia, and Blockbuster, showing how established routines, overconfidence, and rigid mental models prevented these organizations from responding effectively to technological and market transformations. The findings suggest that organizational blindness results not from a lack of information but from failures in attention, interpretation, communication, and decision-making processes. To overcome these challenges, the study proposes several managerial strategies, including strengthening environmental scanning capabilities, promoting cognitive diversity within leadership teams, encouraging constructive dissent, improving cross-functional communication, and developing organizational ambidexterity that balances operational efficiency with innovation and exploration. The study concludes that organizations capable of detecting weak environmental signals and adapting proactively are better positioned to sustain competitive advantage in turbulent environments. It further recommends future research on the role of digital technologies, artificial intelligence, and real-time analytics in enhancing organizational awareness while also examining whether these technologies may create new forms of organizational blindness.

EFL Teachers’ Perceptions of Ethical AI Use in Teaching, Learning, and Assessment: Insights from an Omani University Context

This study investigates English as a Foreign Language (EFL) teachers’ perceptions of ethical artificial intelligence (AI) use in teaching, learning, and assessment within an Omani higher education context. Despite rapid AI adoption in education, institutional governance frameworks remain critically underdeveloped, particularly in EFL contexts — creating an urgent need for empirical, locally grounded research. Using a convergent mixed-methods design, quantitative data were collected from 52 EFL faculty members through a structured 52-item Likert-scale questionnaire, complemented by focus group discussions with nine purposively selected teachers drawn from the same participant pool. Analysis across five constructs revealed high levels of AI literacy and ethical awareness (M = 4.09), ethical responsibility and academic integrity (M = 4.19), and positive pedagogical engagement (M = 4.11). The most critical finding was a significant institutional policy deficit reflected in the lowest construct mean (M = 3.25), with the majority of participants reporting an absence of clear guidelines, consequences, or detection tools. Future orientation and framework acceptance recorded the highest mean (M = 4.30), with 98.1% of participants endorsing formal adoption of an Ethical AI Responsibility (E.A.R.) framework. Qualitative findings corroborated a persistent awareness–practice gap, student over-reliance on AI, and inadequate institutional scaffolding. The study recommends urgent development of context-specific, human-centered AI governance frameworks that bridge individual ethical awareness and institutional policy action, with particular relevance to Omani and comparable EFL higher education contexts.

Wrist and Hand Ligament Injuries

Ligament injuries of the wrist and hand are common causes of pain, instability, and functional impairment, yet their diagnosis remains challenging. Imaging frequently reveals structural abnormalities, but their clinical significance is not always clear. This thesis therefore focuses not only on detecting abnormalities, but on identifying which findings are truly clinically meaningful and relevant for treatment. Chapter 2 investigates the prevalence of scapholunate interosseous ligament (SLIL) signal abnormalities on wrist MRI. Among 1,021 patients, SLIL signal changes were present in 31% of MRIs. Most patients belonged to the low clinical suspicion group, and prevalence increased with age. More than half had no documented prior wrist trauma. These findings demonstrate that SLIL signal abnormalities are common and should not automatically be interpreted as acute or clinically relevant pathology. Chapter 3 examines the relationship between extrinsic ligament injury and scapholunate diastasis in patients with MRI-confirmed scapholunate ligament injury. Among 101 patients, 40% had scapholunate diastasis greater than 2 mm. Injuries to both the volar and dorsal extrinsic ligaments were independently associated with diastasis. These findings suggest that clinically meaningful scapholunate instability may extend beyond the intrinsic scapholunate ligament and reflect a broader pattern of ligamentous insufficiency. Chapter 4 focuses on thumb ulnar collateral ligament (UCL) avulsion fractures. Among 114 patients, the avulsion fragment was, on average, similar in size to the UCL footprint. However, fragment size was not associated with surgery, whereas metacarpophalangeal joint instability was significantly associated with operative treatment. Thus, although radiographic morphology helps characterize the injury, clinical instability appears more important for treatment decision-making. Chapter 5 places these findings within the broader context of imaging for wrist ligament pathology. No single imaging modality fully resolves the diagnostic challenges. Radiography mainly demonstrates indirect signs, ultrasound is useful for superficial structures but operator dependent, CT provides excellent assessment of osseous anatomy but limited direct ligament visualization, and MRI allows direct visualization but has variable diagnostic performance. Artificial intelligence (AI) may provide additional value by improving standardization, reducing observer variability, supporting quantification, and facilitating more consistent and clinically meaningful interpretation. Chapter 6 further explores AI-based clinical prediction models. Such models may support individualized decision-making by integrating multimodal data and identifying complex patterns that may not be apparent through conventional interpretation alone. However, their clinical value depends on rigorous development, validation, transparent reporting, and demonstration of clinical impact. For wrist and hand ligament injuries, prediction models may ultimately help integrate imaging with factors such as age, trauma history, physical examination, and associated injury patterns. Overall, this thesis demonstrates that detecting a ligament abnormality is only the first step. Age, clinical history, associated injuries, instability, and examination findings determine whether an imaging abnormality is clinically meaningful. Future diagnostic approaches should therefore move beyond detection toward integrated, patient-specific interpretation, with advanced imaging and AI potentially supporting more accurate and treatment-oriented decision-making.

#259 ARISE-NMD: an open-access, annotated needle electromyography database for clinical assessment, research, and artificial intelligence

Artificial Intelligence in Supporting Self-Regulated Reading among English as a Second Language Learners: A Systematic Review

Artificial intelligence (AI) has become increasingly prominent in English language education, offering new possibilities for supporting reading instruction and independent learning among English as a second language (ESL) learner. This study aims to examine the existing body of empirical research concerning the use of AI to improve reading skills and encourage self-regulated learning (SRL). The significance of this study lies in its synthesis of fragmented data to provide a unified framework for future technology-mediated reading instruction. Using a systematic literature review approach that strictly followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, 19 empirical studies were extracted from major academic databases including Scopus, ERIC, and Google Scholar and analyzed through thematic analysis. The synthesized results reveal that AI applications, particularly adaptive learning platforms and conversational agents, effectively support reading development by giving feedback, personal learning experiences, and opportunities for learners to monitor their own progress. Concurrently, the results highlight several concerns, including excessive dependence on technological support and limited development of higher-order reading skills. Overall, AI can make a valuable contribution to reading instruction when integrated with pedagogical practices that encourage learner independence. Future research should investigate the influence of AI on reading comprehension through longitudinal studies conducted in authentic educational settings.