artificial intelligence

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.

Artificial intelligence–driven decision-making after endoscopic resection for early gastric cancer

Artificial intelligence framework for predicting inclined magneto-bioconvective Ellis penta-hybrid nanofluid flow over a stretching cylinder with gyrotactic microorganisms: A Levenberg–Marquardt approach

This study aims to develop data-driven framework for predicting the magneto-bioconvective transport characteristics of an Ellis penta-hybrid nanofluid over a stretching boundary, with particular emphasis on rheological behavior, Brownian diffusion, thermophoresis, and gyrotactic microorganism transport. The Levenberg–Marquardt backpropagation scheme was employed to optimize a two-layer feed-forward neural model comprising sigmoid-activated hidden units and a linear output layer. The resulting BLMS-ANN predictions showed close agreement with the numerical data, with MSE values ranging from 10 −9 to 10 −11 across the training, validation, and testing stages. Physically, increasing the Ellis fluid parameter enhanced the velocity profile while reducing both nanoparticle concentration and motile microorganism distributions. In contrast, increasing the thermophoresis parameter reduced the temperature profile but increased the concentration and microorganism profiles. These findings demonstrate the capability of the proposed ANN framework to reproduce strongly coupled transport behavior with low prediction error and reduced computational effort. The present results are relevant to engineering applications involving thermal management, biomedical transport, energy systems, and advanced heat-transfer devices employing non-Newtonian hybrid nanofluids.

AI-Driven Sustainability in Industry 5.0: The Role of Responsible Leadership

Abstract As Industry 5.0 advances, artificial intelligence (AI) is increasingly positioned as a driver of sustainability in knowledge-based economies; however, empirical outcomes remain uneven and frequently symbolic. Addressing this paradox, this paper examines AI-driven sustainability through the lens of knowledge creation, governance, and application, rather than technological capability alone. Using a Critical Interpretive Synthesis, the paper systematically analyses interdisciplinary literature on AI, Industry 5.0, sustainability, and leadership to move beyond descriptive aggregation toward theory development. The findings reconceptualise AI as a knowledge infrastructure whose sustainability value depends on how AI-generated knowledge is governed, interpreted, and applied across systems. The paper further advances theory by reframing responsible leadership as a knowledge-governance mechanism, explaining how leadership shapes the prioritisation and diffusion of AI-enabled knowledge across micro (individual), meso (organisational/industry), and macro (institutional) levels. Building on these insights, the paper develops an integrative framework that explains why AI-enabled sustainability initiatives often result in performative environmental, social, and governance (ESG) compliance rather than substantive environmental and social impact. By linking responsible leadership with AI knowledge governance, the paper contributes to the knowledge-economy literature by explaining variability in sustainability outcomes beyond technological adoption. The paper concludes by outlining implications for organisational governance and identifying directions for future empirical research to test and extend the proposed framework.

AI-Powered Automated Speaking Scoring and Human Evaluation: A Comparative Study of EFL Learners’ Oral Proficiency

Recent advances in artificial intelligence have led to the development of automated speaking assessment systems capable of evaluating oral proficiency with increasing accuracy. This study compares Artificial Intelligence (AI) powered automated speaking scoring and human evaluation in assessing the oral proficiency of 74 Saudi English as Foreign Language (EFL) learners. Participants completed a picture-description speaking task, which was evaluated by both Claude and two trained human raters using identical holistic and analytic speaking rubrics. The study examined holistic speaking scores and five analytic dimensions: coherence, cohesion, content development, grammar, and vocabulary. Statistical analyses included descriptive statistics, comparative analyses, Intraclass Correlation Coefficients (ICC), Weighted Kappa coefficients, and Bland-Altman analysis. Results showed no significant difference between AI-generated and human-assigned holistic speaking scores. Similarly, cohesion and grammar demonstrated strong similarity between the two assessment approaches. However, significant differences were observed for content development, vocabulary, and coherence, with Claude consistently assigning slightly higher scores than the human raters. Agreement analyses revealed good-to-strong agreement across both holistic and analytic assessments. The findings suggest that AI-powered speaking assessment can produce evaluations broadly comparable to human judgments while demonstrating strong consistency across multiple dimensions of oral proficiency. The study supports the potential of AI-assisted speaking assessment as a reliable complement to human evaluation in EFL contexts.

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.

Deconstructing and Reconstructing University Classroom Authority in the Age of Generative AI: Practical Reflections on a Project Based Learning Reform

The rapid proliferation of generative artificial intelligence is fundamentally reshaping higher education, challenging the traditional lecture-based, knowledge-transmission model of classroom instruction. This paper offers a reflective analysis based on the author's first-hand teaching experience at a Chinese university with a finance and economics focus, where two AI-related courses are offered: a general-education AI literacy course for all undergraduates and an advanced deep learning course for computer science majors. The analysis reveals that AI, as a near-perfect knowledge transmitter, has rapidly devalued the knowledge-delivery function of traditional classrooms. Teachers find themselves caught between the narrowness of their own specialised training and the explosive, fast-moving breadth of AI, while student engagement continues to decline. In response to this crisis, the author's school officially launched a teaching reform in the spring semester of 2026, shifting its core approach from "knowledge-point instruction" to "project-based learning" (PBL). For the general-education course, which enrols a large number of students from social science and humanities backgrounds, the reform emphasises individual creation using off-the-shelf AI tools, aims at developing a perceptual understanding of AI principles, and involves minimal or no coding. For the computer science majors, in contrast, the advanced course adopts more technically intensive, code-based projects. This paper describes in detail the initial implementation and emerging challenges of this differentiated reform, and reflects on the necessity and pathways for transforming the teacher's role from "knowledge authority" to "learning environment designer."

Refusal geometry reflects refusal training: diverse refusal prefixes can raise stable rank and weaken refusal vector ablation attacks

Refusal training protects AI models from jailbreaks by training models to decline unsafe queries, reducing the risk of misuse. Recent work finds that refusal behavior in aligned language models can be mediated by a single activation direction or a low-dimensional refusal subspace shared across harmful prompts: ablating those directions suppresses refusals while largely preserves other model capabilities. Yet it remains unclear why safety-critical features in a wide range of models emerge in a concentrated, low-dimensional structure. In a case study of OLMo-2-0425-1B-Instruct we find that the refusal geometry reflects refusal training: activation updates resulting from refusal-completion first-token losses explain the resulting refusal direction and refusal subspace. We study refusal directions through the training dynamics across refusal datasets and reveal that their brittleness is associated with repetitive refusal starts, which in turn is linked to concentration of gradients and refusal features in a low-dimensional subspace. Across frozen-model analyses and controlled synthetic fine-tuning, we find evidence of a hardening lever: diverse refusal starts can raise stable ranks of gradients and activation changes, making refusals harder to remove with a vector ablation attack.

Ask Twice, Look Twice: Prompt Echoing Resolves the Question-First Paradox in Vision-Language Models

Where should the question go in a vision-language model (VLM) prompt: before the image or after it? Intuition says before: knowing what is asked should tell the model where to look. Yet across visual question answering benchmarks, question-first prompting consistently underperforms the image-first ordering recommended for frontier VLMs, a phenomenon we term the question-first paradox. We trace this paradox to a conflict between two stages of VLM computation. Logit-lens and attention probes show that question-first prompting steers perception, shifting image patch representations toward question-relevant concepts. But downstream, stranded behind hundreds of image tokens, the question is barely attended by the answer token, which instead commits to image-driven, often wrong answers. Causal attention knockout confirms that the answer reads the question only when it follows the image. This diagnosis yields a training-free fix: question echoing, restating the question on both sides of the image so one copy steers perception while the other is available at answer time. A similar division of labor appears in a fifty-year-old finding on human 'adjunct questions', where repeating a question before and after a passage improves comprehension. Echoing the image as well brings further gains by restoring the whole-image view otherwise lost by a causal decoder. The paradox holds across five open VLMs, costing up to 17.5 group-accuracy points. Echoed prompts recover most of the gap and, on NaturalBench and Winoground, surpass the best single-pass ordering by up to 19 group-accuracy points on Winoground, with no training, fine-tuning, or architecture change. The paradox reveals a tension between steering what a model sees and preserving access to what it was asked; echoing resolves this through prompt design. Project Page: https://rakshanda-cmu.github.io/ask-twice-look-twice/