Search Results (603)

The integration of human judgment into artificial intelligence (AI) systems has emerged as a key research direction, particularly for high-stakes applications where full automation remains insufficient. Human-in-the-Loop (HITL) AI represents a field that combines machine learning capabilities with human oversight, feedback, and decision-making at various stages of the AI pipeline. This survey provides a systematic review of HITL approaches, covering theoretical foundations, technical methods, ethical considerations, and domain-specific applications. We propose a unified taxonomy that categorizes HITL systems based on loop placement, interaction granularity, and temporal characteristics. This review synthesizes findings from healthcare, autonomous systems, cybersecurity, and other high-risk domains where human oversight is essential. We also examine the challenges of scalability, cognitive load, and trust calibration that affect the practical deployment of HITL systems. The final section outlines open research directions and introduces a framework for designing effective human–AI collaborative systems. Full article

The establishment of AI ethics bylaws in academia is needed for teaching, learning, and assessment. The adaptive parameters of these bylaws define the ethical, pedagogical, and operational standards for the use of artificial intelligence tools within academia. The main aim is to ensure that AI tools are used to enhance educational practices while preserving human judgment, safeguarding academic integrity, and promoting critical thinking. Specifically, these are intended to mentor all domains of academia to uphold the core values of fairness and transparency while adapting to the advent of modern technologies. While many are enthused by the support provided by large language models, it is also important to prevent over-reliance or misuse of AI technologies. This establishes clear responsibility for faculty, students, and administration. These significant bylaws pay more attention to these issues to provide a foundation for good governance, evaluation, and amendment of AI-related practices. To provide normative insight into the anticipated reception of these bylaws, we conducted a small exploratory pilot study with STEM faculty. The resulting observations offer preliminary indications of the feasibility of the proposed method for future research and policy development. Full article

Blind image quality assessment (BIQA) without reference images remains significantly challenging due to the fact that perceptual quality is largely determined by subtle, spatially localized distortions. However, existing Contrastive Language–Image Pre-training (CLIP)-based methods exhibit limited sensitivity to fine-grained degradations such as local blur, noise, compression artifacts, and exposure inconsistencies, since they are optimized for global semantic alignment. To overcome these limitations, we propose a fine-grained vision–language framework that enhances distortion-aware representation by considering both fine-grained visual and detailed textual domains. Specially, our method employs a fine-grained CLIP architecture in conjunction with explicit textual descriptions to enable the effective identification of subtle regional degradations. Furthermore, a parameter-efficient prompt-tuning strategy is utilized to facilitate the learning of task-adaptive prompt representations tailored to image quality assessment (IQA). Extensive experiments on three widely used in-the-wild IQA benchmarks show that the proposed method achieves strong consistency with human subjective judgments: our training-free FGCLIP-IQA reaches a maximum SROCC of 0.732 on KonIQ-10k, outperforming the vanilla CLIP-IQA baseline, while the prompt-tuned FGCLIP-IQA+ further achieves a maximum SROCC of 0.909 on KonIQ-10k with only a small number of learnable parameters and exhibits robust cross-dataset generalization capabilities. These results demonstrate that the fine-grained vision–language alignment shows great potential for future development, and provides an efficient and accurate solution for the BIQA task. Full article

Our paper presents an intuitive model of bystander intervention to workplace mistreatment. Drawing on the literature on moral intuition, our paper proposes (1) that bystanders match an observed conduct to mistreatment descriptions (the first type of mistreatment prototypes), and (2) that bystanders make intuitive judgments and take immediate interventions when intervention prescriptions (the second type of mistreatment prototypes) exist in their long-term memory. Our paper also argues that bystanders’ intuitive judgments and interventions depend on the accessibility of their mistreatment prototypes, which are formed through learning mechanisms. Our paper contributes to the literature on bystander responses to workplace mistreatment. Full article

Background/Objectives: Emergency room severity classification (KTAS) is essential for patient safety but has limitations due to its reliance on subjective judgment. Existing machine learning models have not been trusted in clinical settings due to their opaque ‘black box’ nature in decision-making processes. To overcome this, this study aims to develop an explainable machine learning framework that provides a transparent basis for judgment with high accuracy. Method: We retrospectively analyzed 133,198 emergency room visits from 2022 to 2024. We trained Random Forest (RF) and XGBoost models using vital signs and pain scores and applied explainable AI (XAI) techniques to ensure model transparency. Results: Although XGBoost showed the highest predictive performance (94.7% accuracy within a ±1 error margin), we ultimately selected the RF model, which provides a good balance of predictive power (91.6%) and interpretability for clinical use. The results of the XAI analysis confirmed that pain score, age, and systolic blood pressure were the key variables in prediction, strongly aligning with clinical logic. Conclusions: This study demonstrates that explainable AI can provide transparent insights for KTAS prediction beyond the limitations of traditional black-box models. These models may support emergency department triage by improving consistency and assisting clinicians in identifying potentially high-risk patients. However, further external validation is required before routine clinical implementation. Full article

Theological engagement with artificial intelligence has largely focused on applied ethics, addressing bias, governance, and labor displacement. While indispensable, this framing often presumes that algorithmic systems operate as external instruments acting upon already constituted subjects. This article argues that contemporary predictive architectures intervene at a deeper anthropological level by structuring attention, expectation, and habituation prior to deliberative judgment. It introduces the concept of ordo extractionis to designate a technologically mediated regime of formation characterized by behavioral trace extraction, probabilistic modeling, and recursive projection of statistically inferred continuity. Drawing on Augustine’s account of ordered love and temporality and Aquinas’s doctrine of habitus and the invisible mission of the Spirit, the article distinguishes algorithmic projection from sanctification as divergent pedagogies of temporal formation. Predictive systems stabilize continuity by extrapolating from measurable past behavior; sanctification reorders desire teleologically toward a final end not deducible from prior pattern and grounded in non-competitive divine causality. Algorithmic mediation is therefore interpreted pedagogically rather than metaphysically: it does not rival divine agency but participates creaturely in shaping the ecology within which habituation unfolds. Engagement with contemporary AI research on recommender systems, reinforcement learning, and generative models situates the argument within technological realism and resists determinism. The digital twin is analyzed as a probabilistic representation that acquires institutional authority when operationalized in ranking, profiling, and evaluative systems, without constituting a metaphysical competitor to the imago Dei. In response to anticipatory closure, Eucharistic anamnesis and epiclesis are developed as practices that re-situate memory and expectation within eschatological promise. The article concludes that the central theological question posed by AI is not whether machines can think, but how formative sovereignty over desire is exercised within technologically mediated modernity. Full article

The rapid adoption of generative artificial intelligence tools such as ChatGPT in educational settings has generated both enthusiasm and concern regarding their influence on student learning. While several studies report improvements in efficiency, confidence, and perceived understanding, evidence for durable conceptual learning and knowledge transfer remains mixed. This article examines these tensions through the concept of fluency illusion, a cognitive phenomenon in which information that is easy to process is mistakenly judged as being well understood. Using a narrative conceptual review approach, this study synthesizes findings from 41 publications identified through searches of Google Scholar, Scopus, Web of Science, and ERIC covering the period from 2022 to early 2026. The reviewed literature includes 28 empirical studies, nine conceptual or theoretical analyses, and four review articles addressing the use of ChatGPT in educational contexts. Across domains such as writing and language learning, STEM problem solving, feedback and tutoring, and assessment, the literature shows a recurring pattern in which fluent AI-generated responses increase learners’ confidence without consistently improving deeper conceptual understanding. Drawing on research from cognitive psychology and metacognition, this review proposes an integrative conceptual account of how fluent AI output may shape learners’ judgments of understanding and influence their engagement with learning tasks. The paper concludes by discussing implications for instructional design, assessment practices, and metacognitive scaffolding, and outlines directions for future research aimed at empirically examining the proposed framework and identifying strategies to reduce fluency-driven misjudgments while preserving the potential benefits of generative AI in education. Full article

The intellectual exchanges between late-Ming Jesuits and Chinese literati have long been interpreted primarily as a process of cultural accommodation aimed at “harmonizing with Confucianism” (合儒), and scholarship has tended to focus on missionary strategies, social networks, or individual conversion histories. By contrast, the question of how resources within Confucian thought made ethical dialogue with Catholicism possible—especially why the practical-learning strand (實學派) of Wang Yangming’s School (陽明學) exhibited such pronounced receptivity to Catholic ideas among late-Ming literati—remains insufficiently theorized at the level of conceptual structure. This study, therefore, shifts the analytical focus from “historical narratives of converts” to an explanation of the mechanisms that enabled Sino-Jesuit dialogue. It argues that Augustine and Wang Yangming display a notable convergence in their conceptions of good and evil (善惡論), and that this convergence created an intellectual space for engagement between Jesuits and later Yangming scholars. The Jesuits’ deliberate promotion of doctrines concerning the punishment of evil (懲惡) further facilitated the practical-learning Yangmingists’ reception of Catholic resources regarding ultimate judgment and retributive justice, especially as they confronted the problem of inadequate means to restrain or punish wrongdoing. This article situates late-Ming Sino-Western intellectual exchange within an analytical framework of “theories of good and evil—mechanisms for punishing evil—pathways for supplementing Confucianism (補儒),” thereby offering a mechanism-based explanation, grounded in theories of good and evil, for the historical interaction between Chinese Confucian thought and the ethical systems of incoming religions. Full article

Engineering education often emphasizes technical competencies while underemphasizing and devaluing the social, ethical, and political contexts of engineering systems. This gap is particularly pronounced in middle-year courses, where students develop technical fluency but rarely confront the sociotechnical complexity of real-world problems. We propose sociotechnical judgment as a framework to help students see the intimately intertwining nature of technical knowledge and social, ethical, and contextual reasoning, using energy systems—particularly offshore wind—as an illustrative domain. We designed three course-integrated case studies in thermodynamics, circuits, and statics/dynamics to embed sociotechnical judgment in middle-year engineering courses. These cases include pedagogical strategies, such as project-based learning, problem-based learning, and role-play exercises connecting technical analysis with social, environmental, and policy considerations. The design of these case studies is rooted in real-world problems surrounding U.S. offshore wind, engineering science learning outcomes, and ABET student outcomes. In these pedagogies, we have created opportunities for students to analyze technical systems while engaging with social, ecological, and political factors. Offshore wind projects, including turbine siting, transmission system design, and efficiency trade-offs, provide opportunities to operationalize sociotechnical reasoning in authentic, regionally relevant contexts. Sociotechnical judgment provides a practical framework for bridging technical competency and contextual reasoning in engineering education. Integrating sociotechnical cases into core courses will prepare students to navigate complex, real-world systems through engagement with ethical, social, and environmental considerations inherent in engineering practice. Full article

Tourism destinations exposed to chronic natural hazards require robust analytical frameworks to understand and prioritize the factors that sustain post-disaster resilience. This study examines Baños de Agua Santa (Ecuador), a volcano-exposed destination whose long recovery trajectory illustrates the complexity of socio-ecological adaptation. Using a multidimensional FAHP model grounded in expert judgments, eight dimensions and fifty-six criteria were evaluated through fuzzy triangular numbers and the extended analysis method of Chang to capture uncertainty and ambiguity in decision-making. Results show a consistent and hierarchical structure of resilience, with experiential, economic-entrepreneurial, and socio-community dimensions emerging as the most influential drivers of post-disaster adaptability. Fifteen criteria—primarily perceptual, community-based, and endogenous—achieved “very high impact” status, including risk perception, basic education, individual resilience capacities, institutional coordination, and entrepreneurial environment. Conversely, limited healthcare infrastructure, low economic diversification, and national-level vulnerabilities were identified as critical weaknesses. The study concludes that post-disaster recovery in Baños is shaped by a bottom-up dynamic that emphasizes agency, learning and socio-ecological memory. It also proposes an evidence-based Action Matrix for adaptive governance to guide prioritized, time-phased interventions. The FAHP model proves effective for transparent, context-sensitive prioritization in highly uncertain tourism environments. Full article

The claims resolution process is a cornerstone of the insurance industry, aiming to fairly and accurately determine the economic losses caused by adverse events. Traditionally, adjusters have relied heavily on expert judgment to perform this task. While this approach is essential, it often suffers from subjectivity, inconsistent criteria, and difficulty integrating complex data sources into objective analyses. In this context, Smart Industry tools—such as Artificial Intelligence (AI), Machine Learning (ML), Computer Vision (CV), and the Internet of Things (IoT)—have demonstrated high potential to automate damage detection and assessment; however, their effective integration into loss determination remains uneven across different productive sectors. This study addresses this problem through two objectives. First, we conducted a systematic literature review following PRISMA guidelines to identify which Smart Industry tools are currently used in the insurance sector for loss determination and to analyze their level of maturity in different productive sectors. We searched the Web of Science and Scopus databases, identifying 253 studies, of which 23 met our inclusion criteria. Second, based on the gaps we identified between the construction sector and more advanced industries such as automotive, we propose a methodological framework based on Building Information Modeling (BIM). Our results show that most solutions focus on the detection and technical classification of damage, especially in the automotive sector, while construction lacks methods to convert these technical findings into operational economic estimates. The proposed framework addresses this gap by standardizing technical and economic data from the underwriting stage, enabling more automated, traceable, and objective loss determination for infrastructure claims. Full article

With the widespread application of automatic fire alarm systems in various types of buildings, the problem of fire false alarms has gradually become prominent, which not only causes resource waste, but also may reduce users’ trust in the alarm system, thereby affecting the efficiency of emergency response in actual fires. According to data from a certain fire cloud platform, 99.85% of the suspected fires predicted by its system are false alarms. Although existing models can recognize most fire accidents, the accuracy of fire alarm recognition is only 0.15%, due to loose judgment logic, which still requires a large amount of manpower to verify alarms. This article analyzes a large amount of false alarm data and explores the main causes of false alarms, including environmental interference, equipment failure, and improper human operation. By using a fire dynamics simulator (FDS) to establish fire simulation models under different data settings, horizontal and vertical multi-scene fire simulation data are obtained. The study combines simulation and platform data to form a fire and false alarm dataset using a one-dimensional convolutional neural network (1D-CNN) and deep neural network (DNN) deep learning techniques to learn the deductive rules of the fire scene, establish a two-stage judgment model, and gradually, accurately, judge the results. By quantifying the precision, recall, and F1 score of the model, a deep learning model designed to accurately identify genuine fire alarms while filtering out false ones is proposed that can significantly reduce the false alarm rate. The results indicate that the model can identify 1705 false alarms out of 2255 highly suspected true alarms identified by existing systems in multiple practical scenarios and eliminate 75.61% of false positive alarms. On the premise of ensuring an authenticity recognition rate greater than 98%, the accuracy of fire alarm recognition increased from 0.15% to 28.85%, which will significantly reduce the workload of staff verifying alerts, and has good practical value. Full article

JOLs are widely used to measure metacognitive monitoring, yet their elicitation can reactively enhance memory—a phenomenon known as the positive reactivity effect. The enhanced engagement theory posits that JOLs improve memory by increasing attentional and cognitive engagement during encoding, but direct experimental evidence remains scarce. Across three experiments, we directly manipulated key components of learning engagement—attentional focus (via silent vs. aloud production), cognitive effort (via massed vs. spaced repetition), and motivational involvement (via standard vs. time-saving instructions)—while assessing their impact on the JOL reactivity effect in word recognition memory. Results consistently demonstrated robust positive reactivity effects, critically, the magnitude of these effects was significantly attenuated under high-engagement conditions (aloud reading, spaced learning, and heightened motivation). These converging findings provide the first direct, multi-method experimental support for the enhanced engagement theory, specifying that making JOLs benefit memory most when baseline engagement is low. The results delineate boundary conditions under which making JOLs yield beneficial effects and provide practical insights into leveraging JOLs to regulate engagement in real-world learning environments. Full article

Background: Professional identity and ethical integrity are foundational to nursing practice and are shaped in part by educational experiences. This study evaluated an online Holocaust and genocide educational seminar delivered to fourth-year Bachelor of Science in Nursing (BSN) students and explored how students linked seminar content to professional identity formation, ethical vigilance, and patient advocacy. Methods: We conducted a descriptive mixed-methods educational evaluation. Students completed an anonymous pre-seminar survey (demographics, motivations for studying nursing, self-identified desirable professional qualities, and self-rated knowledge of the Holocaust and other genocides) and an anonymous post-seminar feedback survey with four open-ended questions. Quantitative items were summarized descriptively; qualitative data were analyzed using inductive qualitative content analysis. Results: Of the 205 students who attended the seminar, 133 completed the pre-seminar survey, and 110 completed the post-seminar survey. Students reported high baseline knowledge of the Holocaust but limited knowledge of the Armenian and Rwandan genocides. The five themes that emerged are as follows: (1) ethical judgment and the influence of nurses; (2) patient advocacy and social justice; (3) the effect of historical and contemporary trauma on students’ learning experience; (4) genocide awareness and prevention; and (5) approaches to education and content presentation. Conclusions: Carefully facilitated Holocaust and genocide education, delivered through interactive online pedagogy and structured debriefing, may support late-stage nursing students’ reflection on ethical integrity and professional identity during the transition to professional practice. Full article

Comprehensive soil surveys necessitate the integration of multidimensional pedological information, ranging from the morphological delineation of horizons and the taxonomic identification of soil groups to the quantitative assessment of soil organic matter (SOM). These attributes collectively constitute the basis for interpreting pedogenesis and guiding sustainable soil management. However, conventional methods are limited by the subjectivity of expert judgment for horizon and soil group identification, and the time-consuming nature of laboratory analyses for SOM quantification. We developed a novel multimodal deep learning framework based on an improved Mask R-CNN architecture that integrates soil profile images with auxiliary soil property data to concurrently delineate soil horizons, classify soil groups, and quantify SOM. The model was trained on high-resolution soil profile images from 451 soil survey sampling sites spanning ten soil groups across Anhui Province, China. Data augmentation and transfer learning with pre-training on large general image datasets were employed to address the dataset size limitations and improve model generalization. In addition to accurately delineating master horizons, we evaluated three schemes for classifying transitional horizons, which are often ambiguously determined by expert assessments: (i) assigning the transitional horizon to one adjacent master horizon; (ii) assigning it to both neighboring master horizons as an overlapping section; and (iii) treating the transitional horizon as an independent layer. Scheme (iii) achieved the best overall performance, e.g., horizon delineation with accuracy = 0.925, recall = 0.933, F1-score = 0.929, and segmentation mean average precision (seg-mAP) = 0.918, soil group classification accuracy = 0.717 and prediction of SOM with R² = 0.565. These results demonstrate that treating transitional horizons as independent layers yields superior segmentation. Consequently, this integrated framework provides a robust, automated solution for high-throughput soil resource assessment. Full article