The complexity of many AI models hinders their clinical adoption because the clinicians using them do not regard them as transparent. This study addresses the lack of clinician-centered explainable AI (XAI) interfaces by designing and evaluating intuitive visual explanations for intubation prediction, testing the hypothesis that workflow-compatible designs enhance acceptance. This study compares three, time-aware, visual explanations for XAI-based intubation prediction and evaluate their acceptance, comprehension, and perceived utility among clinicians. We developed machine learning models to estimate the near-term risk of deterioration in the patient's condition which may lead to mechanical intubation using ICU time-series data. We generated global and local explanations using SHAP and designed three customized visual formats-a temporal force plot, a temporal bar chart, and a dual-encoded SHAP heatmap. Clinicians (n=206) evaluated comprehension and usability using objective questions and a Likert-based survey. Based on 4608 critically ill patients with 10 medical variables over 7hours of data for each patient, the Random Forest (RF) model achieved the highest area under the curve (AUC): 0.94. Furthermore, the local explanations were customized and evaluated by 206 clinicians through a survey conducted on the Prolific platform. A customized heatmap representation was selected as the visualization with the highest perceived clinical utility and alignment with clinical workflows. The reported findings support the need for explanation formats to be tailored to clinical reasoning and task context, supporting the concept of cognitive fit. The heatmap's close alignment with clinicians' mental models and its graphical integrity enhances interpretability and trust. This study demonstrates that explanation effectiveness depends on contextual relevance, rather than a universal standard, and that the presentation format itself significantly shapes clinicians' trust in XAI systems. This study advances clinical XAI by introducing a time-aware explanation framework for ICU intubation decisions. By integrating temporal trends with model reasoning, our visualizations closely align with clinicians' cognitive workflows. Rigorous clinician-centered evaluation identified the dual-encoded SHAP heatmap as the most useful and workflow-compatible visualization, highlighting the importance of explanation design alongside predictive accuracy for clinical adoption.

An ergonomics study on side- and rear-view CMS display locations in two lane-changing scenarios.

Optimizing sepsis care within a learning health system: qualitatively examining the perspectives of those involved in sepsis care.

To characterize clinical reasoning in prioritization and test whether errors are linked to experience or are universal, by examining how information congruence and informativeness influence nurses' prioritization and diagnostic reasoning, and by identifying cognitive mechanisms underlying systematic errors under clinical uncertainty. A concurrent embedded mixed-methods study was conducted with 130 nurses from two university hospitals. Using a think-aloud protocol, participants reasoned through four experimentally controlled clinical scenarios in which information congruence (data aligned vs. misaligned with the most common diagnosis) and informativeness (amount of data) were manipulated. Prioritization accuracy (correct vs. incorrect priority) was the primary outcome. Qualitative analysis identified cognitive biases, which were entered into a logistic regression model to quantify their association with accuracy. Accuracy collapsed when nurses faced incongruent clinical data, falling from 49.3 % in congruent scenarios to 18.4 % in incongruent ones (31-point drop; 95 % CI 20-42 %; p<0.001). This decrement was independent of age, experience, educational level, and ward type. Qualitative analysis showed that most nurses (71.4 %) actively dismissed critical conflicting cues. Confirmation bias (OR=0.048, p=0.015) and information bias (OR=0.082, p=0.010), were strong significant predictors of incorrect prioritization. Nursing prioritization errors are systematic cognitive failures rather than random mistakes or simple knowledge deficits. The core vulnerability appears to be metacognitive: an impaired ability to detect and resolve conflict between an activated mental model and new, incongruent information. Interventions to reduce diagnostic and prioritization errors should explicitly train cognitive and metacognitive skills for managing incongruence and flexibly updating clinical representations.

ABSTRACT ‘Fungus blindness’ is a persistent underappreciation of fungi’s importance and has significant implications for building regenerative futures. This study explores public perceptions of fungi in a predominantly ‘fungi-blind’ society and whether experiential engagement can challenge them. Two formats were examined: (1) a hands-on workshop, encouraging noticing fungi at macro and micro scales, and (2) an immersive audio experience giving voice to afictional fungus, prompting reflection on ecological interconnectedness. Using Personal Meaning Mapping, the mental models of 70 participants were captured pre- and post-intervention, revealing both affective and cognitive dimensions. Analysis focused on the breadth of participant understanding around ‘fungi’. Pre-intervention findings revealed narrow perceptions dominated by ‘Mushroom’, ‘Food’ and ‘Poison’, alongside limited knowledge of fungal diversity, morphology, and classification. Participants expressed minimal awareness of fungi’s ecological and biotechnological significance alongside a fascination with their strangeness. Post-intervention, ecological learning expanded across both cases, with contextual elements (e.g. ‘trees’ and ‘soil’) facilitating emerging understanding of fungi. Modest shifts towards eco-centric perspectives were also observed, demonstrating the value of experiential approaches in encouraging human-fungi and broader nature-connection. This study advocates educational reform, targeted outreach, and proposes a refined definition of fungus blindness with actionable learning aims.

The Liaison Committee on Medical Education (LCME) accreditation process presents significant challenges to medical schools in the current educational landscape. Medical schools invest substantial resources in accreditation preparation, often funding consultants and mock site visits, often diverting resources from institutional priorities. Current self-evaluation resources, while meeting Department of Education minimum requirements, inadequately prepare both medical school staff and site surveyors for the complex evaluation process. The accreditation system lacks transparent evaluation methodologies that would ensure consistent application of evaluations based on institutional contexts. Site surveyors and medical school staff require specialized competencies that are not adequately addressed through existing preparation methods. This has led to the perception that application of standards is inconsistent and results in increased financial burden on medical schools. This commentary proposes implementing rubrics and frame-of-reference training to improve the LCME accreditation process. Drawing from established assessment and program evaluation methodologies that employ transparent standard-setting processes, frame-of-reference training can establish shared mental models for how standards and elements are rated, thereby improving consistency in their application. This approach would provide greater guidance to both medical schools and site surveyors throughout the evaluation process. This adoption could redirect medical school funding from accreditation consultants toward institutional priorities and educational improvements. This systematic approach has the potential to enhance the consistency of accreditation decisions while reducing the financial burden on institutions. Future research should examine outcomes of this proposed framework to evaluate its effectiveness in improving the accreditation process and institutional resource allocation.

Supervision in higher education is a complex and evolving process that necessitates adaptive, evidence-based decision-making to effectively guide postgraduate students. Conventional supervisory models often encounter difficulties in addressing uncertainties and the non-linear dynamics inherent in academic mentorship. This chapter examines the AI-based application of Fuzzy Cognitive Mapping (FCM) as an innovative framework to enhance supervisory practices by integrating expert insights, student progress data, and institutional guidelines within a structured yet flexible system. Employing a mental model approach, the study utilises fuzzy logic principles to simulate supervisory scenarios and assess causal relationships among critical factors, such as student motivation, research complexity, institutional support, and mentor–mentee engagement. The FCM-based framework enables supervisors to visualise interdependencies between variables, predict outcomes, and dynamically adjust mentoring strategies. Mixed methods, combining quantitative and qualitative data, are employed. Findings indicate that FCM enhances supervisory efficiency by promoting proactive interventions, improving communication, and supporting continuous monitoring of mentor–mentee relationships. Furthermore, the model advances a data-driven and transparent approach to supervision, minimising subjectivity while preserving contextual flexibility. By operationalising cognitive and computational intelligence, this chapter illustrates how FCM can bridge gaps between qualitative judgement and quantitative assessment in higher education supervision. The study contributes to emerging scholarship on artificial intelligence applications in academic contexts, underscoring the potential of cognitive modelling in improving student outcomes. It concludes by emphasising the necessity of empirical validation and the integration of adaptive mental models into institutional supervisory frameworks to strengthen postgraduate research management and mentoring effectiveness.

Mental models are internal representations or conceptions that students construct as they make sense of their everyday interactions with the natural world. The layers of the Earth are topics covered in the Chilean national curriculum for primary and middle school education. Studies have shown that primary and middle school students hold many preconceptions about earthquakes, mountain formation, and volcanoes. In this study, we analyzed 785 drawings by students from six Chilean schools at six school levels. We developed and validated a rubric to analyze the students’ drawings of the Earth’s internal layers and identify the location of magma within the Earth. Among the main results, most students were found to draw concentric layers without distinguishing their thickness, number, or name. Fifth-grade and eleven-grade students were the most likely to create drawings that reflected the internal structures, albeit without differentiating the thickness of the layers. Most students placed magma in the center or core of the Earth. The study concludes that a possible learning progression begins with a framework theory in which students recognize aspects of their daily lives within the Earth, then continues with a mixed model in which scientific knowledge of concentric layers within the Earth is combined with the location of magma at the center, and ends with a model similar to that proposed by scientists.

In culturally rich regions such as Papua, Indonesia, formal science education is often disconnected from students' lived experiences, creating a gap between the abstract chemistry curriculum and the local ethnochemical knowledge embedded in ancestral traditions of the Biak ethnic group, particularly Sasisen and Napnap Mor. This study emphasizes the importance of integrating local wisdom to bridge this gap. This study aims to reconstruct the implicit ethnochemical knowledge within these traditions by mapping the community's mental models using Johnstone’s Triangle multi-level framework, thereby connecting local knowledge with the formal chemistry curriculum. A qualitative approach was employed, using triangulation of data from in-depth interviews, participant observation, and document analysis. The data were analyzed based on Johnstone's three levels of chemical representation. The findings reveal culturally developed mental models (emics) among the Biak people that align empirically with modern chemical concepts (etics), especially in the use of natural materials related to the functions of secondary metabolites. This mapping confirms that the Sasisen and Napnap Mor traditions provide rich contextual foundations for chemistry learning. Integrating Biak traditions into the chemistry curriculum enhances the relevance of science education and students’ scientific literacy while also contributing to cultural preservation and the decolonization of science education. This study offers a local wisdom-based pedagogical model that supports sustainable development.

As artificial intelligence (AI) products diversify, it remains unclear whether trust mechanisms generalize across systems or vary by users’ construal of AI. In a video-based vignette study (N = 242), participants evaluated 11 AI systems ranging from disembodied assistants to highly anthropomorphic agents. Exploratory factor analysis and hierarchical cluster analysis identified four perception-based categories: Functional Machines, Social Companions, Anthropomorphic Agents, and Disembodied Assistants. Multilevel models showed that perceived competence and warmth predicted trust across categories. At the same time, anthropomorphism showed mixed effects: familiarity and animacy increased trust, whereas mind attribution and uncanniness decreased trust, with stronger negative effects for embodied agents. We further observed a dissociation between trust and willingness to interact, with willingness to interact more sensitive to visceral discomfort and category ambiguity. These findings indicate that trust in AI is category-dependent and that effective design should align social cues with users’ mental models.

Temporal dynamics of gaze behavior around system activation: The role of trust, mental model, and prior experience in automated driving

Communication, Teamwork, and Culture: Creating Winning Teams to Support Optimal Childbirth Outcomes.

Drivers' mental models of advanced driver assistance systems: A systematic review of conceptualization, associated factors, and intervention strategies

This study examines the development of drivers' general mental models during their first real-world experience with the SAE Level 3 conditionally automated driving system (CADS) Drive Pilot. While previous research has primarily investigated mental model formation in simulators or on test tracks, little is known about how accuracy and completeness evolve during initial use in naturalistic traffic. Twenty-nine participants without prior CADS experience completed a within-subject on-road study with three measurement points: before receiving any information about the CADS (t 1 ), after a short instructional video (t 2 ), and after a real-world drive on a German motorway (t 3 ). Mental models were assessed with a system-specific self-report questionnaire designed to evaluate both accuracy and completeness. Qualitative and statistical analyses showed high initial accuracy for core functions, alongside considerable misconceptions and knowledge gaps regarding limitations and operational aspects. The instructional video improved both accuracy and completeness, including for some limitations not explicitly covered. Real-world driving further increased accuracy across categories. However, completeness declined, particularly for limitations not encountered during the drive. Statistical analyses confirmed significant improvements in accuracy from t 1 to t 2 , t 1 to t 3 and t 2 to t 3 . Findings suggest that short, targeted instructions combined with immediate real-world exposure can effectively enhance the accuracy of drivers' mental models. However, knowledge about seldom-encountered limitations decays rapidly without reinforcement, highlighting the need for specific instruction and in-vehicle systems that sustain awareness of rare but safety-critical constraints over time. • Real-world study on development of drivers' mental model of conditionally ADS. • Short instruction video and first drive significantly enhanced mental model accuracy. • Mental model completeness decreased, especially for rarely encountered system limits. • Indirect transfer improved understanding of features not directly experienced. • Findings highlight need for short trainings and HMI that reinforce rare constraints.

The Challenges of Prognosticating Persistent Critical Illness: A Qualitative Study.

Under the background of the popularization of higher education, poor college students, due to multiple factors such as economic pressure, social comparison and resource limitations, generally have varying degrees of inferiority complex, which seriously affects their academic development and personality integrity. The traditional mental health intervention model is confronted with challenges such as limited coverage, obvious privacy concerns and insufficient personalization. This thesis explores the combination of artificial intelligence technology and library reading therapy to construct a new intervention mechanism for the inferiority complex of poor students. The research first analyzes the special manifestations and causes of inferiority complex among impoverished students, and then systematically expounds how AI technology emempower reading therapy to achieve precise identification, personalized matching and dynamic assessment. On this basis, a systematic intervention model including four major links: intelligent recognition and evaluation, personalized resource matching, immersive healing scenarios, and effect evaluation and feedback is designed, and its practical path is illustrated through cases. Finally, countermeasures and suggestions are put forward for potential challenges such as technological dependence, the digital divide, and privacy protection. Research shows that AI-supported library reading therapy can provide low-cost, easily accessible and highly privacy-protected psychological intervention services for impoverished students, helping them achieve psychological growth from "self-help" to "transcendence", and providing a new model for the innovation of mental health education in colleges and universities.

Communities across the world need resilience to adapt to changing circumstances. In this paper, we blend focus group work and network analysis to assess adaptive capacities and potential resilience, in a peri-urban community in Hobart, Tasmania, Australia. We used focus groups to gather information about core social and environmental features of the community and then constructed group mental models (a diagrammatic representation of how the community works). We asked participants to identify drivers (presses and pulses) of change, and to use the mental models to show how externally imposed changes might impact core features. To complement this qualitative data, network analysis was used to quantitatively identify core features, based on centrality, and to test for redundancies-assessing the likelihood of the system remaining resilient if a core feature were removed. The empirical results highlight the significance of networks' contributions to local resilience and provide new pathways for conceptualizing change in social-ecological systems. Future research based on this case study could be scaled up and applied in many situations to improve our understanding of ways to maintain resilience at multiple geographic scales.

Abstract In professional motorsport, driving simulators play an important role in driver and racing team training, as well as race car development and testing. Their usability relies on cueing systems, with the Motion Cueing Algorithm (MCA) being responsible for translating virtual vehicle motion into simulator motion demands. This study evaluates the effects of MCAs on 31 amateurs and two professional racing drivers in a four-degrees-of-freedom driving simulator. Assessment criteria are based on drivers’ performance metrics (e.g., lap time, probability of fatal errors), objective driving characteristics (e.g., steering wheel reversal rate, full-throttle ratio), subjective workload, and preferences for specific MCAs. The results revealed consistent MCA preferences for both professional racing drivers, which are supported by their superior driving performance. In contrast, preferences among amateur drivers were distributed across the MCAs, and their subjective workload was at least double that of the professionals. The absence of a common MCA preference in the amateur group may be attributed to their high workload, combined with limited experience and inappropriate mental models for race driving. These results are important because they suggest that motorsport driving simulators, even if used for less experienced or non-experienced racing drivers, should rely on the MCAs preferred by professional racing drivers. The findings further underscore the importance of MCA tuning by professionals with an adequate reference to the real world scenario that is to be tested in the virtual environment.

ABSTRACT The purpose of this article is to provide a systemic and transdisciplinary review of Total Interpretive Structural Modelling (TISM), a system‐based methodology used to convert unstructured mental models into hierarchical frameworks for decision‐making, policy design, and systems analysis. Based on a review of 523 articles from the Scopus database, the study identifies methodological advancements, highlights prominent institutions and authors, explores co‐citation clusters, identifies key application areas, examines thematic domains, and analyses integrated methodologies. TISM has undergone several advancements, each enhancing its analytical depth, contextual relevance, and decision‐making utility across diverse domains. The study conducts a co‐citation analysis that reveals three major knowledge clusters: organizational excellence, application areas, and multi‐method studies. TISM has been applied extensively across domains such as healthcare, sustainability, human resource management, technology adoption, and manufacturing. Within these applications, researchers have explored various contextual dimensions including barriers, facilitators, challenges, and enablers that shape system behaviour. TISM has also been integrated with diverse methodologies such as case studies, regression analysis, structural equation modelling, confirmatory factor analysis, system dynamics, causal loop mapping, and interviews, highlighting its flexibility for both qualitative and quantitative inquiry. The study also suggests future research agendas and implications. The suggested implications will be relevant for researchers seeking innovative, systems‐based approaches to address complex societal and organizational challenges.

The purpose of this study was to examine prospective biology teachers’ drawings of the structure of the nephron and the stages of urine formation to determine their conceptual knowledge and to identify misconceptions based on these drawings. This research was conducted using a case study, a qualitative research design. The study group consisted of 32 prospective teachers studying in the biology teaching program at a state university in Turkey. The prospective teachers’ drawings and semi-structured interviews were used as data collection tools. The drawings were evaluated using content analysis using a researcher-developed rubric. In this rubric, correct and complete drawings were scored separately with 2 points, incomplete and incorrect drawings with 1 point, and no drawing with 0 points. These drawings were reported using frequency and percentage distributions. Interview data was analyzed using content analysis. Based on the results obtained from both the drawings and the interviews, majority of prospective teachers knew the basic structures of the nephron, but they made errors in the detailed sections (collecting duct, vascular structures). While filtration in the urine formation process was partially explained correctly, there were serious misconceptions about the reabsorption and, especially, the secretion steps.