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Abstract Large language models (LLMs) demonstrate strong reasoning and planning capabilities in static textual contexts, yet they struggle significantly with dynamic decision-making tasks involving spatial elements, such...

Purpose – This study aims to determine the effectiveness of NHT Cooperative Learning assisted by Lumi Education in improving the abductive reasoning skills of junior high school students in science lessons on substance...

Anatomical education presents ongoing challenges in helping students master complex content while integrating it with clinical relevance. Neuroanatomy is particularly difficult due to its intricacy and abstract nature. T...

Background: Clinical reasoning in the intensive care unit (ICU), a complex, high-acuity and stressful environment, may require a different set of critical thinking and decision-making approaches to undergraduate physiot...

Modern software evolves rapidly, accumulates architectural debt, and develops cross-module dependencies that complicate reliable maintenance. We present EvoGraphCoder, an evolutionary graph-reasoning framework for self-a...

Abstract Collaborative filtering and neural recommendation models have advanced substantially over the past decade, yet they frequently struggle to capture the fine-grained structure of user preferences and the rich, mul...

Background: The transition from arithmetic to algebra remains a persistent challenge in early mathematics education. While structural awareness is widely recognised as foundational to this shift, the developmental mecha...

Background Large language models (LLMs) have demonstrated promising capabilities in medical diagnostic reasoning, yet their performance in specialized clinical domains such as rheumatology remains incompletely characteri...

Background: Speech-language pathology (SLP) students in South Africa often face persistent challenges in transferring theoretical learning to authentic clinical contexts. This theory–practice divide is compounded by l...

During the golden-time phase of urban disasters, emergency response systems must issue timely directives that remain compliant with jurisdictional policies and regulations, even when early human reports precede reliable ...

Background: Clinical education is central to physiotherapy training, yet many students enter their first placements with limited experience in holistic clinical reasoning. The International Classification of Functioning...

Abstract Background High-fidelity simulation is a cornerstone of emergency medicine (EM) education and is used by a majority of EM clerkships. Traditional high-fidelity simulation involves learners working through a pred...

Abstract Background Deep learning faces a significant bottleneck in medical image analysis due to its reliance on large-scale, expert-annotated datasets. This challenge is acute in ophthalmology, particularly for detecti...

Abstract The importance of fear level detection in affective computing using physiological signals has not been extensively explored. Accurately detecting fear responses facilitates real-time emotional monitoring and imp...

Abstract The global challenge of effectively cultivating clinical thinking in medical students persists, often hindered by traditional textbooks that present knowledge in a linear sequence disconnected from the non-linea...

Abstract Concerns that AI tools may erode diagnostic reasoning contrast with claims that AI can foster higher-order thinking. This longitudinal study followed 372 medical students across 12 months of supervised rotations...

IntroductionWe address moral uncertainty in reinforcement learning (RL) by proposing a framework that integrates multiple ethical theories into decision-making. Existing approaches rely on single moral frameworks or hand...

Abstract Background To evaluate whether performance-based sequencing of clinical placements improves learning outcomes compared with randomized sequencing in undergraduate physiotherapy education. Methods Undergraduate p...

The provision of accurate and explainable captions and answers to visual questions about remote sensing images is essential in areas such as disaster management, land-use supervision, and military analysis, where system ...

Abstract Wearable sensor–based human activity recognition (HAR) is central to smart sport and health-monitoring applications, yet existing deep learning models often rely on ad hoc multimodal fusion and offer limited i...

Novel innovations in large language models (LLMs) have demonstrated their ability to generate and analyze literary texts. As a result of intricate semantic layers, metaphors, polyphony, and nonlinear narrative structures...

Abstracts Alert fatigue remains a major barrier to the effective deployment of predictive models in emergency care, particularly in the context of rare but critical outcomes such as in-hospital mortality (IHM), which oft...

IntroductionThe complex interplay between environmental dynamics, biodiversity loss, and public health necessitates advanced methodologies for quantifying and interpreting their interactions. Respiratory health, highly s...

Abstract Reliable interpretation of clinical imaging requires integrating complementary evidence across modalities, yet most AI systems remain limited by single-modality analysis and poor generalization across institutio...