OpenAI, Google, and Microsoft have recently developed popular large language models (LLMs) with incredible clinical applications. LLMs specific to neurosurgery, such as AtlasGPT, have also been recently released. However, the comparative neurosurgical diagnostic capabilities of these models are not well studied. The aim of this study was to evaluate and compare the ability of LLMs to diagnose neurosurgical pathologies. Clinical vignettes (n = 148) extracted from a common neurosurgery case-based review textbook were stratified by subspecialty. OpenAI's ChatGPT-3.5 and ChatGPT-4, Google's Gemini, Microsoft Copilot, and AtlasGPT were prompted to provide a diagnosis: "Provide a neurosurgical diagnosis given the following history…[vignette]." Imaging was inputted for capable LLMs, and all queries were run in May 2024. Diagnoses were compared with the textbook for accuracy and errors were categorized appropriately. ChatGPT-4 was the most accurate model (74% correct), followed by AtlasGPT (63% correct), ChatGPT-3.5 (53% correct), Microsoft Copilot (48% correct), and Gemini (36% correct). Chi-square comparisons demonstrated that ChatGPT-4 was more accurate in providing clinical diagnoses than its counterparts (p = 0.005). Across all vignettes and LLMs, most errors were due to an inability to attribute a key piece of information (generally imaging data) to the diagnostic process while otherwise using logical stepwise reasoning. ChatGPT-4 offered the most accurate diagnoses when given established clinical vignettes. Adding imaging processing capabilities and relevant data significantly increased the accuracy of LLM diagnoses. LLMs can offer accurate assessments of common neurosurgical conditions but necessitate detailed prompting from clinicians. Artificial intelligence has incredible clinical potential; however, practitioners must be cautious and think critically while using them for diagnostic purposes.

Driven by the intensifying mission to foster virtue through education within the Chinese higher education landscape, the strategic integration of value-based guidance into academic curricula has emerged as the primary vehicle for achieving a comprehensive and holistic instructional environment. Chemistry, as a discipline, is uniquely defined by its combination of rigorous scientific theory and extensive practical utility. Consequently, embedding ideological elements into chemistry courses is not merely a critical step in enhancing the caliber of professional training; it is also instrumental in nurturing students' logical reasoning, ethical standards, and civic accountability. By synthesizing domestic and international scholarly progress from the last five years, this study dissects the multi-layered theoretical framework that supports value-integrated chemistry education. It provides a detailed analysis of six essential pillars—including scientific integrity, patriotism, and cultural pride—and distills an operational model characterized by "six dimensions and four pillars" derived from current pedagogical practices. Furthermore, the paper addresses existing bottlenecks, such as undeveloped assessment tools and fragmented resource utilization, by proposing targeted optimization strategies. This research aims to offer a fresh theoretical benchmark for innovating and standardizing the practical implementation of ideological-political education in chemistry, ultimately laying a solid groundwork for preparing a new generation of chemical experts who possess both professional excellence and unwavering social convictions.

This study aims to analyze and describe students' intuitive thinking abilities in solving mathematical problems on the topic of integers. This research employs a qualitative descriptive method. One research subject (S1) will be selected using purposive sampling based on their ability to demonstrate initial indications of intuitive thinking, such as the accuracy and speed of their initial response to a problem. The research instrument consists of one integer problem-solving test question designed to assess intuitive thinking abilities. Data is collected through triangulation using the think-aloud technique during the problem-solving process, followed by a semi-structured interview to explore the subject's (S1) reasoning and intuitive thought processes. Data is analyzed qualitatively through the stages of data reduction, data presentation, and conclusion drawing. The results reveal that intuition, specifically the common-sense type, acts as a cognitive bridge that accelerates the emergence of ideas and the formulation of problem-solving strategies. This intuitive thinking characteristic is demonstrated through the application of systematic strategies, logical reasoning, and a strong reliance on prior learning experiences. These findings indicate that learning experiences can serve as a crucial foundation in forming effective mathematical intuition. Therefore, mathematics instruction should be designed to enrich student experiences through a variety of problem-solving tasks to develop students' intuitive thinking abilitie

Green-synthesized nanomaterials have emerged as promising candidates for environmentally sustainable remediation; however, experimental evidence describing their synthesis routes, physicochemical properties, remediation performance, and sustainability-relevant attributes remains fragmented, inconsistently reported, and difficult to integrate across studies. This work addresses this challenge by proposing an ontology-based semantic framework for the interoperable integration of green-synthesized nanomaterials, contaminants, and remediation processes, incorporating explicit provenance metadata and structured sustainability descriptors. The ontology was developed using the Linked Open Terms (LOT) methodology and implemented in OWL 2 DL, with selective alignment to established vocabularies including eNanoMapper, ChEBI, ENVO, and PROV-O. Adsorption and photocatalysis were instantiated as representative remediation mechanisms to evaluate the framework’s capacity to accommodate structurally distinct processes. Logical reasoning and SHACL-based validation were applied to assess semantic consistency, provenance traceability, and data completeness. The results demonstrate that the proposed ontology effectively integrates heterogeneous experimental data within a unified, FAIR-compliant semantic framework, supports conservative and provenance-aware inference, and enables comparative analysis across mechanistically diverse remediation systems without structural modification. This ontology-based approach provides a robust foundation for sustainability-aware knowledge integration in environmental nanotechnology and establishes the basis for future extensions involving data quality assessment and explainable AI-driven analysis.

Necessary and sufficient knowledge enhanced collaborative logical reasoning in LLMs.

Large language models (LLMs) have gained prominence in medical applications, yet their performance in specialized clinical tasks remains underexplored. Prostate cancer, a complex malignancy requiring guideline-based management, presents a rigorous testbed for evaluating artificial intelligence (AI)-assisted decision-making. This study compared the clinical accuracy, reasoning ability, and language quality of DeepSeek-R1 and ChatGPT variants in addressing prostate cancer diagnosis and treatment. A dataset of 98 prostate cancer multiple-choice questions from MedQA, MedMCQA, and China's National Medical Licensing Examination was constructed, alongside three real-world clinical cases. Responses were generated by five LLMs (DeepSeek-V3, DeepSeek-R1, ChatGPT-4o, -o3, -o4-mini) and evaluated for accuracy across three repeated runs. For case-based simulations, only R1 and o3 were compared with practicing urologists. A Clinical Decision Quality Assessment Scale (CDQAS) assessed outputs across four domains: readability, medical knowledge accuracy, diagnostic test appropriateness, and logical coherence. Blinded scoring was performed by senior urologic oncologists. Statistical analyses used one-way ANOVA with GraphPad Prism v10.1.2, Boston, Massachusetts, USA. DeepSeek-R1 achieved the highest accuracy (96.60 %) on multiple-choice tasks, significantly outperforming the other models (p < 0.05 to <0.0001). In simulated case evaluations, both R1 and o3 performed comparably with physicians in overall readability and diagnostic appropriateness. Whereas R1 demonstrated superior guideline compliance and evidence-based reasoning, o3 showed advantages in workflow clarity, sequencing, and response fluency. However, o3 generated fewer explicit errors than R1. Human clinicians maintained strengths in terminology precision and logical reasoning. DeepSeek-R1 and ChatGPT-o3 exhibit complementary strengths in prostate cancer clinical decision-making, with R1 favoring factual accuracy and o3 excelling in expressive clarity. Although both models approach human-level performance in structured evaluations, human oversight and continued domain-specific optimization remain essential for their safe and effective integration into clinical workflows.

Backward induction (BI) is only defined for perfect information games, but its logic is also invoked in many concepts for imperfect or incomplete information games. Yet, the meaning of BI reasoning is not clear in these settings, and we lack a way to capture the essence of BI without assuming equilibrium. We introduce backward rationalizability, a nonequilibrium solution concept for incomplete information games, which we argue distills the logic of BI reasoning. We show several of its properties and discuss a few applications, including a new version of Lipnowski and Sadler’s (2019) peer-confirming equilibrium. (JEL C72, C73, D83)

Objective: The research aimed to develop a scale for students' logical reasoning in football, prepare educational exercises for the idea filtering strategy and its application in physical education football lessons on outdoor fields, and identify its impact on logical reasoning and performance in the skills of forward shooting from dribbling and from side passes in football. Research methodology: The experimental method was adopted with a two-group design (experimental and control) with (58) students randomly selected and divided into the two groups, representing (65.909%). The researchers used (120) students to develop the scale under study, employing systematic steps and various sequential statistical analyses. They also prepared the forward shooting test from dribbling and the shooting test from side passes. Educational exercises for the idea filtering strategy were developed and applied in the research experiment, the results of which were systematically analyzed. Results: The results showed that the students in the experimental group outperformed their peers in the control group in all three dependent variables. The differences between the experimental and control groups in the reasoning tests were significant. The logical reasoning in football, scoring from a dribble, and scoring from a side pass (0.000) are statistically significant and positive, confirming the effectiveness of the idea filtering strategy on students. Conclusions: The most important conclusion is that implementing educational exercises using the idea filtering strategy helps improve the level of logical reasoning and improves the performance of students who study using this strategy in terms of scoring from a dribble and scoring from a side pass in football, making them outperform their peers who study without it.

Objective: This study examines how digital literacy and Indonesian language proficiency shape university students’ patterns of social media use. Digital literacy is conceptualized not merely as technical ability, but as a reflective and ethical capacity to access, evaluate, and produce information responsibly. Indonesian language proficiency is similarly positioned as a key indicator of students’ logical reasoning, clarity of expression, and politeness in digital communication. Using a mixed-methods approach with 75 Communication Science students, this study collected quantitative data through Likert-scale questionnaires measuring digital literacy, language proficiency, and ethical awareness, while qualitative insights were obtained from open-ended responses describing students’ verification practices, communicative strategies, and perceptions of responsible online behavior. The results show that 84% of students routinely verify information sources before sharing, 78% maintain polite and audience-appropriate language when interacting online, and 92% express pride in using proper Indonesian in digital spaces. These findings indicate that higher digital literacy is associated with stronger discernment, self-regulation, and ethical awareness in social media use, while greater Indonesian language proficiency supports clarity, civility, and context-sensitive communication. The integration of these competencies fosters responsible and reflective participation in online environments. The novelty of this study lies in demonstrating how language proficiency complements digital literacy in fostering responsible digital behavior and strengthening students' communicative ethics. Importantly, these insights have practical implications for curriculum development and character education in higher education, by promoting an integrated approach that combines digital literacy and language ethics to prepare students for responsible digital citizenship.

This classroom action research investigated the effectiveness of Problem-Based Learning (PBL) in enhancing critical thinking skills within English speaking activities at Global Madani Senior High School. Conducted over two iterative cycles with 21 eleventh-grade students, the study employed a mixed-methods approach within a Classroom Action Research (CAR) framework. Quantitative data from pre- and post-tests, analyzed via a paired sample t-test, revealed a statistically significant improvement in students' critical speaking abilities, with the average score rising from 50.71 to 81.90 (p .001). Qualitative data from observations, recordings, and student journals illustrated a clear developmental trajectory: initial engagement in Cycle 1 exposed deficits in argument structure and logical reasoning, prompting the introduction of targeted scaffolds—including explicit argumentation frameworks and peer feedback protocols—in Cycle 2. The findings demonstrate that PBL’s efficacy is significantly amplified when integrated with responsive, reflective CAR cycles and structured linguistic-cognitive supports. The study concludes that embedding PBL within an adaptive CAR process fosters a synergistic environment where authentic problem-solving motivates communication, and deliberate scaffolding transforms engagement into disciplined, critical spoken discourse, offering a replicable model for enhancing higher-order thinking in EFL contexts.

This study investigated the classification and functions of Acehnese riddles used among Acehnese community in Bireuen District, Aceh, Indonesia. The interviews were held with 8 people from 2 two different villages in Bireuen district, Aceh province, Indonesia. Data were collected from participants as Indigenous Acehnese and Acehnese language is their mother tongue. The youngest participant is 33 years old and the oldest one is 85 years old. All participants are resided in Bireuen district. Since they did not travel much (except for occasional holidays with families and Hajj pilgrimage), they are deemed untainted native speakers of Acehnese. For analysis, grounded by the Conceptual Riddle Theory, this study found that the classification of Acehnese riddles and the functions of the riddles among Acehnese community use animals’ concept, living beings, human beings, inanimate entity, and plants. The functions of riddles are for entertaining, educational purpose, and logical reasoning. Most of the Acehnese riddles post the questions use living beings but undetected whether the living beings are human being or other kinds of living entities; animals or plants for example: ta ikat pijut, tapeuleuh tumbon (Tiep up thin, Released fat). The riddle is referred to mosquito net however, the entity is unpredictable until the answer is revealed.

Mathematics is a fundamental subject that supports logical reasoning, problem-solving, and it provides analytical skills which is essential for all students in academic and professional development. The main objectives of this study were to identify the perceptions of students about the impact of instructional methods on students’ motivation and mathematics achievement in Nepal and to determine the relationship between instructional methods, students’ motivation and mathematics achievement. Expected value theory and Self-determination theories were taken as the theoretical base. In this study, only a quantitative approach was used, and 196 students were used as a sample, which was selected using a stratified random sampling method. Likert 5-point scale questionnaire was used to examine the perceptions of students in different headings, and the validity of items was tested by the expert judgement method, and the reliability was tested using Cronbach’s alpha value. The findings show that the students had positive perceptions about all three variables: instructional methods, motivation in mathematics, and mathematics achievement. Also, there was a strong positive correlation between the variables which shows that effective and engaging teaching methods increase a higher level of motivation of students in learning mathematics, and to achieve good achievement in mathematics. Therefore, the teacher must prioritize different teaching methods which helps students in psychological engagement and academic success in mathematics.

This study aims to describe the teacher's role in implementing a deep learning approach to enhance students' critical thinking skills in Grade V mathematics at SDN Made I Surabaya. Employing a qualitative descriptive method, the study involved one teacher and 28 students as subjects. Data were gathered through observation, interviews, and documentation, then analyzed via data reduction, data display, and conclusion drawing. The findings reveal that the teacher functions as a facilitator, mentor, mediator, and motivator within the deep learning framework by presenting contextual problems, posing open-ended questions, and fostering discussion and reflection. This approach effectively develops critical thinking, specifically in problem identification, information analysis, logical reasoning, and drawing conclusions. Challenges such as time constraints and diverse student abilities are addressed through the use of varied instructional media and scaffolded guidance.

Academic writing is a challenging process that requires a coherent expression of ideas, logical reasoning, and data-driven arguments. The implementation of Artificial Intelligence (AI) into academic writing has become important in recent years. The research paper examines the attitudes of English as a foreign language (EFL) learners toward AI language processing tools for academic writing. The authors aim to address the following question: What do Ukrainian EFL learners think about using AI to enhance their academic writing skills? This study aims to bridge the gap between scientific findings on Ukrainian EFL learners’ preferences and their actual use of AI language processing tools in academic writing tasks. To collect research data, online tools (Google Forms) were used and processed via a quantitative method. The second-year students training to become interpreters in the MA program (Philology, Germanic languages, literature (including translation), first foreign language – English) were the focus of the research. The findings suggest that Ukrainian EFL students have positive perceptions of using AI writing tools. The learners also report using AI language processing tools to provide ideas for their writing and to facilitate the composition of essays and paragraphs. The study is significant in promoting the broader use of AI tools for teaching English for Academic Purposes, as well as in identifying and addressing effective teaching techniques and methods.

Abstract The integration of digital literacy into educational systems has become a crucial factor in developing computational thinking (CT) skills among students. The review examines a range of methodological frameworks, national policies, and digital tools contributing to CT development. Findings reveal a strong interdependence between digital literacy and computational thinking: students with higher digital competence exhibit up to 30 % better performance in problem-solving, logical reasoning, and algorithmic tasks. Nevertheless, significant challenges remain, including digital inequality, insufficient access to infrastructure, lack of teacher preparedness, and curriculum fragmentation. The paper explores successful international initiatives – such as the Bebras Challenge, CodeWeek, CS Unplugged, and ISTE standards – as scalable models of integration. It also outlines effective strategies such as gamification, project-based learning, and adaptive platforms. Policy recommendations include the adoption of national digital education strategies and professional development for educators. The study concludes that only a systemic, inclusive approach to digital literacy can ensure equitable access to computational thinking education worldwide.

This study examines the impact of perceived social support on the academic interest in mathematics among vocational high school students, as academic interest is a crucial determinant of engagement and success in learning mathematics. For students in Computer and Network Engineering (TKJ), mathematics is crucial for logical reasoning and technological problem-solving; nonetheless, many exhibit just modest motivation for the subject. Although there is increasing evidence that social support enhances favorable academic achievements, there is insufficient research elucidating the psychological process that connects support to interest in mathematics within vocational education contexts. This study demonstrates that academic self-efficacy fully mediates the association between perceived social support and interest in mathematics. Data were obtained from 260 TKJ vocational students utilizing a quantitative correlational design with a mediation model, employing validated questionnaires and evaluated through regression-based mediation testing. The findings indicated that perceived social support was a significant predictor of academic self-efficacy (β = 0.672, p &lt; .001), and academic self-efficacy was a strong predictor of mathematics interest (β = 0.596, p &lt; .001). The indirect impact was significant (β = 0.359, p &lt; .001) and constituted 93.40% of the overall effect, whereas the direct effect was non-significant (β = 0.026, p = .213), so demonstrating full mediation. The findings suggest that initiatives to increase mathematics interest in vocational schools should focus on bolstering students' academic self-efficacy through continuous social support from family, peers, and educators.

Since 2022, artificial intelligence (AI) methods have progressed far beyond their established capabilities of data classification and prediction. Large language models (LLMs) can perform logical reasoning, enabling them to plan and orchestrate complex workflows. By using this planning ability and equipped with the ability to act upon their environment, LLMs can function as agents. Agents are (semi-)autonomous systems capable of sensing, learning and acting upon their environments. As such, they can interact with external knowledge or external software and can execute sequences of tasks with minimal or no human input. In cancer research and oncology, evidence for the capability of AI agents is rapidly emerging. From autonomously optimizing drug design and development to proposing therapeutic strategies for clinical cases, AI agents can handle complex, multistep problems that were not addressable by previous generations of AI systems. Despite rapid developments, many translational and clinical cancer researchers still lack clarity regarding the precise capabilities, limitations, and ethical or regulatory frameworks associated with AI agents. Here we provide a primer on AI agents for cancer researchers and oncologists. We illustrate how this technology is set apart from and goes beyond traditional AI systems. We discuss existing and emerging applications in cancer research and address real-world challenges from the perspective of academic, clinical and industrial research.

The Australian government spends millions of dollars funding new programs every year. Taxpayers, policy makers, school leaders, teachers, and students need to know whether these programs are good. Legislation ensures they are evaluated, but do those evaluations report what good looks like, how good these programs are, and for whom? This research sought an answer to that question by analysing publicly available educational evaluations using a new conceptual framework that integrated the logic of evaluation and evaluative reasoning. Both are essential to making a credible, valid, and defensible claim about how good something is: the logic of evaluation makes the judgement legitimate, and evaluative reasoning justifies it. We examined 37 reports using our framework using an adapted systematic quantitative analysis method. Only four provided a legitimate and justified evaluative judgement; the rest we categorised as research – not evaluation. Based on our findings, we propose an updated conceptual framework we called the stairways to heaven which clarifies the steps for evaluation in comparison with research. The evaluation stairway clarifies the logic, justifications, and their relationship, integrating current resources for evaluation practice. It can be used by evaluators, evaluation commissioners, and users to clarify when evaluation is needed and get actually evaluative evaluations that connect values and data to decision-making to drive positive social change.

Background: Corneal ulcers or lesions are considered emergencies in veterinary practice and may have a prolonged course. Treatment is typically based on antibiotic therapy and the use of autologous or heterologous serum, which may be combined with other adjuvant drugs. Therapeutic decisions aim to provide an environment conducive to corneal tissue regeneration, ensuring the preservation of ocular structures and the patient's visual capacity. This report aims to describe a case of an indolent and refractory corneal ulcer in a Holstein calf, with the purpose of elucidating the therapeutic measures adopted and illustrating the patient’s visual recovery process.Case: A 4-month-old Holstein calf was admitted to the Large Animal Veterinary Hospital with complaints of apathy, pale mucous membranes, nasal and ocular discharge, and diarrhea. Additionally, physical examination revealed a whitish spot in the left eye, which, upon ophthalmic examination with fluorescein staining, was diagnosed as a corneal ulcer. Initial treatment included equine heterologous serum, 0.3% ciprofloxacin eye drops, and 1 mg diclofenac eye drops, with approximately 0.1 mL administered. After 9 days, the clinical condition worsened, with increased ulcer diameter, more pronounced conjunctival edema, photophobia, and blepharospasm. As a result, keratotomy was chosen as a therapeutic alternative to promote proper corneal remodeling. The procedure was performed under general anesthesia and occurred without complications. Following the surgery, treatment with moxifloxacin eye drops was initiated. Within 2 days, tissue neovascularization and a slight reduction of the ulcer were observed, leading to complete healing 20 days after the procedure. However, a corneal scar was noted, which was treated with prednisolone eye drops for 2 days. The corticosteroid was selected due to several favorable properties, including high intraocular concentration, ease of passage between epithelial cells, and slow absorption rate. After this therapeutic approach, only a slight residual scar remained. The animal was discharged without significant visual impairment.Discussion: The corneal tissue plays a crucial role in the visual mechanism; therefore, when affected, it requires attention, as the greater the number of compromised layers, the lower the cornea's regenerative capacity. Infectious bovine keratoconjunctivitis is an ocular condition of infectious nature, mainly caused by Moraxella bovis, which leads to severe ocular inflammation and may progress to corneal ulceration if not treated early. In the case in question, it was a deep ulcer, which contributed to a longer recovery time until complete healing. Treatment is based on the use of antibiotics and other topical drugs, as well as more invasive techniques aimed at controlling inflammation, ensuring ocular integrity, preserving visual capacity, halting lesion progression, and minimizing potential scarring. Corneal ulcers are considered emergencies in veterinary practice, demanding the clinician’s full attention and immediate treatment to avoid irreversible damage to the animal’s vision and ocular tissues. Logical reasoning should be applied to evaluate the type and severity of the ulcer, with a therapeutic approach focused on promoting an ideal environment for corneal epithelium regeneration. Thus, decisions must be made quickly, efficiently, and carefully, and the treatment should be reassessed and adjusted whenever necessary, aiming to restore corneal health.Keywords: ulcerative keratitis, cattle, melting ulcer, ophthalmology.

Towards Graph-Based Neuro-Symbolic Logic Reasoning to Improve AI Applications