BackgroundAge-associated memory impairment (AAMI) is a predementia state linked to endothelial dysfunction. The endothelial activation and stress index (EASIX) quantifies endothelial injury, yet its association with cognitive function remains unvalidated in population studies. This study aimed to evaluate the relationship between EASIX and cognitive performance.MethodsData from adults aged ≥ 60 years in the NHANES 2011–2014 were analyzed. Multiple linear regression assessed associations between EASIX and cognitive function scores. LASSO regression selected variables, and six machine learning models (e.g., Random Forest and XGBoost) were developed. SHAP values interpreted feature importance.ResultsAmong 2,763 participants, EASIX showed a significant negative correlation with all cognitive scores (P < 0.05). The ElasticNet model outperformed other models. SHAP analysis identified EASIX as one of the top four influential variables, with cognitive function levels demonstrating a declining trend as EASIX score increased, particularly among older adults.ConclusionsEASIX is significantly negatively associated with cognitive function, especially in advanced age. Although the cross-sectional design precludes causal inference, it shows promise as a blood-based biomarker for early screening and risk assessment of cognitive decline, supporting its potential clinical utility.Supplementary InformationThe online version contains supplementary material available at 10.1186/s40001-025-03512-4.

This study examines teacher–student verbal interactions in Taiwan’s Teaching English through English (TETE) vocational high school classrooms under the national Bilingual 2030 initiative. We aimed to characterize teacher questioning and initiation–response–feedback (IRF) interaction patterns, focusing on question types, cognitive demands, and student responses. Fourteen TETE lessons (totaling 684 minutes) taught by vocational teachers in a national bilingual education competition were video-recorded. We manually transcribed the recordings and coded all teacher questions ( n = 430) using Sánchez-García’s taxonomy and Anderson &amp; Krathwohl’s revised Bloom’s taxonomy. IRF sequences were identified, and student responses were coded by language (Chinese vs. English). Descriptive and inferential statistics (correlations, ANOVA via SPSS) were applied. Inter-rater agreement on coding was 90%–92%. Quantitative results revealed that teachers’ questions were mostly procedural (28.4%), display (26.0%), or off-task (13.0%). Referential (content-focused) questions were significantly more common among higher-performing teachers. By cognitive level, 62% of questions targeted recall (Bloom’s lowest level), 28% application, and 10% analysis. Question lexical complexity aligned with basic-user proficiency (CEFR A1–A2). In 197 IRF sequences, students responded more often in Chinese (202 instances) than English (176 instances). Teachers’ question frequency was positively correlated with teaching performance (R = .585, p = .028). ANOVA indicated significant grade-level differences in questioning frequency ( F (2, 11) = 4.619, p = .035), with ninth-grade teachers asking significantly fewer questions. These findings indicate that current TETE classroom discourse is dominated by lower-order, procedural questions and frequent first language (L1) use, posing challenges for English-immersion goals. We discuss implications for Taiwan’s bilingual education policy and practice, including the need for targeted teacher development to promote higher-order questioning and balanced L1/L2 interaction to better support bilingual instruction. These results highlight the need for classroom practices and policies aligned with Taiwan’s bilingual goals.

Human-AI interaction, particularly in educational contexts, is a dynamic and cognitively demanding process that holds promise for enhancing goal-directed learning. Yet, there remains a scarcity of empirical studies that examine how learners’ interaction with generative AI (GenAI) varies in structure and how these patterns influence distinct learning outcomes. This study investigates the relationship between human-AI interaction processes and outcomes such as AI literacy, problem-solving skills, metacognitive strategies, and task performance. We conducted an experimental study with 45 secondary school physics student teachers engaged in a GenAI-supported lesson plan assessment task. Using questionnaire responses, trace data, and prompt logs, we coded human-AI interaction behaviours based on self-regulation and cognitive processing levels. Through sequence clustering analysis, we identified two distinct interaction patterns. Both clusters showed significant improvement in task performance, but with divergent benefits. Cluster 1 exhibited diverse regulation processes characterized by exploratory, divergent prompting and low-level cognitive engagement in the early stages. This group showed significant gains in problem-solving skills through active idea generation and broad reflection. Cluster 2 demonstrated structured regulation behaviours, initiating interaction with deep-level cognitive processing and convergent prompting. These learners made more deliberate modifications and completed full self-regulated learning (SRL) cycles—planning, monitoring, and reflecting—which led to enhanced AI literacy and metacognitive strategy use. Our findings suggest that effective human-AI collaboration goes beyond prompt diversity; structured regulation behaviours serve as a key mediator between prompting and learning gains. GenAI served as both cognitive and metacognitive scaffolding, facilitating critical assessment and productive delegation. These results contribute to SRL theory in AI contexts and emphasize the importance of process-level analysis. Limitations include a small sample and limited prompt feature analysis. Future research should explore emotion-aware AI systems, multimodal interaction data, and the impact of task complexity on interaction dynamics. This study provides practical insights for educators and designers of AI-integrated learning systems. Specifically, it highlights the importance of tailoring AI scaffolds to different learner regulation styles: for exploratory learners, scaffolds can encourage broad idea generation and reflection, while for structured learners, scaffolds should support iterative planning and monitoring. These findings underline both opportunities and limitations of current GenAI use in classrooms, suggesting concrete directions for teacher practice and instructional design.

ABSTRACT The purpose of this study is to explore how integrating artificial intelligence tools into practical course instruction can enhance metacognitive skills and environmental awareness among art and design students. Using an action research methodology, this study focuses on a practical course that teaches design as part of an art and design curriculum at a university in Henan Province, China. Log records, interviews, and classroom observations were used to gather data. The research investigates the teaching paradigm of employing artificial intelligence technologies to improve students’ environmental awareness. It does this by following the four processes of action research: preparation, action, observation, and reflection. According to the study, integrating AI tools into all facets of instruction is a dynamic, continuous process. Through the three phases of project-based learning, students gradually increase their understanding of the environment, develop their environmental skills, and foster the formation of environmental attitudes. The results imply that artificial intelligence enhanced instructional designs can support students in more effectively integrating a range of components, expressing themselves creatively, and using digital technologies. It can also help students gain a deeper understanding of environmental issues and the significance of sustainable development, which raises their cognitive level of environmental awareness.

Accurate classification of cognitive levels in instructional dialogues is essential for personalized education and intelligent teaching systems. However, most existing methods predominantly rely on static textual features and a shallow semantic analysis. They often overlook dynamic temporal interactions and struggle with class imbalance. To address these limitations, this study proposes a novel framework for cognitive-level classification. This framework integrates time entropy-enhanced dynamics with a dynamically weighted, heterogeneous ensemble strategy. Specifically, we reconstruct the original Multi-turn Classroom Dialogue (MCD) dataset by introducing time entropy to quantify teacher–student speaking balance and semantic richness features based on Term Frequency-Inverse Document Frequency (TF-IDF), resulting in an enhanced MCD-temporal dataset. We then design a Dynamic Weighted Heterogeneous Ensemble (DWHE), which adjusts weights based on the class distribution. Our framework achieves a state-of-the-art macro-F1 score of 0.6236. This study validates the effectiveness of incorporating temporal dynamics and adaptive ensemble learning for robust cognitive level assessment, offering a more powerful tool for educational AI applications.

ABSTRACT Background Sustainability education emphasises critical thinking and interdisciplinary understanding, making the assessment of students' learning outcomes complex. While Large Language Models (LLMs) have shown promise in educational assessment, their reliability in domains requiring contextual reasoning—such as sustainability—remains unclear. Objectives This study aims to evaluate the agreement between human raters and several LLMs (GPT‐4o, Gemini 2.0 Flash, DeepSeek V3, LLaMA 3.3) in assessing short‐answer responses from a university‐level Sustainability course. It also investigates how this agreement varies across cognitive skill levels. Methods A total of 232 short‐answer responses were evaluated using a rubric aligned with Bloom's Revised Taxonomy. Consensus scores from human raters were compared to LLM‐generated scores using multiple statistical measures, including Quadratic Weighted Kappa (QWK), Intraclass Correlation Coefficient (ICC), Pearson correlation, and distributional overlap. Results Moderate agreement was found between LLMs and human raters in total scores (QWK: 0.585–0.640; r : 0.660–0.668; : 0.681–0.803). Inter‐rater reliability among humans was good to excellent (ICC: 0.667–0.800). Criterion‐level agreement declined as cognitive complexity increased, with notably low agreement on evaluating higher‐order skills. Conclusions Overall, LLM–human agreement was moderate on total scores but declined at higher cognitive levels, indicating that LLMs are suitable for basic comprehension checks while human oversight remains necessary for complex reasoning.

Gustatory function and cognitive impairment in the Korean elderly.

ObjectiveThis study seeks to analyze the coordinated patterns of spontaneous neural activity and instantaneous electrical activity in the brain using dual-dimensional indicators of brain functional connectivity and microstates, aiming to identify potential biomarkers for early screening and precise classification.MethodsA case-control study design was utilized, with 195 older adults suffering from cognitive impairment (with a roughly equal distribution of mild and moderate cases) serving as the case group, and 65 healthy older adults matched as the control group. Participants were required to complete a demographic questionnaire, the Montreal Cognitive Assessment Scale, the short form of the International Physical Activity Questionnaire, and the Pittsburgh Sleep Quality Index, after which 5 min of eyes-closed resting EEG signals were recorded.ResultsSignificant differences were observed in the average strength and density of brain functional connectivity within the δ and θ frequency bands among older adults with different cognitive levels, indicating that higher average strength and density corresponded to more severe cognitive impairment (P < 0.05). Older adults with varying cognitive levels showed significant differences in both static features (Duration, Coverage, Occurrence) and dynamic features (transition probabilities) of microstates A, B, C, and D (P < 0.05). In terms of static features, stronger temporal characteristics of microstates B and D were associated with greater severity of cognitive impairment, while microstate A demonstrated the most pronounced temporal characteristics during the mild cognitive impairment stage (P < 0.05). In dynamic features, healthy older adults primarily exhibited bidirectional balanced transitions between A/C↔C/A and B/D↔D/B, while those with mild cognitive impairment displayed transitional characteristics in the paths A→B/D and C→D. In contrast, older adults with moderate to severe cognitive impairment showed significantly enhanced directed transitions from microstates B/D to A/C (P < 0.05).ConclusionOlder adults with mild cognitive impairment demonstrated increased abnormal and redundant brain functional connectivity, inefficiency in microstate C, and compensatory mechanisms in low-frequency connectivity in brain functions as well as microstates A, B, and D. Older adults with moderate to severe cognitive impairment displayed sustained compensatory mechanisms in brain functional connectivity and microstates, characterized by dominant abnormal and redundant connections, along with pathological hypersynchrony in microstates B and D, which persisted until rigidity set in.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12984-025-01803-9.

Objective. A novel phase-amplitude coupling (PAC) estimator is proposed to address the limitations of existing PAC estimators in terms of insufficient application scenarios.Approach. The polar mutual information (PoMI) method is compared with the currently dominant PAC estimators, mean vector length, Kullback-Libler distance, general linear model, and phase-locking value, focusing on analyzing its characteristics in terms of coupling strength sensitivity, data length dependency, noise resistance, and coupling frequency band sensitivity. We recruited 54 healthy controls and 41 mild cognitive impairment (MCI) patients and assessed their cognitive level and whole-brain PAC connectivity by neurophysiological tests and resting electroencephalography, respectively.Main results. The PoMI algorithm is sensitive to changes in coupling strength, exhibits low dependence on data length, is insensitive to noise variations, and produces stable computational outcomes. Therefore, the PoMI algorithm can quantify the PAC phenomenon in neural oscillations. Furthermore, reduced PAC connectivity in the frontal lobe of patients with MCI, while PAC activity is enhanced in the parietal and occipital lobes. The results indicate that alterations in prefrontal PAC connectivity in MCI patients may represent one manifestation of neuronal group degeneration in the prefrontal cortex of these individuals.Significance. The PoMI algorithm can effectively evaluate the PAC phenomenon in neural oscillations and can be used as a PAC estimator. (Approved No. of ethic committee: 2024-P2-210-02).

Theta activity as a marker of cognitive development in infancy: A longitudinal study across the first two years of life.

DeepLabCut, a deep-learning-based network for markerless pose estimation, has been used to track the acquisition and development of tool use in a nontool-using corvid species. This development has the potential to lead to new lines of research understanding how tool and motor expertise develop at the behavioral, neural, cognitive, and evolutionary level. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Worsening binge eating over time following an unsupervised low-carbohydrate diet.

Is developmental imitation related to rational imitation in young children with ASD?

Artificial intelligence (AI) tools like ChatGPT offer new opportunities to enhance student learning through active recall and self-directed inquiry. This study aimed to determine student perceptions of a classroom assignment designed to develop proficiency in using ChatGPT for these strategies. First-semester Doctor of Pharmacy students in a foundational sciences course completed an assignment using ChatGPT for active recall. The assignment involved generating quizzes from lecture notes on protein structure and apoptosis, verifying ChatGPT's answers, and engaging in further inquiry. Students completed a Qualtrics survey assessing their perceptions. Nearly 60% of students had no prior ChatGPT experience, and only 21% had used it for previous quizzing purposes. Most (96%) found the instructions for the assignment clear, and 89% reported that ChatGPT was easy to use. The majority believed ChatGPT quizzes were as effective as instructor-provided quizzes. All students agreed that verifying ChatGPT's answers with lecture notes was a valuable learning experience. Open-ended responses highlighted the ease of generating additional questions and exploring concepts at higher cognitive levels, though some noted challenges with crafting precise prompts and verifying answers. Although many students were new to ChatGPT, the structured assignment improved their comfort with and understanding of the platform's capabilities and limitations when used for active recall and critical inquiry. The integration of AI tools, when guided and purposeful, can enrich traditional learning methods and support student engagement and deeper understanding of biological concepts in pharmacy education.NEW & NOTEWORTHY A structured classroom assignment introduced first-semester pharmacy students to using ChatGPT for active recall and self-directed learning. Despite limited prior experience with the tool, most students found it easy to use and an effective learning tool. Verifying ChatGPT's responses with lecture notes was viewed as a valuable learning strategy. Students appreciated the platform's ability to support higher-level inquiry. Overall, the assignment enhanced student engagement and understanding of AI-assisted learning.

Artifical Intelligence (AI) has been a significant transformative force in modern education especially through providing personalized learning pathways and increasing student engagement. In this systematic review we will synthesize current developments in AI based personalized learning models and assess how these AI based models support student engagement on behavioural, emotional, and cognitive levels. We used a structured literature search over four databases: IEEE, Scopus, Springer, and ScienceDirect; to find all peer reviewed articles written since 2023 until 2025. Our results show that machine learning models specifically XGBoost, deep neural networks and reinforcement learning are very effective at identifying students' needs and providing dynamic, adaptive instruction. Recommendation systems, based on AI, and behavioural analytics also enhance students' motivation, encourage active participation in class, and improve academic achievement. The results also highlight several limitations such as explainability of AI decision making processes; ethically responsible personalized learning; ability of AI to adapt in real time; and limited utilization of multi modal student engagement data. Finally, our review includes recommendations for future research directions and outlines an integrative framework for using AI to provide personalized learning that is both scalable and equitable.

The study aims to examine university students' rumination, cognitive defusion, and subjective vitality levels according to their depression levels. The study group consists of 846 students from two different universities. The data in this study was gathered by the Lovibond Depression, Anxiety, and Stress Scale, Ruminative Response Scale, Drexel Defusion Scale, and Subjective Vitality Scale. To reveal the relationship between variables in the data, the Pearson moment correlation coefficient was calculated. To determine if the rumination, cognitive defusion, and subjective vitality levels change in accordance with the depressive symptoms of the participants, ANOVA analysis was implemented, and to determine if there is a difference between the groups, Tukey was calculated among post hoc tests. The Jamovi (2019) program was used for the analysis of the data. As a result of this study, it was concluded that depression is negatively related to cognitive defusion and subject vitality, positively related to rumination. As the depression levels of individuals increase, their rumination level increases, their cognitive defusion levels decrease, and their subjective vitality levels decrease. At the same time, there was a negative relationship between rumination, cognitive defusion, and subjective vitality. There was a positive relationship between cognitive defusion and subjective vitality. The findings demonstrate that depression plays a critical role in shaping mental health dynamics among university students, offering novel insights into the interactions between depression, rumination, cognitive defusion, and subjective vitality.

Critical Thinking is the ability to think rationally and see problems objectively so that the results obtained are unusual and in accordance with existing reality. HOTS is a process of thinking of students at a higher cognitive level developed from various cognitive concepts and methods and learning taxonomies such as the problem-solving method, Bloom's taxonomy, and the taxonomy of learning, teaching, and assessment. This study uses a descriptive qualitative approach, which aims to analyze students' critical thinking skills in solving mathematics problems. This study was conducted in August 2024 at SMP IT Daarul Istiqlal. This study took a sample of grade VIII, namely 30 students. Furthermore, students will be classified based on the level of high metacognitive ability, medium metacognitive ability and low metacognitive ability according to the results of the analysis of student answers. Grouping categories of high, medium and low metacognition. Subjects were selected based on considerations of availability and relevance to the research objectives. Data collection techniques used were using questions or tests, interviews, questionnaires and documentation. Documentation is a qualitative data collection technique in the form of printed, written data and recordings in answering questions. This data analysis technique uses three stages: data reduction, data presentation, and drawing conclusions. The results of the data analysis are specifically organized and presented descriptively.

ABSTRACT Competitive gaming esport players manage performance pressure, negative emotions, and ultimately what is known as tilt – a common phenomenon described in the game of League of Legends (LoL) as frustration associated with performance decline. Given the characteristics of LoL game competitive organisation, and its association with tilt-related behaviours, the present study focused on factors and processes related to tilt in LoL esports players. More precisely, a qualitative methodology grounded in post-positivism was applied to examine tilt antecedents, its effects on players, and the strategies used to manage tilt-inducing situations at emotional, cognitive, and behavioural levels. To that end, LoL players (N = 14 men), with ages ranging between 19 and 28 (M = 23.7, SD = 2.3 years), were interviewed and their responses were analysed using reflexive thematic analysis. Tilt antecedents identified included: teammates failing to meet players’ expectations; teammates’ displaying toxic behaviours; players failing to meet their own expectations; and players experiencing negative emotions. Such tilt experiences were reported to result in players feeling discouraged, losing control, misplacing attention, and displaying toxic behaviours. Ultimately, players reported to manage tilt by muting the in-game chat, refocusing attention, accepting and letting go of tilt-inducing events, and taking breaks. Findings provide a process-based understanding of tilt in relation to antecedents to tilt, its impact on esport players, and how esport players ultimately manage such tilt experiences in the game of LoL.

Purpose This study reconceptualizes job-seeking as a preparation for transition. While existing transition theory primarily focuses on the transition experiences, limited attention has been paid to the preparation phase. We investigate how graduating university students’ information behaviors on a social platform contribute to their preparation for the campus-to-career transition. Design/methodology/approach We selected a typical social platform, Rednote (Xiaohongshu), as a case to deeply explore the process of users’ information behavior at the temporal, behavioral, cognitive and emotional levels. We employed a qualitative research design featuring semi-structured interviews with 20 university undergraduate and master’s students who were seeking jobs and were also frequent Rednote users. Data analysis followed a grounded theory approach. Findings Our analysis revealed four distinctive characteristics of job-seeking as the preparation for transition: stage-based progression (planting, cultivation, drought and harvest stages), continuously evolving information needs, reliance on social interactions and requirements for diverse information types. We identified two pivotal information strategies on the social platform, linking and comparing, that may function to either facilitate or hinder at different stages. These findings informed the development of the information behavior model in preparation for the transition stage. Originality/value This research extends transition theory to the preparation stage by distinguishing preparation for transition. Based on the examination of job-seeking-related information behavior on Rednote, an increasingly important yet understudied platform for graduating student job-seeking, this research reveals how social platforms’ unique characteristics shape the preparation for transition processes. It proposes a theoretical model of information behavior specifically for the transition preparation stage, using graduating student job-seeking on Rednote as the empirical context to capture the multidimensional nature of pre-transition information behavior.

Proficiency in reading not only enhances one's knowledge but is essential for a good education. In the framework of education, students must read any written materials about any subject area to understand and become familiar with the source (Nair, 2020). It is common knowledge that proficient readers possess well-developed reading abilities (Hernández-Chérrez et al., 2020). Reading comprehension is thought to be a difficult activity that essentially requires two stages: decoding graphemes and extracting linguistic meanings (Tárraga-Mínguez &amp; Sanz-Cervera, 2020). Along with this, reading comprehension is a necessary skill for deriving meaning from the text (Hasanah &amp; Kholili, 2023). Reading comprehension is the capacity to comprehend and appropriately interpret the information included in a text (Grabe &amp; Stoller, 2019),. Additionally, they pointed out that reading without comprehension is pointless, proving that the two are inextricably linked (Guo et al., 2023). Students must comprehend what they read and derive meaning from it; simply decoding words on a page is insufficient (Lai &amp; Mukundan, 2023). Students who are unable to comprehend are considered to be reading a blank page (Imsa-ard, 2022). However, problems including visual literacy difficulties, unfamiliarity with GOs, poor cognitive level, and choosing the right GO all affect how effective graphic organizers are (Lai &amp; Mukundan, 2023).