Universities increasingly promote sustainability, yet environmental intentions do not consistently become private or public action. This qualitative study examined how university students and faculty in eastern China understood the personal, social, pedagogical, and institutional conditions shaping campus pro-environmental behavior. Four focus groups with 20 undergraduate and graduate students from arts and science disciplines and 10 semi-structured faculty interviews were analyzed using a hybrid deductive-inductive framework approach informed by an institutionally situated extended Theory of Planned Behavior. Six interrelated themes showed that intention was necessary but conditional on situated agency, habit, workload, convenience, health, safety, and institutional support. Perceived behavioral control was analytically distinguished from institutional reliability: the former concerned participants’ appraisals of capability and opportunity, whereas the latter concerned whether facilities, procedures, information, and response systems actually worked. Public participation additionally required collective efficacy grounded in student voice, visible outcomes, organizational continuity, and institutional responsiveness. Environmental knowledge became actionable when local, practical, and linked to meaningful opportunities. Co-designed living laboratories emerged as a participant-generated proposal, not an intervention conducted in the study. The findings explain how curriculum, operations, and governance jointly condition private and public environmental action.
Loneliness is increasingly prevalent among university students, posing substantial challenges to their mental health and academic performance. From a positive psychology perspective, cultivating positive emotional experiences may strengthen psychological resilience and wellbeing, particularly among individuals experiencing loneliness. Although virtual learning assistants (VLAs) are widely used in higher education, prior research has focused primarily on their functionality and learning outcomes, leaving their emotional effects, underlying mechanisms, and boundary conditions insufficiently understood. Drawing upon Social Response Theory, Emotional Contagion Theory, and Social Surrogacy Theory, this study examines how anthropomorphic VLAs influence university students' positive emotions through social presence and whether loneliness moderates this process. Across four controlled experiments, anthropomorphic design incorporating visual and verbal cues was compared with non-anthropomorphic design in terms of social presence and positive emotions. The results showed that anthropomorphic VLAs elicited greater social presence and stronger positive emotions than non-anthropomorphic VLAs. Social presence mediated the effect of anthropomorphic design on positive emotions, while loneliness strengthened its effects on both social presence and positive emotions. These findings extend VLA research from an instrumental to an affective perspective, clarify the social-psychological mechanism and boundary condition underlying anthropomorphic design effects, and inform the design of emotionally supportive educational technologies for students with higher levels of loneliness.
IntroductionPsychological resilience is commonly defined as an individual’s capacity to maintain stability and engage in constructive adaptation in the face of adversity, and its role in promoting positive developmental outcomes among college students has received increasing attention. Individuals with higher levels of resilience tend to exhibit more balanced emotional responses under stress, which may in turn facilitate their involvement in prosocial actions such as helping, comforting, cooperating, sharing, and donating. Cognitive reappraisal-the cognitive reframing of potentially stressful situations-is believed to further reinforce this adaptive response by mitigating the adverse effects of stress. However, the structural nature of the interrelations among psychological resilience, cognitive reappraisal, and prosocial behavior remains insufficiently understood, particularly regarding whether cognitive reappraisal accounts for part of the resilience–prosocial behavior association in cross-sectional data. The present study, therefore, aimed to examine the statistical dependencies among these variables based on cross-sectional survey data, with a view to providing empirically grounded references for psychological health education among college students.MethodsA cross-sectional survey was administered to 411 college students recruited from eight universities across four cities in Guizhou Province, China. Participants completed three self-report instruments: the Psychological Resilience Questionnaire (CD-RISC), the Prosocial Behavior Tendency Questionnaire for College Students (PTM), and the Emotion Regulation Strategies Questionnaire (ERQ). All measures have been previously validated in Chinese samples. Data were analyzed using SPSS 22.0. Correlation and regression analyses were conducted to examine bivariate associations among the variables. To estimate the statistical indirect effect, the PROCESS macro for SPSS (Model 4; Hayes et al., 2018) was used with 5,000 bootstrap resamples to generate bias-corrected 95% confidence intervals. Model fit for the proposed structure was evaluated using structural equation modeling in AMOS. In addition, three competing models the proposed model, a reverse-path model, and a common-cause model were compared to assess the extent to which the cross-sectional data could distinguish between alternative directional explanations.ResultsCorrelation analyses showed that the psychological resilience was positively correlated with prosocial behavior (r = 0.469, p < 0.001), and cognitive reappraisal was also positively correlated with prosocial behavior (r = 0.474, p < 0.001). Regression analysis indicated that psychological resilience accounted for 22.0% of the variance in prosocial behavior (R2 = 0.220, adjusted R2 = 0.218), with an unstandardized coefficient of 0.490 (t = 10.732, p < 0.001). When regressed on cognitive reappraisal, psychological resilience explained 34.4% of the variance (R2 = 0.344, adjusted R2 = 0.343), with a coefficient of 0.202 (t = 14.659, p < 0.001). The indirect effect of psychological resilience on prosocial behavior through cognitive reappraisal was estimated at 0.180 [BootSE = 0.041; 95% BootCI (0.100, 0.263)], accounting for approximately 38% of the total effect. When cognitive reappraisal was included in the model, the direct effect of resilience on prosocial behavior decreased from 0.471 to 0.292 (t = 5.596, p < 0.001), while the path from reappraisal to prosocial behavior remained significant (β = 0.214, t = 5.862, p < 0.001). The proposed model demonstrated good fit (CFI = 0.993, RMSEA = 0.044). However, comparison with competing models including a reverse-path model (Reappraisal → Resilience → Prosocial Behavior) and a common-cause model showed that all three models produced nearly identical fit indices, with indirect effect estimates of 0.180 for the proposed model and 0.183 for the reverse model. This equivalence is a mathematical consequence of the cross-sectional design and indicates that the data cannot distinguish between directional alternatives.DiscussionIn the current college student sample, both psychological resilience and cognitive reappraisal were positively correlated with prosocial behavior, and cognitive reappraisal accounted for a portion of the statistical effect underlying the psychological resilience–prosocial behavior association within the same cross-sectional data. This finding suggests the presence of statistical conditional dependencies among resilience, emotion regulation strategies, and prosocial tendencies; however, the nature of these interrelations should be interpreted with caution, given the cross-sectional design of the present study.
Major depressive disorder (MDD) remains challenging to treat due to the lack of objective biomarkers for monitoring treatment response and predicting outcomes. Traditional treatment response evaluation rely heavily on subjective assessments, creating a pressing need for objective biomarkers to monitor therapeutic efficacy. Functional near-infrared spectroscopy (fNIRS), a non-invasive, portable, and high-temporal-resolution brain imaging technique, can indirectly reflect brain function by monitoring metrics such as oxy-hemoglobin. This review systematically synthesizes research from 2021–2026 to detail the application of fNIRS in evaluating various depression interventions, including pharmacotherapy, physical therapies, psychological interventions, integrated interventions, and combined East–West medicine approaches. Evidence indicates that fNIRS can precisely capture hemodynamic and functional connectivity changes in key brain regions like the prefrontal and temporal cortices across different treatments, which correlate closely with clinical symptom improvement. This positions fNIRS as a promising biomarker for treatment efficacy assessment and a potential tool for predicting therapeutic response. Current limitations, such as small sample sizes and a lack of standardized protocols, are also discussed. Future directions include promoting multimodal integration, establishing standardized procedures, and building machine learning models to advance personalized and precise treatment monitoring for depression.
BackgroundBefore artificial intelligence-based judging systems can be applied to Taekwondo Poomsae, it is necessary to understand which performance components human judges can identify consistently and which remain difficult to evaluate reliably. This study examined reliability in freestyle Poomsae judging to identify evaluation components with stable and unstable human scoring.MethodsTen internationally certified Taekwondo Poomsae referees evaluated ten official competition videos twice, separated by a one-week washout period. Systematic session effects were evaluated with separate linear mixed-effects models estimated by restricted maximum likelihood using the SPSS MIXED procedure. Session was specified as a fixed effect, judge and video as crossed random intercepts, and the two observations within each judge-video pair as repeated measurements with an unstructured residual covariance matrix. Fixed-effect inference used Satterthwaite denominator degrees of freedom. Test–retest stability was evaluated for identical judge-video pairs, with primary emphasis on the absolute-agreement ICC because systematic session effects were detected. Inter-rater agreement was evaluated separately by session using two-way random effects, single rating absolute-agreement and consistency ICCs with 95% confidence intervals and Kendall’s W. Item-specific analyses were exploratory, and no multiplicity adjustment was applied.ResultsThe total score increased by 0.304 points, 95% CI [0.187, 0.421], t (99) = 5.177, p < 0.001. At the nominal 0.05 level, exploratory item-specific increases were observed for jumping side kick (β = 0.057, p < 0.001), acrobatic kicking technique (β = 0.021, p = 0.003), and expression of energy (β = 0.047, p = 0.003); the increase for basic movements and practicability did not reach the 0.05 threshold (p = 0.053). Absolute-agreement test–retest ICCs ranged from 0.039 to 0.848 and were excellent for harmony and the total score. All single-rating inter-rater ICC(A,1) point estimates were below 0.40. For the total score, ICC(A,1) was −0.007, 95% CI [−0.018, 0.036], in the first session and 0.060, 95% CI [0.010, 0.224], in the second session.ConclusionSystematic session effects, absolute test–retest agreement, relative consistency over time, and inter-rater agreement represent distinct aspects of judging reliability. Evaluation components with poor single-judge agreement require clearer operational definitions and further validation before computational or AI-assisted scoring is considered.
University students enrolled in sport-related majors may simultaneously manage academic requirements and regular training or competition, yet the short-term organization of their physical, academic/performance-related, affective, and cognitive states remains insufficiently understood. This study used a 14-day ecological momentary assessment (EMA) design with three smartphone assessments per day and multilevel vector autoregressive modeling. The original recruitment pool included 72 students from one university in Chongqing, China. Five participants were excluded for repeated responses outside the designated EMA windows, and seven were excluded for broader protocol or response-quality failures, including discontinuation of EMA completion and/or repeated invariant, non-informative response patterns. The retained analytic sample comprised 60 students and a balanced panel of 2,520 observations. Eight brief, study-specific momentary indicators were modeled: fatigue, soreness/bodily pain, academic stress, performance worry, depressed mood, tension/anxiety/irritability, rumination, and concentration difficulty. The revised primary temporal model used population-level fixed lagged slopes and showed no singularity or convergence problems. Soreness and fatigue showed the largest temporal out-strength, whereas the tension/anxiety/irritability indicator showed the largest temporal in-strength. In the contemporaneous network, the tension/anxiety/irritability indicator and rumination showed the highest expected-influence and bridge-centrality estimates. Participant-level bootstrap and case-dropping analyses indicated high centrality stability within the retained sample, although between-person edge estimates were substantially less precise. Sensitivity analyses showed that the broad outgoing and contemporaneous structure was comparatively robust to measured training, academic, and time-of-day context, whereas several incoming temporal relations, particularly those involving academic stress, were context-sensitive. Poorer-than-usual sleep remained prospectively associated with higher next-morning levels of all eight states after adjustment for participants' mean sleep, the previous-evening value of the same outcome, study day, and participant-level clustering. These findings characterize conditional associations among the specific momentary indicators measured in this single-university sample; they do not establish causal pathways, comprehensive dual-career functioning, or intervention targets.
The increasing reliance on smartphones for emotional relief and social connectivity has contributed to the rise of nomophobia. The present study examined interpersonal cognitive distortions (ICDs) as mediators of the relationship between psychological distress and nomophobia in adolescents using a cross-sectional mediation design. A sample of 500 adolescents completed standardized measures assessing psychological distress (DASS-21), nomophobia (NMP-Q), and interpersonal cognitive distortions. To examine the hypothesized mechanisms through which psychological distress influences nomophobia, a parallel mediation analysis was conducted using PROCESS Macro (Model 4). The significance level was set at <0.05 throughout the analysis. Findings from parallel mediation analysis revealed that psychological distress significantly predicts nomophobia, both directly and indirectly through two dimensions of ICDs: interpersonal rejection and unrealistic relationship expectations. However, interpersonal misperception did not mediate this relationship. Findings suggest that emotionally distressed individuals may turn to smartphones as a maladaptive coping strategy to avoid social discomfort and seek digital reassurance. This pattern fosters a self-reinforcing cycle of avoidance, cognitive distortion, and digital dependency. The study contributes to a nuanced understanding of nomophobia’s development and highlights the importance of addressing both emotional and cognitive vulnerabilities in interventions aimed at reducing problematic smartphone use.
BackgroundAs generative artificial intelligence (GenAI) increasingly enters higher education, understanding how teachers integrate GenAI into innovative teaching is critical. Prior research has mainly focused on intentions to use AI tools. Guided by the AI-Technological Pedagogical Content Knowledge (AI-TPACK) framework, Social Cognitive Theory, and professional identity theory, this study explored the relationships surrounding GenAI-supported teaching innovation behavior, with a focus on teaching self-efficacy, professional identity, and AI literacy.MethodsA total of 898 Chinese university teachers from eight comprehensive universities completed standardized scales assessing AI-TPACK dimensions, teaching self-efficacy, professional identity, AI literacy, and AI teaching innovation behavior. Data were analyzed via structural equation modeling and multi-group analysis.ResultsThe results showed that among the AI-TPACK dimensions, all except AI technological knowledge and integrative knowledge were significantly associated with AI teaching innovation behavior. Professional identity and AI literacy partially mediated the relationship between competence structures and innovative behavior, whereas the mediating effect of teaching self-efficacy was only partially supported. Further multi-group analysis showed that the relationship between professional identity and innovative behavior was stronger among teachers with lower reported frequencies of GenAI tool use.DiscussionThese findings provide empirical evidence on the associations of competence- and psychology-related factors with GenAI-supported teaching innovation. The study highlights the roles of professional identity, AI literacy, and teaching self-efficacy in promoting innovative teaching and offers practical implications for faculty development and AI integration in higher education.
IntroductionStudent learning is a dynamic process that depends on both current engagement and accumulated learning experience. Conventional statistical and machine learning approaches often overlook the temporal memory effects that influence learning progression. This study develops a fractional differential equation model to represent student learning dynamics using anonymized learning analytics data.MethodsThe proposed framework uses the publicly available Open University Learning Analytics Dataset (OULAD) to construct continuous, time-dependent variables from assessment outcomes and interactions in the virtual learning environment. A Caputo fractional differential equation is formulated to incorporate memory-dependent learning behavior. Model parameters are estimated from empirical data, and the numerical solution is obtained using the L1 finite difference scheme. Model performance is evaluated through prediction accuracy, residual analysis, and sensitivity analysis.ResultsThe simulated learning state evolves from initial values of approximately 0.52 to 0.58 to a steady-state range of 0.66 to 0.78, depending on the student group. The proposed model achieves a mean squared error of approximately 0.004 and a coefficient of determination of about 0.88, indicating strong agreement between predicted and observed learning trajectories. Residuals remain small and unbiased, while sensitivity analysis identifies the fractional order as the most influential parameter governing long-term learning behavior.DiscussionThe results demonstrate that fractional differential equations provide an accurate and interpretable framework for modeling memory-dependent learning processes. The proposed approach offers practical insights for learning analytics by supporting the identification of students who may benefit from timely instructional interventions based on their engagement patterns and memory-related learning characteristics.
Neuroarchitecture has developed rapidly at the intersection of architecture, environmental cognition, neuroscience, and immersive technologies, yet the field remains methodologically heterogeneous and its progression from measurement toward design application is insufficiently structured. This study addresses this gap through a PRISMA 2020-compliant systematic mapping review of neuroarchitecture and closely related architectural and spatial-cognition research published between 2015 and 2025. Expanded searches across Scopus and Web of Science Core Collection identified 510 records. After removal of 206 duplicates, 304 unique records were screened; 125 records entered the final eligibility and data-extraction audit, and four additional boundary or temporal exclusions resulted in a final corpus of 121 studies. The corpus comprised 61 human empirical studies, one non-human empirical study, 58 review or conceptual synthesis records, and one methodological/protocol record. Publication activity increased markedly in the later review period, with 81 studies (66.9%) published between 2023 and 2025. Among the 61 human empirical studies, questionnaire/self-report measures were used in 39 studies (63.9%), behavioral task/performance measures in 32 (52.5%), EEG in 32 (52.5%), eye tracking in 15 (24.6%), EDA/GSR in 14 (23.0%), HRV in six (9.8%), fMRI in three (4.9%), and fNIRS in one (1.6%). To organize this heterogeneous evidence base, the study develops an Immersive Spatial Cognition Framework (ISCF) linking spatial stimuli, environmental exposure, neurophysiological measurement, cognitive–emotional processing, evidence interpretation, design translation, and adaptive design, together with an Evidence-Based Neuroarchitecture Maturity Model (EBN-MM). EBN-MM classification indicated that 30.6% of the corpus remained at Stage I (Conceptual Interpretation), 35.5% reached Stage II (Experimental Validation), 33.1% reached Stage III (Evidence Integration), and only 0.8% reached Stage IV (Adaptive Design Operationalization). These findings indicate that neuroarchitecture is progressing beyond isolated experimental validation toward evidence integration, while translation into adaptive and operational design remains exceptional. The proposed frameworks provide a structured basis for comparing heterogeneous evidence and identifying methodological priorities for future evidence-based neuroarchitecture research.
IntroductionCompetitive performance in electronic sports (e-sports) depends on rapid decision-making, emotional regulation, and coordinated communication, yet little is known about whether pre-match vocal behavior contains information predictive of competitive outcomes.MethodsThis study applied supervised machine learning to 68 acoustic features extracted at the frame level (50-ms frames, 25-ms step) from 60-second pre-match team communication recordings in 89 professional Counter-Strike: Global Offensive matches; frame-level predictions were aggregated into a match-level score representing the proportion of frames classified as a win. Three predictive conditions were evaluated, acoustic features only, ranking difference only, and a combined model integrating both, using stratified group five-fold cross-validation. Uncertainty was quantified using percentile 95% confidence intervals from 2,000 match-level bootstrap resamples of the pooled out-of-fold predictions, with chance-level discrimination defined as AUC = 0.50.ResultsAcross algorithms, models combining voice and ranking achieved the strongest performance, with the Decision Tree classifier reaching a mean bootstrap AUC of 77.3% (95% CI 65.8–86.9) and accuracy of 78.6% (95% CI 69.9–86.7); all five voice-plus-ranking models had 95% CIs excluding chance. Voice-only models showed more limited evidence of above-chance discrimination: only the Decision Tree (AUC 67.8%, 95% CI 54.8–79.2) and Random Forest (AUC 64.0%, 95% CI 50.7–76.3) had confidence intervals excluding 0.50, whereas Linear Discriminant Analysis, Logistic Regression, and k-Nearest Neighbors did not. No ranking-only model showed a confidence interval excluding chance. Exploratory LIME-based feature-attribution analyses indicated that ranking difference received the highest within-model attribution in the combined models, while delta spectral flux, chroma standard deviation, and spectral centroid received the highest within-model attribution among acoustic descriptors for tree-based, linear, and distance-based classifiers, respectively; these rankings are descriptive and were not subjected to formal cross-model statistical comparison.DiscussionThese findings provide preliminary, dataset-bounded evidence that acoustic patterns in brief pre-match team communication were associated with match outcome and, for some models, contributed predictive information beyond ranking; the retrospective, single-team design does not establish a generalizable behavioral biomarker or a causal link between vocal acoustics and competitive readiness.
Generative artificial intelligence (AI) has become an integral part of educational environments, where university students often encounter algorithm-recommended information unexpectedly, known as AI information encounters. Such interactions frequently lead to increased cognitive load, and it is unclear whether the ongoing depletion of these cognitive resources is statistically linked to digital burnout among university students. This study utilizes the Stimulus–Organism–Response (SOR) framework as its analytical foundation, employing cognitive load theory to elucidate the mediating role of cognitive flexibility, and social cognitive theory to explain the moderating role of AI learning trust, thereby constructing a moderated mediation model. A cross-sectional questionnaire survey, based on 566 valid responses, is conducted and analyzed using the PROCESS macro program (Model 4, 15) to examine mediation and moderated mediation effects. The results reveal that: (1) AI information encounters are significantly positively correlated with digital burnout; (2) cognitive flexibility partially mediates this relationship, as AI information encounters negatively correlate with cognitive flexibility, while cognitive flexibility negatively correlates with digital burnout; (3) AI learning trust negatively moderates the direct path between AI information encounters and digital burnout, meaning that higher levels of AI learning trust weaken their positive association; (4) AI learning trust also negatively moderates the link between cognitive flexibility and digital burnout, indicating that higher AI learning trust diminishes the negative association between cognitive flexibility and digital burnout. This study highlights the psychological costs associated with involuntary technology use and suggests that educators in higher education should be cognizant of the cognitive resource consumption resulting from AI information encounters, and the interactive effects of AI learning trust on this relationship. Additionally, educators should encourage students to develop reasonable AI learning trust and acknowledge the risk of digital burnout linked to AI information encounters.
IntroductionWhile Self-Determination Theory (SDT) establishes the importance of autonomy, competence, and relatedness for motivation, less is known about how these needs are specifically fueled within digital learning ecosystems. Prior research often examines learning resources, support systems, and delivery modes (virtual vs. traditional) in isolation. This study addresses this gap by positing engagement as the critical mediator that explains how these combined structural and support factors fulfill psychological needs and, in turn, enhance motivation. The novel integrated model tested here uniquely combines SDT with a learning engagement framework to reveal the precise pathways through which educational environments drive motivational outcomes.MethodsUsing a quantitative approach, data were collected from students and analyzed through Structural Equation Modeling (SEM) via Smart-PLS.ResultsThe findings indicate significant direct effects, with diverse learning resources (H1: β = −0.453, t = 5.624, p = 0.000) and support systems (H2: β = 0.263, t = 2.883, p = 0.004) directly influencing engagement. Mediation analysis revealed complementary and competitive partial mediation effects, such as support systems enhancing motivation through engagement (H2: β = 0.413, t = 6.066, p = 0.000) and diverse learning resources exhibiting competitive partial mediation (H1: β = −0.512, t = 7.452, p = 0.000). Conversely, learning mode showed no significant mediation effect (H3: β = −0.025, t = 0.554, p = 0.580).DiscussionThese findings reinforce SDT’s premise that intrinsic and extrinsic factors shape student motivation, providing theoretical insights into motivation-driven learning. Practically, the study suggests enhancing support systems and engagement strategies in educational settings. The study contributes to educational psychology by extending SDT into digital and traditional learning contexts, with implications for curriculum design and policy development.
IntroductionLoneliness is increasingly recognized as a transdiagnostic vulnerability among university students, yet the behavioral correlates associated with psychological distress remain insufficiently understood. This study examined the statistical indirect association between perceived loneliness and psychological distress via problematic pornography use among Ecuadorian university students.MethodsA quantitative, cross-sectional, correlational design was used with 273 students aged 18 to 29 years (M = 23.64, SD = 1.90) who reported habitual pornography use. Participants completed measures of perceived loneliness, problematic pornography use, and symptoms of depression, anxiety, and stress. Spearman correlations, multiple linear regression, and mediation analysis with 10,000 bootstrap resamples were conducted, controlling for age and sex.ResultsPerceived loneliness was positively associated with problematic pornography use and psychological distress, and problematic pornography use was positively associated with psychological distress. In the multivariate model, loneliness and problematic pornography use were independently associated with psychological distress, whereas age and sex did not show statistically significant unique associations. The statistical indirect association between perceived loneliness and psychological distress via problematic pornography use was significant. The direct association between loneliness and psychological distress remained significant after the intermediate variable was included.DiscussionThese findings suggest that problematic pornography use may represent a maladaptive behavioral correlate linking perceived social disconnection with broader emotional distress. The results also emphasize that pornography consumption should not be pathologized based on frequency alone; attention should instead focus on impaired control, functional interference, and the psychological function served by the behavior. University prevention and counseling initiatives may benefit from integrating loneliness reduction, emotion-regulation skills, social connectedness, and the non-stigmatizing assessment of problematic sexual behaviors.
IntroductionArtificial intelligence (AI) is increasingly being integrated into science education, creating new opportunities for visualization, simulation, data interpretation, inquiry-based learning, and scientific explanation. However, it remains unclear how pre-service science teachers translate AI literacy into the professional knowledge and confidence required for AI-supported inquiry-based science teaching. Drawing on AI-TPACK and teacher self-efficacy perspectives, this study examined the serial mediating roles of AI-TPACK and science teaching self-efficacy in the association between AI literacy and Chinese pre-service science teachers’ intention to integrate AI into inquiry-based science teaching.MethodsA total of 548 Chinese pre-service science teachers from universities participated in the survey. Data were analyzed using partial least squares structural equation modeling.ResultsAI literacy was positively associated with AI-TPACK, AI-TPACK was positively associated with science teaching self-efficacy, and science teaching self-efficacy was positively associated with AI integration intention. Further analysis supported a serial indirect pathway linking AI literacy to AI integration intention through AI-TPACK and science teaching self-efficacy.DiscussionThe findings suggest that AI literacy does not automatically translate into an intention to integrate AI into teaching; rather, this association operates through contextualized technological, pedagogical, and content knowledge and positive capability beliefs. This study extends the application of the AI-TPACK framework to pre-service science teacher education and offers practical implications for systematically developing pre-service science teachers’ AI-related pedagogical competence and integration confidence.
Sustaining engagement in demanding learning contexts is a central psychological challenge, yet relatively little is known about how translation learners' internal motivational resources work together to support such engagement. The present study examined whether the alignment between persistence and confidence at baseline was prospectively associated with subsequent engagement in translation learning among Chinese university students. A two-wave survey design was employed, with 620 students participating at Wave 1 and 510 matched responses retained at Wave 2. Persistence and confidence were measured at Wave 1, and engagement in translation learning was measured 4 weeks later at Wave 2. After confirmatory factor analyses supported the distinctiveness and reliability of the focal constructs, polynomial regression with response surface analysis was conducted to test associations between congruence and incongruence in persistence and confidence and later engagement. The results showed that engagement was generally higher when persistence and confidence were aligned and increased together. However, this positive association leveled off at higher levels of congruence. Engagement was also lower when the discrepancy between persistence and confidence was larger, regardless of the direction of the mismatch. These findings suggest that engagement in translation learning is associated not only with individual psychological resources in isolation, but also with the degree to which they are internally coordinated. Because persistence and confidence were measured concurrently at Wave 1, the results should be interpreted as temporal associations rather than evidence that psychological mismatch causally reduces engagement. The study contributes to the psychology of translation learning by applying a congruence perspective to the balance between effortful persistence and capability-related confidence.
IntroductionThis study investigates Italian parents' perceptions of ‘good parenting' and cultural variations. 250 mothers and 87 fathers were surveyed during the third trimester, with follow-ups at 6–12 weeks and 6 months postpartum.MethodsUsing Grounded Theory Analysis, data underwent iterative coding, forming lower and higher order codes, super-categories, and themes.ResultsThe resulting theory, ‘The diminishing polyads of parenting,' identifies four themes for mothers and three for fathers. Mothers prioritize emotional presence, self-sacrifice, and role balance, while fathers focus more on physical presence and active involvement.DiscussionThe study advocates for a gender-neutral approach to supporting diverse family dynamics, recognizing evolving parental views and societal norms' impact. Inclusive interventions and policies can better aid families in their parenting journey.
IntroductionCochlear implants (CIs) require an external audio processor (AP), which can impose responsibilities and challenges for the user. Monitoring user satisfaction is crucial to identify and address issues that may lead to reduced device usage. The audio processor satisfaction questionnaire (APSQ) was developed to measure user satisfaction across three subscales: user comfort, social life, and device usability.MethodsWe analyzed 861 APSQ responses collected between 2019 and 2023 from CI users in Germany and the UK. Multivariable quantile and ordinal logistic regression models were used to examine factors associated with satisfaction and daily usage duration. Population norm values were established for the total score and subscales.ResultsMedian total APSQ scores were high (8.7/10). Males and users with single-sided deafness (SSD) reported slightly lower satisfaction. Off-the-ear (OTE) processors produced marginally higher usability ratings. Higher APSQ scores were strongly associated with longer daily CI usage. Each additional point on the total score was associated with 44% higher odds of using the CI at least 3 h longer per day. SSD and bimodal users showed higher odds of shorter use relative to bilateral CI users. Item-level comparisons indicated greater wearing comfort with glasses for OTE users and with headwear for behind-the-ear (BTE) users.ConclusionCI user satisfaction, as measured by the APSQ, is generally high and is positively associated with daily usage duration. The APSQ is a valuable tool for monitoring user experience and identifying populations at risk of reduced CI use. The population norm values facilitate the interpretation of clinically acquired APSQ scores.
BackgroundGenerative artificial intelligence is profoundly reshaping knowledge work, yet the cognitive mechanisms through which AI use relates to employee innovation performance remain underexplored.ObjectiveIntegrating Conservation of Resources theory and Cognitive Appraisal Theory, this study proposes that generative AI use is associated with innovation performance through a three-stage partially serial mediation pathway: resource acquisition (cognitive divergence), resource integration (cognitive elaboration), and resource activation (challenge appraisal), with work stress moderating each stage.MethodsSurvey data from 612 knowledge workers were analyzed using structural equation modeling and Monte Carlo bootstrap methods.Results(1) Generative AI use is positively associated with employee innovation performance; (2) the three-stage serial mediation model received support, with indirect effects accounting for 72.9% of the total effect; and (3) work stress moderation exhibits a stage-dependent pattern: non-significant at the resource acquisition stage but significantly strengthening mediation efficiency at the integration and activation stages.ConclusionThis study advances a chain mediation framework that offers a more coherent theoretical account of AI-enabled cognitive transformation and provides organizations with specific intervention points for shifting from tool adoption to cognitive empowerment.
BackgroundToxic leadership behaviors by nurse managers may be associated with lower career resilience among nurses. However, it remains unclear whether different regulatory focus profiles exhibit heterogeneous indirect associations in this context.AimTo examine the association between toxic leadership behaviors and nurses’ career resilience, and to investigate whether different regulatory focus profiles exhibit distinct indirect associations in this relationship.MethodsFrom May to October 2025, a multicenter cross-sectional survey was conducted among 1,210 nurses using the Toxic Leadership Behaviors of Nurse Managers Scale, the Nurse Regulatory Focus Scale, and the Career Resilience Scale. Latent profile analysis identified regulatory focus profiles, and three-step methods were used to examine profile differences and associations while accounting for classification uncertainty. Profile-based path analysis estimated overall and profile-specific statistical indirect associations.ResultsFour regulatory focus profiles were identified: Low-Regulatory (9.8%), Balanced (40.4%), Prevention-Dominant (11.9%), and Dual-High (37.9%). Career resilience was highest in the Dual-High profile and lowest in the Low-Regulatory profile. Higher toxic leadership scores were associated with lower odds of membership in the other three profiles relative to the Low-Regulatory profile. Toxic leadership was negatively associated with career resilience (total: β = −0.388, 95% CI [−0.439, −0.344]; direct: β = −0.280, 95% CI [−0.325, −0.241]), with a negative overall indirect association through regulatory focus profiles (β = −0.108, 95% CI [−0.145, −0.082]). Relative to the Low-Regulatory profile, the relative indirect contribution was negative for the Dual-High profile (β = −0.163, 95% CI [−0.228, −0.116]), positive for the Prevention-Dominant profile (β = 0.065, 95% CI [0.040, 0.100]), and nonsignificant for the Balanced profile.ConclusionToxic leadership behaviors were negatively associated with nurses’ career resilience, and the profile-specific relative indirect contributions varied across regulatory focus profiles.
