Social exclusion threatens the fundamental need for belonging and disrupts emotional and physiological regulation. This becomes particularly crucial during adolescence, when navigating complex social relationships is a key developmental challenge. Although gentle touch can reduce emotional distress and facilitate autonomic regulation, research on these effects in adolescents remains sparse. Here, we investigated whether tactile stimulation modulates adolescents’ responses to virtual social exclusion using an immersive virtual reality Cyberball paradigm combined with subjective and autonomic measures. Early adolescents (N = 67) experienced virtual ostracism followed by either affective touch or a control auditory stimulus. Compared with the auditory condition, affective touch was associated with a more positive post-exclusion affective state and lower perceived arousal. Although cardiac indices were not significantly altered by social exclusion itself, participants receiving touch showed lower heart rate, reduced sympathetic activation, and greater parasympathetic activity than those receiving auditory stimulation. Exploratory correlational analyses further indicated that these effects were more pronounced in adolescents reporting higher loneliness. Together, these findings suggest that touch supports emotional adjustment following virtual social exclusion and promotes a shift toward a more parasympathetic-dominant autonomic state during a developmental period characterized by heightened sensitivity to social evaluation.
IntroductionHow leadership processes that support teacher motivation are related to effective instructional practices remains a relevant topic in educational research. However, how leader-member exchange in schools is related to instructional practices is not yet well understood. Furthermore, there remains uncertainty in the literature regarding how the relationship between leader-member interaction and teachers’ instructional practices shapes collective innovation and learning effectiveness. In this respect, the aim of the study is to examine the mediating role of perceived collective teacher innovativeness and learning effectiveness in the association between leader-member exchange and teachers’ instructional practices.MethodsThe sample consisted of 1185 teachers recruited from all 12 NUTS-1 regions of Türkiye through proportional regional stratification with voluntary participation within schools. Moreover, the study was conducted using quantitative and cross-sectional design, the study data were collected with structured scales, and Structural Equation Modeling was used in the analyses.ResultsThe analysis showed positive associations among all four constructs. Leader-member exchange had a small direct association with instructional practices (β = 0.10) and larger associations with perceived collective innovativeness (β = 0.34) and learning effectiveness (β = 0.25); the two mediators together carried about 63% of the total association (total indirect β = 0.17), with the learning pathway (β = 0.10) somewhat larger than the collective pathway (β = 0.07). The model explained 28.8% of the variance in instructional practices. Because the design is cross-sectional and all measures were self-reported, these results describe associations consistent with the proposed model rather than causal effects.DiscussionThey suggest that relational leadership is related to instructional practice mainly through teachers’ perceptions of collegial innovativeness and their beliefs about their own professional learning.
BackgroundThere is a significant gap between sophisticated models of metacognition and empirical testing of these models in educational settings, especially the hierarchical AMERT model. Agent-based simulation frameworks, and their conceptual evolution toward simulation-based digital twins, remain largely underutilized in higher education as virtual laboratories for hypothesis generation, despite their potential to model complex developmental dynamics.ObjectiveThis study aimed to design and internally evaluate a simulation-based digital twin framework that combines the five-level AMERT model and agent-based architecture to investigate the dynamics of metacognitive development.MethodUsing Design and Development Research (DDR), we developed the framework through theoretical synthesis and subjected its conceptual content to expert review via a modified Delphi method with 12 experts. Student agents were simulated using NetLogo and represented four computational learner archetypes. Instructional scenarios were simulated for 150 ticks and repeated 50 times for each scenario, and internal consistency of the simulation logic was examined using Graph Neural Networks; the outputs remained systematically distinguishable across the predefined computational archetypes (89% classification accuracy).FindingsThe hybrid scenario (instructor feedback, peer feedback, and high task complexity) resulted in the highest weighted AMERT mean (3.86) and a modeled self-transfer rate of 38% (assuming the model’s parameters). Feedback from the model teacher was correlated with a 38% decrease in simulated metacognitive gap. Importantly, simulated interventions were modeled as being uniform, which led to a greater difference in performance between the high- and low-performing agent profiles, a mathematically expected pattern under the threshold-based progression mechanism that aligns with the Matthew Effect concept and generates a hypothesis for empirical exploration in practice. All findings should be considered model-based hypotheses, not observations of real students.ConclusionThis is one of the first efforts to use simulation-based digital twin modeling to metacognition in higher education, providing a proof-of-concept framework for exploring complex metacognitive theory and proposing hypotheses for empirical testing in the future.
IntroductionAs generative AI systems increasingly provide answers in everyday and academic contexts, understanding how source attribution influences evaluative judgment has become critical.MethodsIn a within-subjects design, 103 participants evaluated 30 pre-generated reflective-correct, intuitive-incorrect, and arithmetic control-correct answers labeled ChatGPT or computer. Assignment of two parallel batteries to source labels and trial presentation order were randomized, and displays were visually matched. An independent unlabeled pretest with 35 participants supported battery comparability.ResultsParticipant-level analysis showed greater overall acceptance under ChatGPT (68.0% versus 63.4%, p = 0.002). A trial-level logistic mixed model with crossed participant and parallel-problem-pair random intercepts showed a source-by-category interaction (p < 0.001): acceptance was higher under ChatGPT for arithmetic controls (OR = 6.08, 95% CI [2.70, 13.69]), while source contrasts within the two CRT categories were not significant. Judgment accuracy is reported descriptively. Signal-detection analyses of CRT answers detected no significant source difference in sensitivity (p = 0.930) or criterion (p = 0.082). Evaluation times were longer under ChatGPT in the raw-time analysis (p = 0.034), but the source difference was not significant after log transformation (p = 0.180). Exploratory correlations detected no significant associations between source-related differences and trust, AI attitudes, or critical-thinking disposition.DiscussionThese findings distinguish overall source-related endorsement, particularly for arithmetic controls, from successful discrimination between correct and incorrect CRT answers.
The widespread adoption of generative artificial intelligence (AIGC) has reshaped animation students’ skill profiles and the conditions of instructional design in project-based learning (PBL) studios. We examine whether and how perceptions of PBL design are associated with Learning Engagement (LE), treating AIGC-assisted PBL studios as the research context rather than testing generative AI as a model variable. Drawing on Community of Inquiry (CoI) theory, we examine Problem Driven (PD), Project Design (PDe), Project Implementation (PI), and Project Evaluation (PE) as antecedents; Cognitive Presence (CP), Social Presence (SP), and Teaching Presence (TP) as statistical mediators; and LE as the outcome. We analyzed two-wave survey data from 490 upper-year animation students using PLS-SEM and fsQCA. PBL and CoI were measured concurrently at T1, and LE at T2; indirect effects therefore represent indirect statistical associations rather than mediation with a fully established temporal sequence. PD, PDe, and PI showed significant positive direct associations with LE. PE’s zero-order and single-predictor associations were positive, but its direct path coefficient became significantly negative after controlling for the other PBL dimensions and CoI mediators. The coefficient was sensitive to model specification, suggesting possible statistical suppression; it should not be interpreted as evidence that evaluation itself inherently inhibits engagement. The three CoI dimensions showed significant statistical mediation associations across all 12 indirect paths. TP and SP had relatively higher point estimates of indirect statistical associations, but differences between indirect effects were not formally tested; no dimension is claimed to be optimal. PE’s positive indirect associations through CoI partially offset its negative net direct association without reversing the total association after adjustment. The extent of this offset may depend on the degree to which CoI-related experiences are concurrently reported. fsQCA identified seven high-engagement configurations. No individual PBL dimension was indispensable. CoI dimensions appeared as core or peripheral conditions, although not every configuration included all three. These findings clarify the dual pathways linking evaluation and engagement through indirect statistical associations and configurational conditions, and provide guidance for optimizing PBL under different resource constraints. Conclusions are limited to AIGC-assisted PBL studios and do not establish the instructional effects of generative AI.
