Search Results (421)

The increasing use of Neural Language Models (NLMs) calls for strategic and metacognitive skills in prompt formulation, monitoring, and adaptation. This study presents the Strategic Prompting Scale (SPS), designed to measure the extent to which users apply planning, adaptation, and evaluation strategies during interactions with generative language models. After expert review and pilot testing, an exploratory factor analysis with 187 participants revealed a three-factor structure—Planning, Adaptation, and Evaluation—explaining 60.4% of the total variance. The final 15-item scale demonstrated good internal consistency. Confirmatory factor analysis with an independent sample of 406 participants supported the proposed factor structure. The SPS also showed theoretically expected associations with related constructs, including positive correlations with metacognition and critical thinking, and negative correlations with disengagement in AI interaction. Overall, the findings provide preliminary psychometric evidence for the SPS as a theory-driven instrument to assess metacognitive regulation and self-regulated behaviors in generative AI use, with potential applications in educational, professional, and research contexts. Full article

This study investigates the effectiveness of peer assessment as a formative learning strategy in multilingual English-Medium Instruction (EMI) higher education. Although peer assessment is widely associated with enhanced learner engagement, self-regulation, and feedback literacy, its implementation in linguistically diverse EMI settings remains insufficiently explored. Addressing this gap, the study examines students’ perceptions of peer assessment, the degree of alignment between peer and instructor evaluations, and students’ reflective experiences in a multilingual university context. A mixed-methods design was employed with 60 undergraduate students enrolled in EMI courses at South East European University in North Macedonia. Quantitative data were collected through a structured questionnaire and peer-assessment rubric, while qualitative data were obtained from reflective interviews with 37 students. Quantitative data were analyzed using descriptive statistics, Pearson correlation analysis, and paired-samples t-tests, whereas qualitative responses were examined through thematic analysis. The findings reveal generally positive student perceptions of peer assessment, particularly regarding self-reflection, critical thinking, collaboration, and confidence in evaluating academic work. Significant positive correlations among key dimensions of feedback literacy suggest that peer assessment supports interconnected cognitive and metacognitive learning processes. Although a statistically significant difference emerged between peer and instructor scores, overall agreement was moderate, with students tending to assign slightly lower marks than instructors. Qualitative findings further indicate that peer assessment enhanced students’ understanding of assessment criteria and learning processes while also exposing challenges related to language proficiency, emotional discomfort, and concerns about feedback accuracy. The study demonstrates that peer assessment can be an effective pedagogical approach in multilingual EMI classrooms when supported by clear assessment criteria, structured guidance, and feedback training. These findings contribute to research on feedback literacy, formative assessment, and multilingual learning in higher education. Full article

The proliferation of generative artificial intelligence (GenAI) has precipitated an urgent, global reassessment of how higher education evaluates critical thinking, creative agency, and academic integrity. However, scholarly and institutional responses remain fragmented across cultural contexts, impeding the development of robust, flexible, and discipline-adaptable assessment frameworks. Responding to the imperative to move beyond the traditional standardized assessment paradigm, this study conducts a comparative discourse analysis of 5368 academic articles in Anglophone/Western scholarly discourse (Web of Science, WoS) and Chinese (CNKI). Using LDA topic modeling and Word2Vec-enhanced semantic analysis, the study identifies two divergent orientations: an Anglophone/Western discourse that frames AI as an instrument for cognitive augmentation, efficiency optimization, and functional human–AI collaboration; and a Chinese discourse that emphasizes epistemic sovereignty, the reconstruction of creative subjectivity, and systemic institutional rebuilding against technological alienation. These pathways are mapped onto a tripartite framework of Tools, Creative Subjectivity, and Organizational Ecosystems. The findings demonstrate that AI integration is culturally embedded rather than technically determined, carrying profound implications for assessment validity, academic integrity policy, and equitable access to AI-enhanced learning. The study synthesizes these insights into a culturally responsive assessment framework that redirects evaluation from standardized, product-centric outputs toward process-oriented, transparent, and ethically governed human–AI co-authorship. By centering critical autonomy, AI literacy, and epistemological diversity, this framework offers actionable strategies for inclusive assessment redesign, institutional policy development, and sustainable competency cultivation in the GenAI era. Full article

Learning agility has become an essential capability for employees working in technology-driven environments characterized by rapid change and uncertainty. Despite increasing attention on learning agility, limited empirical research has examined how different levels of cognitive abilities contribute to its development, particularly among Generation Z employees. This study investigates the cognitive determinants of learning agility by distinguishing between basic cognitive abilities and high-level cognitive abilities and examining their roles across established and start-up companies. A total of 270 Generation Z employees in Indonesia participated in the study, consisting of 135 employees from established companies and 135 from start-up companies. Cognitive abilities were assessed using objective psychometric instruments, where basic cognitive abilities (reasoning, memory, attention, coordination, and perception) were measured using CogniFit, while high-level cognitive abilities were assessed through the Divergent Association Task (DAT) for creativity, the Watson–Glaser Critical Thinking Appraisal for critical thinking, and the FourSight framework for problem-solving. Learning agility was measured using a multidimensional behavioral scale. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that higher-order cognitive abilities play a more prominent role in shaping learning agility than basic cognitive abilities. Creativity and problem solving consistently demonstrate significant positive relationships with learning agility across organizational contexts, while reasoning, critical thinking, and perception show context-dependent effects across organizational environments. These findings suggest that learning agility is primarily driven by generative and evaluative cognitive processes rather than by basic cognitive efficiency alone. The study contributes to a deeper understanding of the cognitive architecture of learning agility and provides insights for organizations seeking to develop adaptive talent in rapidly evolving technological environments. Full article

Background: The convergence of machine learning (ML), gamification, and critical thinking assessment within adaptive educational platforms has accelerated since 2020, driven by large language models (LLMs) and graph neural networks (GNNs). No prior systematic review has jointly addressed all three dimensions, and Central Asian educational contexts remain underrepresented. Methods: Following PRISMA 2020 guidelines, we searched Scopus (n  =  4396) and OpenAlex (n  =  4152) for publications from 2016 to 2026. Quality assessment used the Mixed Methods Appraisal Tool (MMAT 2018; threshold ≥  2), yielding 82 papers. Five research questions addressed ML personalization (RQ1), gamification and engagement (RQ2), critical thinking assessment tools (RQ3), recommendation algorithms (RQ4), and regional applicability in Kazakhstan and Central Asia (RQ5). Results: Transformer-based and GNN models dominate the recent literature (52% of corpus from 2025), with an accuracy of 91–97% for dropout prediction and learning path recommendation under single-institution conditions. Gamification studies report up to 90% student satisfaction; LLM-based critical thinking assessment shows promise but faces validity concerns. Thirteen papers address Central Asian contexts. Conclusions: Significant gaps persist: no integrated gamification–critical thinking framework exists, recommendation systems lack explainability, and Kazakh-language datasets are severely underrepresented. Future research should prioritize multilingual adaptive systems, explainable algorithms, and privacy-preserving federated learning for low-resource contexts. Full article

Developing critical thinking is a central aim of contemporary higher education, yet conventional instructional approaches often underuse authentic, real-world materials that stimulate higher-order reasoning and reflective judgment. The study examines the effectiveness of case-based socio-legal pedagogy in fostering critical thinking within contexts of legal pluralism and social complexity. A quasi-experimental mixed-methods pre–post design was conducted with 62 undergraduate students enrolled in a course on Islamic law and society. Over a four-week intervention, students engaged with six socio-legal cases drawn from Muslim women’s legal experiences in Israel, focusing on divorce, maintenance (nafaka), and child custody. Quantitative data were collected using a validated Critical Thinking Rubric assessing argumentation, evaluation of multiple perspectives, and legal reasoning. Results showed significant improvement in overall critical thinking, with gains across all measured dimensions. Qualitative analysis of written assignments and student reflections revealed greater recognition of legal ambiguity, more structured and evidence-based argumentation, and deeper engagement with competing normative and social frameworks. Overall, the findings highlight the pedagogical value of integrating socio-legal complexity into case-based learning as an adaptable model for strengthening critical thinking across disciplines involving interpretive, contested, and context-dependent knowledge in higher education and other fields requiring careful judgment under conditions of uncertainty and change. Full article

Artificial intelligence (AI) is transforming higher education, although most research addresses its integration in terms of frequency of use or technological acceptance, without examining how it translates into specific curricular and instructional decisions. That is why this study has a dual aim: to develop and validate the AI Instructional Design Questionnaire for Critical Thinking (AID-CTQ) and to analyze how university faculty integrate AI into instructional design practices in higher education. The sample included 144 faculty members from a university in Madrid, selected by convenience. Exploratory and confirmatory factor analyses of the questionnaire supported a three-factor structure: Activity Design (F1), Critical Thinking Assessment (F2), and Self-Regulation and Reflection (F3). The final 12-item model shows good model fit (CFI = 0.98, TLI = 0.98, RMSEA = 0.05, SRMR = 0.05) and adequate overall reliability (α = 0.86). At the item level, responses related to assessment and reflective practices showed consistently high agreement, whereas items linked to activity design displayed greater variability. Faculty members with more than 10 years of experience obtained significantly higher scores, indicating that the educational value of AI depends less on the tools used and more on the quality of instructional decisions. Reported use of AI was high, with ChatGPT and Copilot being the most frequently used tools. Overall, the findings indicate that the integration of AI in higher education is evolving from predominantly instrumental uses toward more pedagogical and curriculum-oriented forms of implementation. Accordingly, the educational value of AI lies less in the tool itself than in the quality of the instructional decisions through which it is meaningfully embedded in the curriculum. Full article

Small-scale island communities whose livelihoods depend on aquaculture are increasingly vulnerable under interacting climatic and non-climatic stressors. Conventional indicator-based assessments are useful for describing the level of vulnerability, but many empirical assessments remain less able to explain how multiple stressors are mediated through local social–ecological structures and feedback processes to produce different vulnerability patterns. This study aims to explain how vulnerability is formed in island aquaculture communities by linking social–ecological system structures with vulnerability processes and by examining empirically informed feedback pathways. Drawing on evidence from three island aquaculture communities in southeastern China, household survey data were first used to classify community types through hierarchical clustering. Semi-structured interviews, field observations, and documentary materials were then qualitatively coded to develop empirically informed conceptual causal loop diagrams (CLDs) for each type. Key variables and recurring feedback pathways were identified through loop-based structural analysis and cross-case comparison. The analysis indicates that vulnerability formation in island aquaculture communities is associated with recurring reinforcing feedbacks within local social–ecological system structures, through which multiple climatic, ecological and socio-economic stressors are translated into differentiated vulnerability outcomes. Across the case communities, resource overexploitation and marine pollution reinforce an ecology–livelihood degradation loop, while labor outmigration erodes social capital, disrupts intergenerational knowledge transmission, and weakens collective action and adaptive capacity, exacerbating socio-ecological vulnerability. At the same time, dominant stressors, key drivers, and feedback configurations vary across community types, generating divergent vulnerability trajectories and highlighting the context-dependent nature of vulnerability dynamics. These results suggest that governance interventions targeting isolated stressors or relying on static vulnerability analyses are insufficient where reinforcing feedbacks dominate. Effective adaptation strategies should explicitly target critical feedback pathways and strengthen stabilizing processes. By integrating social–ecological systems thinking with vulnerability analysis, this study provides a feedback-oriented approach for diagnosing vulnerability formation and supports more feedback and context-sensitive governance in small-scale island aquaculture communities. Full article

The study investigated the tensile strength and elongation properties of abaca fiber-reinforced polymer (AFRP) composites after varying durations of seawater soaking, with a focus on their potential for reinforcing fiber-optic cables. It aims to bridge industrial technology education, experiential learning, and green technology by evaluating abaca fiber as a sustainable alternative to synthetic aramid yarn. Conducted at Caraga State University, Cabadbaran Campus (CSUCC), the research utilized a quasi-experimental product development design involving industrial technology students and instructors. Tensile strength testing and comparative analysis were performed on abaca fiber samples (A, B, and C) subjected to different seawater soaking durations. Results show that soaking time significantly affects the fiber strength, with Sample A achieving the highest tensile strength (5631.5 MPa) and Sample C the lowest (1679.8 MPa). Findings indicate that prolonged exposure to seawater weakens abaca fiber, emphasizing the need for controlled treatment to optimize its industrial applications. This study emphasizes the importance of hands-on learning in industrial technology education, promoting critical thinking and technical skills while underscoring sustainability. The research advocates for eco-friendly materials in industrial applications and highlights the potential of abaca fiber composites. Future studies should investigate pre-treatment methods to enhance fiber durability, assess the long-term environmental performance, and conduct large-scale pilot testing to evaluate commercial viability. By integrating sustainable innovations into industrial technology education, this study contributes to advancing natural fiber composites for manufacturing and telecommunications infrastructure. Full article

In early childhood initial teacher education, a growing number of mature-aged students with diploma qualifications and years of professional experience are undertaking their early childhood teacher degrees through online modes. Given the national staff shortage of early childhood teachers and the important role of higher education in professional development, it is crucial to support these students’ success in their online learning. Drawing on the critical reflection theory and the notions of “reflection-in-action” and “reflection-on-action”, this autoethnographic study examines a university lecturer’s perspective on the challenges of teaching mature-aged students in online Bachelor of Early Childhood Education programs. Four themes have been identified from the current study: the need for step-by-step technical support for the online learning system; acknowledgment of students’ practical experience contributes to online tutorial classrooms; the need for guidance for ethical and responsible use of Generative Artificial Intelligence (GenAI) in class discussions; and interactive dialogic guidance to support their assessment preparation. This study also included specific pedagogical adaptations to support these students, including offering technical support to assist mature-aged students in transitioning to university study, drawing on students’ professional knowledge to promote active engagement, providing interactive guidance to support understanding of assignment instructions, integrating open discussions about the use of GenAI in online class activities, and asking follow-up questions to encourage critical thinking. This study deepens our understanding of how university educators support mature-aged ECE students in their online learning through tailored pedagogical adaptations that align with their unique needs. Full article

Early childhood is a critical period for creative personality development. Guided by Amabile’s Componential Model of Creativity, this research explored the higher-order structure, developmental trajectories, and predictive role of preschoolers’ creative personality. Study 1 suggested an emergent three-factor higher-order structure comprising intrinsic driving, growing, and openness factors. Due to the longitudinal instability of the openness factor observed at this age, subsequent analyses focused on the two core components. Study 2 employed longitudinal latent growth modeling to examine the developmental trajectories of the intrinsic driving and growing factors across three waves. Study 3 assessed the predictive effects of these two factors on creative thinking performance (specifically figural divergent thinking). The results indicated that: (1) the nine teacher-rated dimensions of preschoolers’ creative personality exhibited preliminary evidence of multidimensional higher-order organization; (2) both intrinsic driving and growing factors significantly increased with age, following distinct linear trajectories; and (3) only the intrinsic driving factor significantly predicted figural divergent thinking in the structural model. While an emergent three-factor structure appeared in cross-sectional data, the longitudinal and predictive findings primarily support the stability and relevance of the core socio-motivational components. Teacher-observed personality tendencies are relevant to early figural divergent thinking but should not be interpreted as evidence for creativity as a whole. These results suggest the relevance of intrinsic driving tendencies to preschoolers’ figural divergent thinking. Full article

This study examines the implementation of an educational workshop on AI-mediated generative art, entitled Programmed Emotions, developed with primary school students. The research aims to explore the potential of generative technologies as tools for emotional expression and creativity, as well as to analyse their implications for students’ reflective processes. A mixed-methods approach was employed, combining quantitative data from self-assessment scales with qualitative data from open-ended responses and image analysis. The study investigated how the use of generative tools influenced engagement, emotional expression, creativity, and reflection. The results revealed high levels of active participation and creative production, together with a clear predominance of positive emotions, particularly joy, and a strong sense of identification between students and their artworks. However, the findings also highlighted significant limitations in reflective depth, characterized by brief responses and limited conceptual elaboration. These results suggest that, although generative art can effectively promote emotional expression and creativity, it does not by itself ensure deeper processes of understanding. Overall, the study underscores the educational potential of generative art in early educational stages while emphasizing the need for explicit pedagogical mediation to foster critical thinking, emotional diversity, and meaningful reflection. Full article

The internal saboteur may be understood as a multidimensional configuration of maladaptive inner processes involving recurrent negative self-evaluation, distressing relational expectations, repetitive negative thinking, and self-undermining inner experiences. Within this framework, the present study aimed to develop and examine the psychometric properties of the Internal Saboteur Scale (ISS), a self-report measure designed to assess this construct. A sample of 328 Italian adults (women 71.6%; Mage = 37.37, SD = 14.88) completed the survey. Confirmatory factor analyses supported both an eight-factor correlational model and a theoretically meaningful higher-order model, in which the lower-order dimensions were grouped into four broader domains: Negative Relational Expectations (Expected Rejection; Expected Judgment), Self-Devaluation (Negative Self-Appraisal; Interpersonal Unworthiness), Rumination (Retrospective Rumination; Anticipatory Rumination), and Internal Destructiveness (Helplessness; Defensive Relational Withdrawal). Measurement invariance across gender was also supported. All dimensions showed satisfactory-to-good internal consistency. Furthermore, ISS scores were negatively associated with secure attachment, self-reassurance, and mentalizing and positively associated with insecure attachment, self-criticism, shame, and anger. Overall, the ISS appears to be a theoretically grounded and psychometrically promising instrument for the assessment of maladaptive inner dialogue and self-sabotaging internal processes. It may represent a useful tool for both research and clinical practice, particularly in supporting transdiagnostic assessment and case formulation. Full article

Recent evidence indicates that many high school students continue to exhibit weak problem-solving skills and incomplete conceptual understanding of environmental change, although both are critical for addressing increasingly complex ecological challenges in the 21st century. This study aims to evaluate the effectiveness of the INSPIRE learning model in enhancing problem-solving skills and conceptual mastery of environmental change among high school students. The INSPIRE model integrates e-modules, guided inquiry-based learning (GIBL), and deep learning principles into a cohesive instructional framework. A quasi-experimental design with a non-equivalent control group pretest-posttest approach was employed, involving 108 Grade 10 students in three groups: an experimental group applying the INSPIRE model, control group 1 utilising discovery learning, and control group 2 implementing guided inquiry without e-modules. Data were collected through pretests and posttests assessing problem-solving skills and conceptual mastery of environmental change, complemented by observations of the learning implementation. The results showed that students in the INSPIRE group obtained higher mean posttest scores in both problem-solving skills and concept mastery than students in the two control groups, with statistically significant differences (p < 0.05). The findings suggest that the integration of e-modules, GIBL, and deep learning principles within the INSPIRE learning model supports a more meaningful learning experience and fosters 21st-century competencies, particularly problem solving in an environmental context. Practically, the INSPIRE learning model represents a promising option for biology instruction, especially for topics that require analytical thinking, investigation, and deep conceptual understanding. Full article

Prospection bias (PB) plays a crucial role in the development and maintenance of depression. However, the symptom-level mechanisms linking specific depressive symptoms to distinct PB components remain unclear, limiting the precision of targeted interventions. This study employed a network analysis approach to elucidate the intricate symptom-level relationships between depression and PB in a large sample of 1162 university students (453 males and 709 females; mean age = 19.47 years, SD = 1.45). Participants completed self-report assessments of depressive symptoms and prospection bias. The network demonstrated high stability. Central symptoms within the depression–PB framework were identified as “suicidal ideation”, “blurry future imagery”, “lack of energy”, and “catastrophic negative future thinking”. Critically, “suicidal ideation”, “future imaginations of self-harm/suicide”, and “guilt” served as the pivotal bridge symptoms connecting depression and PB. The Network Comparison Test revealed no significant gender differences in network structure. These findings highlight that current suicidal ideation and future-oriented suicidal imagery may serve as key nodes in the network of depressive symptoms and prospection bias. The results may help inform early identification and targeted intervention among at-risk university students. Full article