Journal of Intelligence最新文献

Individuals with mild nonspecific intellectual disability (NSID) often exhibit delayed literacy development. Unfortunately, how cognitive-linguistic processing profiles influence literacy in this population lacks clarity. This study investigated literacy development in this population, considering the cognitive-linguistic mechanisms. The Specialist Battery for the Diagnosis of Cognitive Abilities and School Skills was used to assess cognitive-linguistic abilities and literacy-related skills in 122 participants. Fuzzy C-means clustering was used to identify processing profiles. Developmental age equivalents in literacy were estimated using local regression models and matched comparisons with typically developing peers. Two cognitive-linguistic profiles emerged: globally weaker and moderately developed. Those with NSID performed significantly lower than their peers in all domains. Their literacy skills aligned with those of children 2-4 years younger, and plateaued after age 15. Cognitive-linguistic heterogeneity in students with NSID should guide targeted literacy interventions. The findings inform ICD-11 educational expectations for individuals with mild NSID.

The widespread application of Generative Artificial Intelligence (Gen-AI) is transforming educational practices and driving pedagogical innovation. While cultivating higher-order thinking (HOT) represents a central educational goal, its achievement remains an ongoing challenge. Current evidence regarding the impact of Gen-AI on HOT is relatively fragmented, lacking systematic integration, particularly in the analysis of moderating variables. To address this gap, a meta-analysis approach was employed, integrating data from 29 experimental and quasi-experimental studies to quantitatively assess the overall impact of Gen-AI on learners' HOT and to examine potential moderating factors. The analysis revealed that Gen-AI exerts a moderate positive effect on HOT, with the most significant improvement observed in problem-solving abilities, followed by critical thinking, while its effect on creativity is relatively limited. Moderation analyses further indicated that the impact of Gen-AI is significantly influenced by experimental duration and learners' self-regulated learning (SRL) abilities: effects were strongest when interventions lasted 8-16 weeks, and learners with higher SRL capacities benefited more substantially. Based on the research findings, this study proposed that Gen-AI should be systematically integrated as a targeted instructional tool to foster HOT. Medium- to long-term interventions (8-16 weeks) are recommended to enhance learners' problem-solving and critical thinking abilities. At the same time, effective approaches should also be explored to promote creative thinking through Gen-AI within existing pedagogical frameworks. Furthermore, individual learner differences should be accounted for by adopting dynamic and personalized scaffolding strategies to foster SRL, thereby maximizing the educational potential of Gen-AI in cultivating innovative talents.

Conversational interactions, rich in both linguistic and vocal cues, provide a natural context for studying these processes. In this work, we propose an explainable multimodal transformer framework that integrates textual semantics (via RoBERTa) and acoustic prosody (via WavLM) to advance emotion understanding. By projecting both modalities into a shared latent space, our model captures the complementary contributions of language and speech to affective communication, achieving an 0.83 accuracy value across five emotion categories. Crucially, we embed explainable AI (XAI) techniques including Integrated Gradients and Occlusion to attribute predictions to specific linguistic tokens and prosodic patterns, thereby aligning computational mechanisms with human cognitive processes of emotion perception. Beyond performance gains, this work demonstrates how multimodal AI systems can support transparent, human-centered emotion recognition.

This study introduces and validates the construct of Teacher Emotional Commitment (CED), understood as the conative-behavioral dimension that characterizes the emotional bond that teachers establish with their students. To this end, two complementary studies were conducted in the Autonomous Community of the Canary Islands (Spain), with the aim of: to empirically isolate the factorial structure of CED and differentiating it from related constructs, such as empathy; to analyze its presence in both active teachers and those in initial training; and to test the theoretical model's validity by expanding the sample and enlarging the response scale. Study 1 involved 854 practicing teachers and 701 teachers in training, following a validation process that included exploratory and confirmatory factor analysis, as well as item response theory models. The results showed a four-factor structure: loving proactivity, teacher compassion, instructional commitment, and communicative affectivity, with adequate reliability and discriminant validity indices with respect to empathy. Study 2, with an expanded sample of 2096 participants, confirmed the robustness of the model. The findings allow us to consider CED as a psychological competence that can be trained, with relevant implications for improving the educational relationship, student learning, and the emotional well-being of teachers.

This study examined the psychometric properties of the Pre-Literacy Test, developed to measure the literacy readiness skills of children who have completed preschool education. Using a quantitative, multistage design, the study was conducted with a total of 5966 children aged 6-7 who were about to enter elementary school in the 2024-2025 academic year (N₁ = 1911; N₂ = 1644; N₃ = 2411). Exploratory Factor Analysis revealed a three-factor structure-Reading Skills, Writing Skills (Dictation), and Writing Skills (Copying)-which explained 82.38% of the total variance. Confirmatory Factor Analysis demonstrated that this structure showed an acceptable model fit (CFI = 0.997, TLI = 0.997, SRMR = 0.030, RMSEA = 0.111). The internal consistency coefficients (α = 0.891-0.962; ω = 0.912-0.983) and convergent validity values (AVE = 0.867-0.949) of the PLT were found to be high. Discriminant validity was confirmed according to the Fornell-Larcker criterion, and measurement invariance across gender was supported through Multigroup Confirmatory Factor Analysis. Item analyses indicated that most test items were of moderate difficulty (mean difficulty = 0.409) and high discrimination (mean discrimination = 0.516). In conclusion, the PLT was determined to be a psychometrically robust, valid, and reliable instrument for assessing basic literacy skills prior to elementary school entry. These findings suggest that the test can be confidently used in early literacy research and school readiness assessments.

This study reviews 33 meta-analyses and systematic reviews on Computational Thinking (CT), focusing on research quality, intervention effectiveness, and content. Quality assessment of included studies was conducted using the AMSTAR 2 tool. The meta-analysis achieved an average score of 10.9 (a total of 16 points), while systematic reviews scored an average of 6.1 (a total of 11 points). The 15 meta-analyses showed diverse intervention strategies. Project-based learning, text-based programming, and game-based learning demonstrate more pronounced effects in terms of effect size and practical outcomes. Curricular integration, robotics programming, and unplugged strategies offered additional value in certain contexts. Gender and disciplinary background were stable moderators, while grade level and educational stage had more conditional effects. Intervention duration, sample size, instructional tools, and assessment methods were also significant moderators in several studies. The 18 systematic reviews used a five-layer framework based on ecological systems theory, covering educational context (microsystem), tools and strategies (mesosystem), social support (exosystem), macro-level characteristics (macrosystem), and CT development (chronosystem). Future research should focus on standardizing meta-analyses, unifying effect size indicators, and strengthening longitudinal studies with cognitive network analysis. Additionally, systematic reviews should improve evidence credibility by integrating textual synthesis and data-driven reasoning to reduce redundancy and homogeneity.

This theoretical paper introduces the Verbal-Cognitive Scaffold (VCS) Model, a cognitively inclusive framework which proposes the cognitive architectures underlying computational thinking (CT). Moving beyond monolithic theories of cognition (e.g., executive-function and metacognitive control models), the VCS Model posits inner speech (InSp) as the predominant cognitive pathway supporting CT operations in neurotypical populations. Synthesizing interdisciplinary scholarship across cognitive science, computational theory, neurodiversity research, and others, this framework articulates distinct mechanisms through which InSp supports CT. The model specifies four primary pathways linking InSp to CT components: verbal working memory supporting decomposition, symbolic representation facilitating pattern recognition and abstraction, sequential processing enabling algorithmic thinking, and dialogic self-questioning enhancing debugging processes. Crucially, the model posits these verbally mediated pathways as modal rather than universal. Although non-verbal architectures are acknowledged as possible alternative routes, their precise mechanisms remain underspecified in the existing literature and, therefore, are not the focus of the current theoretical exploration. Given this context, this manuscript focuses on the well-documented verbal support provided by InSp. The VCS Model's theoretical contributions include the following: (1) specification of nuanced cognitive support systems where distinct InSp functions selectively enable particular CT operations; (2) generation of empirically testable predictions regarding aptitude-pathway interactions in computational training and performance; and (3) compatibility with future empirical efforts to inquire into neurodivergent strategies that may diverge from verbal architectures, while acknowledging that these alternatives remain underexplored. Individual variations in InSp phenomenology are theorized to predict distinctive patterns of CT engagement. This comprehensive framework, thus, elaborates and extends existing verbal mediation theories by specifying how InSp supports and enables CT, while laying the groundwork for possible future inquiry into alternative, non-verbal cognitive pathways.

Standardized cognitive assessments are essential in research but often limited by proprietary restrictions and methodological constraints. This study evaluates the psychometric properties of two public-domain International Cognitive Ability Resource (ICAR) measures implemented in the Mobile Toolbox (MTB) assessment library: Puzzle Completion and Block Rotation. Using a sample of 100 adults (18-82 years), we assessed internal consistency, test-retest reliability, and construct validity compared to gold-standard measures. Results demonstrated acceptable reliability for both Puzzle Completion and Block Rotation. Each measure showed moderate to strong correlations with respective gold-standard assessments: Puzzle Completion correlated with Raven's Progressive Matrices (r = 0.40), and Block Rotation with Mental Rotation Test (r = 0.46). Practice effects were non-significant. Both demonstrated the ability to discriminate between verbal and nonverbal abilities. Findings were consistent with previous ICAR validations, suggesting MTB provides a viable option for remote self-administration while preserving measurement integrity. This enables larger sample collection and ecological assessment of cognitive abilities outside of laboratory settings.

Creativity is increasingly recognized as an essential 21st-century skill, critical for innovation, problem-solving, and personal growth. Educational systems have responded by prioritizing creative thinking, prompting researchers to explore the potential of Learning Analytics (LA) to support and enhance creativity. This systematic review synthesizes empirical studies, theoretical frameworks, and methodological innovations from databases such as Web of Science, Scopus, ERIC, ProQuest, and Google Scholar, examining how creativity is operationalized within LA contexts. The review identifies diverse assessment frameworks, encompassing divergent thinking tests, product-based evaluations, behavioral metrics, and process-oriented assessments, often underpinned by the "4 Ps of Creativity" framework (Person, Process, Product, Press). Tools such as automated scoring systems, multimodal analytics, and AI-enhanced assessments demonstrate the potential to objectively and reliably capture creative processes and outcomes. However, significant challenges remain, including definitional ambiguity, inconsistent metrics, scalability issues, and ethical concerns related to data privacy. This review underscores the transformative capacity of LA to foster creativity in education while highlighting the critical need for standardized, robust methodologies and inclusive frameworks. By addressing identified gaps, future research can advance innovative approaches to assess and cultivate creativity using LA.

Although numerous studies address inclusive education, especially in Latin America, research analyzing the overall life satisfaction of teachers in schools that implement inclusion policies are scarce. The purpose of this study is to analyze the relationship between Life Satisfaction, Optimism, Culture, and the Inclusive Practice of primary school teachers from Chile. A descriptive quantitative method was employed, with an ex post facto design including 246 primary teachers from urban and rural schools in Chile. The teachers completed four questionnaires: Inclusive Culture (IC), Inclusive Practice (IP) Satisfaction with Life Scale (SWSL), and Life Orientation Test Revised (LOT-R). The results show that these instruments present acceptable reliability. In addition, a significant correlation was found between Classroom Experience Time (CET) and SWSL (r = 0.201, p < .01), as well as between SWSL, and LOT-R (r = 0.411, p < .01), and IC and IP (r = 0.838, p < .01). The regression model is statistically significant [F (4, 241) = 139.572, p < .001]. The findings indicate that IC and SWSL predict IP directly, whereas CET is an inverse predictor. There is a statistically significant relationship between Life Satisfaction, Classroom Experience Time, Culture, and Inclusive Practice, with the three first variables being predictors of Inclusive Practice.