Search Results (86)

This article examines how kindness can be understood, expressed and enacted through intercultural citizenship education in higher education, with particular attention to equity, diversity and inclusion (EDI). Situated within a theoretical framework that brings together intercultural citizenship and EDI, the study argues that these fields are mutually reinforcing and that their integration is enriched by foregrounding kindness. Empirically, the article reports on a qualitative multiple case study conducted in 2023, involving university students from Argentina and the United Kingdom who collaboratively designed English language teaching materials focused on kindness. Data consisted of student-generated textual and artistic artefacts, including lesson plans, teachers’ notes, drawings, comics and other teaching materials, which were analysed using a multimodal approach. Across cases, kindness functioned as a relational disposition, ethical compass, emotional anchor and intentional action, serving as a pedagogical response to issues of gender inequality, mental health and disability inclusion. The study argues that a structured grammar of intercultural kindness offers a vocabulary that makes visible the relational, ethical, emotional and action-oriented dimensions through which kindness shapes the pedagogical enactment of intercultural citizenship and EDI. This approach demonstrates that kindness can be taught; however, its transformative potential depends on a deliberate political orientation. Full article

The peak–end rule proposes that retrospective evaluations depend on the emotional peak and the end of an experience rather than on its duration. Two short, controlled vocabulary-learning experiments tested whether optimizing these moments improves retrospective emotional valence. Study 1 (N = 32) manipulated task length (4 vs. 8 words). Retrospective emotional valence did not differ significantly between groups (p = 0.459, d = 0.27), a result consistent with duration neglect under this short task–episode manipulation but not a strong test of pure temporal duration neglect. Retrospective emotional valence correlated more strongly with the peak–end mean than with the mean of reconstructed page-level ratings (r = 0.761 vs. r = 0.314; Steiger’s Z = 3.03, p = 0.002). Study 2 (N = 56) used a 2 × 2 design to optimize the candidate peak-related completion page and the structurally defined end check-in page through color and anthropomorphic graphics. Both peak (ηp² = 0.18) and end (ηp² = 0.22) optimization enhanced retrospective emotional valence, with a significant non-additive interaction (ηp² = 0.09): the effect of optimizing one node was reduced when the other node had already been optimized. For learning accuracy, the main effect of peak optimization was significant (F(1, 52) = 4.44, p = 0.040), but only the combined peak-and-end optimization significantly outperformed the control condition (p = 0.041, d = 1.11); neither single-optimization condition significantly differed from the control condition after correction. The findings provide preliminary evidence for a peak–end-consistent evaluation pattern in brief, controlled vocabulary-learning tasks, identify a non-additive interaction in peak–end optimization, and offer guidance for designing key interactive moments within similarly short, task-based learning episodes. Full article

Research on language teacher emotional labour has expanded substantially, and recent scholarship includes both psychological and sociopolitical accounts of teachers’ emotional lives. However, within the 2020–2025 literature mapped in this review, leadership and institutional conditions are not always foregrounded with the same analytical specificity as teacher-focused constructs such as burnout, resilience or regulation. To examine this pattern, the study combines bibliometric mapping of 103 records with a focused interpretive thematic synthesis of 14 studies situated in or directly relevant to secondary school contexts. The bibliometric results show a field organised mainly around emotional labour, burnout, identity and teacher psychology vocabularies, with leadership and policy terms less prominent at the keyword level. The thematic synthesis identifies three recurrent school-level mechanisms through which emotional labour is discussed: accountability and governance, micropolitical recognition and exclusion, and support arrangements that shape how emotional burdens are shared or individualised. Across the 14 studies, non-native English-speaking teacher (NNEST) positioning and racialisation are most visible in studies that explicitly foreground legitimacy and marginalisation, but less visible in more generic studies of support or accountability. The review concludes that future research and practice would benefit from more clearly specifying institutional mechanisms and examining how secondary school conditions and NNEST positioning shape teachers’ emotional labour. Full article

This study examines how levels of emotional awareness are linguistically realized in preservice EFL teachers’ reflective discourse in a foreign language following simulation-based learning (SBL). The data consist of nine semi-structured interviews conducted in English approximately one month after an intercultural simulation workshop. Emotional awareness was assessed using the Levels of Emotional Awareness Scale (LEAS), while linguistic realization was analyzed through an emotionally colored language perspective and a Systemic Functional Linguistics framework. The findings reveal three developmental profiles. Higher emotional awareness was associated with richer emotionally colored language and more frequent hypotactic structures, enabling participants to articulate complex and sometimes conflicting emotional perspectives. Intermediate levels showed more balanced clause organization and greater reliance on repetition as an intensification strategy, reflecting a transitional stage in which the ability to articulate emotionally complex professional experiences is still emerging. Lower levels were characterized by limited emotional vocabulary, frequent repetition, and reduced hypotaxis, indicating an initial stage in which the discursive repertoire has not yet developed. Overall, the findings suggest that emotional awareness and its linguistic realization develop in tandem, and the analysis of these patterns offers insight into preservice teachers’ evolving capacity to process emotionally complex professional experiences in a foreign language. Full article

As artificial intelligence systems proliferate across critical societal domains, understanding the nature, patterns, and evolution of AI-related harms has become essential for effective governance. Despite growing incident repositories, systematic computational analysis of AI incident discourse remains limited, with prior research constrained by small samples, single-method approaches, and absence of temporal analysis spanning major capability advances. This study addresses these gaps through a comprehensive multi-method text analysis of 3494 AI incident records from the OECD AI Policy Observatory, spanning January 2014 through October 2024. Six complementary analytical approaches were applied: Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) topic modeling to discover thematic structures; K-Means and BERTopic clustering for pattern identification; VADER sentiment analysis for emotional framing assessment; and LIWC psycholinguistic profiling for cognitive and communicative dimension analysis. Cross-method comparison quantified categorization robustness across all four clustering and topic modeling approaches. Key findings reveal dramatic temporal shifts and systematic risk patterns. Incident reporting increased 4.6-fold following ChatGPT’s (5.2) November 2022 release (from 12.0 to 95.9 monthly incidents), accompanied by vocabulary transformation from embodied AI terminology (facial recognition, autonomous vehicles) toward generative AI discourse (ChatGPT, hallucination, jailbreak). Six robust thematic categories emerged consistently across methods: autonomous vehicles (84–89% cross-method alignment), facial recognition (66–68%), deepfakes, ChatGPT/generative AI, social media platforms, and algorithmic bias. Risk concentration is pronounced: 49.7% of incidents fall within two harm categories (system safety 29.1%, physical harms 20.6%); private sector actors account for 70.3%; and 48% occur in the United States. Sentiment analysis reveals physical safety incidents receive notably negative framing (autonomous vehicles: −0.077; child safety: −0.326), while policy and generative AI coverage trend positive (+0.586 to +0.633). These findings have direct governance implications. The thematic concentration supports sector-specific regulatory frameworks—mandatory audit trails for hiring algorithms, simulation testing for autonomous vehicles, transparency requirements for recommender systems, accuracy standards for facial recognition, and output labeling for generative AI. Cross-method validation demonstrates which incident categories are robust enough for standardized regulatory classification versus those requiring context-dependent treatment. The rapid emergence of generative AI incidents underscores the need for governance mechanisms responsive to capability advances within months rather than years. Full article

Adolescence is a critical period when rapid cognitive maturation coincides with heightened emotional vulnerability. This study examined the dynamic association between academic ability and mental health across early adolescence, focusing on vocabulary ability as a core indicator of academic ability. Using large-scale data from Grades 1–12 (N = 13,412), a vertically scaled vocabulary ability scale was constructed based on Item Response Theory (IRT) and the Non-Equivalent Anchor Test (NEAT) design to achieve cross-grade comparability. Fixed-parameter calibration was then applied to an independent cross-sectional sample of middle school students (Grades 7–9, N = 401) in Tianjin, combined with the DASS-21 to assess internalizing symptoms (depression, anxiety, stress). Hierarchical multiple regression analyses revealed that higher vocabulary ability was significantly associated with lower levels of depression, anxiety, and stress, with the negative association strongest in Grade 8. The present study provides new empirical evidence for understanding the interactive mechanisms between academic and psychological development during adolescence. Methodologically, the study demonstrates the value of IRT-based vertical scaling in establishing developmentally interpretable metrics for educational and psychological assessment. Full article

This research addresses a critical gap in the Public–Private Partnership (PPP) research field by analysing risk allocation in an emergent African context: Morocco. Based on semi-structured interviews with six selected practitioners, along with lexicometric and sentiment analysis, this study identifies the major risks and the determinants influencing their allocation. Findings show a risk profile dominated by commercial, political, and industrial uncertainties. In addition, the research uncovers that risk allocation is not simply a technical task, but a multidimensional negotiation influenced by project characteristics, partner capabilities, macro-environmental imperatives, and transaction dynamics. Moreover, sentiment analysis reveals a vocabulary mainly reflecting the emotions of fear, anticipation, and trust, which points to the affective side of the contract. This study provides a qualitative framework that is sensitive to the context and that challenges standard economic models; it gives clear directions to policymakers handling complicated PPP arrangements in emerging markets. Full article

Vegetable consumption in childhood remains below recommendations worldwide, particularly in disadvantaged socioeconomic groups. Building on prior work showing no socioeconomic status (SES) differences in children’s liking of familiar vegetables, this study examined whether their sensory and emotional descriptions vary by SES and how these relate to liking beyond hedonic ratings. A total of 363 Chilean fourth graders (9–10 years) from five SES groups evaluated eight vegetables at school. For each sample, children rated overall liking (7-point facial hedonic scale) and completed two CATA (Check-All-That-Apply) tasks: a child-derived sensory list (13 terms) and a validated emoji-based emotion list (33 items). Data were analyzed using Cochran’s Q tests, correspondence analyses, and mean-impact analyses. The use and diversity of sensory and emotional descriptors differed significantly between socioeconomic groups (p < 0.05): children from higher SES levels employed a broader and more differentiated vocabulary, while those from lower SES backgrounds used fewer significant terms. Across the sample, juicy, fresh, and mild flavors increased liking, whereas strong aroma decreased it (p < 0.05); positive emojis increased liking, whereas negative and neutral ones had no effect. These findings reveal that perceptual and affective representations are socially patterned, underscoring the need to foster sensory–affective literacy in lower-SES contexts. Full article

Multi-object tracking (MOT) technology integrates multiple fields such as pattern recognition, machine learning, and object detection, demonstrating broad application potential in scenarios like low-altitude logistics delivery, urban security, autonomous driving, and intelligent navigation. However, in open-world scenarios, existing MOT methods often face challenges of imprecise target category identification and insufficient tracking accuracy, especially when dealing with numerous target types affected by occlusion and deformation. To address this, we propose a multi-object tracking strategy based on multi-cue fusion. This strategy combines appearance features and spatial feature information, employing BYTE and weighted Intersection over Union (IoU) modules to handle target association, thereby improving tracking accuracy. Furthermore, to tackle the challenge of large vocabularies in open-world scenarios, we introduce an open-vocabulary prompting strategy. By incorporating diverse sentence structures, emotional elements, and image quality descriptions, the expressiveness of text descriptions is enhanced. Combined with the CLIP model, this strategy significantly improves the recognition capability for novel category targets without requiring model retraining. Experimental results on the public TAO benchmark show that our method yields consistent TETA improvements over existing open-vocabulary trackers, with gains of 10% and 16% on base and novel categories, respectively. The results demonstrate that the proposed framework offers a more robust solution for open-vocabulary multi-object tracking in complex environments. Full article

The accelerated loss of biodiversity and the limited integration of ocean literacy into school curricula highlight the urgent need for innovative approaches in Environmental Education for Sustainability (EES). This study presents the design and classroom implementation of Marine Dobble, a gamified educational activity inspired by the popular card game Dobble®, adapted with illustrations of marine species from the Andalusian coast (Spain). The objective was to explore the feasibility of this tool to foster knowledge, awareness, and commitment toward marine biodiversity conservation among secondary school students. The intervention was carried out in five 1st-year ESO classes (n = 110, ages 12–13) in Cádiz, Spain, during a one-hour workshop facilitated by an environmental educator. A qualitative exploratory design was employed, using group-level observation notes to document participation, reactions, and emergent learning evidence. The activity combined fast-paced gameplay with five reflective pauses addressing key topics: marine habitats, species adaptations, scientific curiosities, environmental problems, and personal commitment. Findings indicate high levels of engagement and participation, with frequent emotional and cognitive responses to novel content such as the ecological role of microalgae and the existence of marine plants. Students progressively incorporated scientific vocabulary and proposed actions for ocean conservation, including reducing plastic waste and promoting sustainable consumption. Differences among groups underscored the relevance of teacher involvement and classroom context for implementation success. Overall, the study suggests that contextualized gamification combined with reflective dialogue is a feasible and promising approach to integrate ocean literacy into secondary education. Full article

Early parent–child interactions are crucial for children’s social–emotional development. Mental state talk (MST)—language referring to thoughts, feelings, and intentions—is a key contributor. MST may be reduced in hearing parents of deaf or hard-of-hearing (DHH) children, who face unique communication challenges. Yet, existing research on MST in hearing parents of DHH children and on MST use by DHH children themselves is limited and fragmented, often focusing on MST quantity in a single context. Few studies have examined MST quality, variation across contexts, or its relationship with children’s social–emotional functioning. This study addresses these gaps by investigating MST quantity and quality across two activities and its associations with children’s MST and social–emotional development. Forty-eight hearing parent–DHH child dyads (ages 2–5) participated. MST was studied during storybook reading and free play. Children completed tasks on emotion understanding and theory of mind; parents reported on MS vocabulary and family characteristics. The results showed that parents adjusted MST complexity based on children’s age but not audiological characteristics. MST varied by activity, with different contexts eliciting distinct types of MST. Parental and child MST were associated, though not linked to children’s task performance. Findings highlight the importance of diverse interaction contexts and suggest a need for longitudinal research on MST’s developmental impact. Full article

Developmental language disorder (DLD) is associated with persistent language difficulties that may impact social competence. The aim of this study is to describe the relationship between language, pragmatics, and social competence in French-speaking preschoolers and to identify the specific social competence difficulties observed in children at risk of DLD at this age. The sample included 63 children aged between 36 and 59 months, 12 of whom were at risk of having DLD. Children were assessed using measures of vocabulary, morphosyntax, pragmatic skills, and narrative abilities, while childcare educators completed a questionnaire evaluating social competence. Results revealed that children at risk for DLD exhibited more characteristics related to dependence on adults compared to their peers without DLD. No significant group differences were observed for the other components of social competence. The findings also identified a relationship between pragmatic and personal narrative skills, and social adjustment. These findings support the social adaptation model, suggesting that functional social impacts in children with DLD may arise from limited language abilities rather than an intrinsic socio-emotional disorder. This study highlights the importance of early pragmatic and narrative development in supporting social competence from the preschool age. Full article

This study examines the performative, spatial, and affective dimensions of Marian devotion in two early modern women’s prayerbooks from Vorarlberg: the Valduna Prayerbook (Freiburg i. Br., UB Hs. 1500,30) and the Thalbach Prayerbook (Bregenz, VLB Hs. 17). Both manuscripts demonstrate that prayer was not a purely mental act but a choreographed devotional performance shaped by posture, gesture, and gaze. Rubrics repeatedly direct the devotee to pray before an image of the Virgin, transforming the image into a locus of embodied interaction that engaged sight, movement, and emotion. Analysis of sixteen such image-based prayers reveals how spatial instructions, somatic cues, and affective language converge to produce a physically enacted piety. Quantitative assessment of affective vocabulary shows that gaze-based prayers concentrate emotional language—especially of joy, sorrow, and distress—at twice the density of other texts in the same manuscripts, underscoring their heightened emotional charge. These prayerbooks thus construct a devotional choreography in which the devotee’s body becomes both instrument and interpreter of spiritual meaning. By situating image, word, and motion within the convent environment, the study reveals how female religious communities enacted Marian devotion as lived performance, where space, gesture, and affect generated spiritual presence. Full article

Background: Speech and Language Impairment (SLI) significantly affects children’s communication skills, limiting their social and academic development. Case Information: This single-case study evaluates the effects of a personalized intervention in a 9-year-old child diagnosed with SLI, integrating linguistic and cognitive strategies to remediate core deficits typically observed in children with SLI. Two main objectives were established: (1) to assess the child’s psycholinguistic competencies and cognitive processes and (2) to analyze the impact of the intervention on skills such as phonology, semantics, syntax, executive functions, and emotional well-being. The longitudinal and personalized design included pre- and post-intervention assessments conducted over two and a half years using tools such as the ITPA and Peabody Vocabulary Test. The intervention sessions were structured into linguistic and cognitive activities, with a frequency of two weekly language sessions and one cognitive functions session. Statistical analysis included ANOVA to evaluate significant changes. Conclusions: The results showed significant improvements in linguistic areas such as auditory comprehension (from 3–5 to 10 years) and verbal expression (from 5–10 to 9–6), as well as in cognitive aspects such as visuomotor sequential memory and visual comprehension, which exceeded the expected values for the child’s age. However, skills such as grammatical integration and auditory association did not show significant progress. This demonstrates that personalized and multidisciplinary interventions can considerably improve linguistic and cognitive abilities in children with SLI, although some areas require more specific approaches. The findings highlight implications for designing tailored intervention strategies, emphasizing the need for further research with larger samples and control groups to generalize the results. This case reaffirms the importance of comprehensive approaches in the treatment of SLI to maximize the academic and social development of affected children. Full article

Multimodal Sentiment Analysis (MSA) aims to predict subjective human emotions by leveraging multimodal information. However, existing research inadequately utilizes explicit sentiment semantic information at the lexical level in text and overlooks noise interference from non-dominant modalities, such as irrelevant movements in visual modalities and background noise in audio modalities. To address this issue, we propose a multimodal sentiment analysis model based on knowledge enhancement and text-guided learning (MMKT). The model constructs a sentiment knowledge graph for the textual modality using the SenticNet knowledge base. This graph directly annotates word-level sentiment polarity, strengthening the model’s understanding of emotional vocabulary. Furthermore, global sentiment knowledge features are generated through graph embedding computations to enhance the multimodal fusion process. Simultaneously, a dynamic text-guided learning approach is introduced, which dynamically leverages multi-scale textual features to actively suppress redundant or conflicting information in visual and audio modalities, thereby generating purer cross-modal representations. Finally, concatenated textual features, cross-modal features, and knowledge features are utilized for sentiment prediction. Experimental results on the CMU-MOSEI and Twitter2019 dataset demonstrate the superior performance of the MMKT model. Full article