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Comparing the Value of Perceived Human versus AI-Generated Empathy
No full textArtificial Intelligence (AI), and specifically large language models, demonstrate remarkable social-emotional abilities, which may improve human-AI interactions and AI’s emotional support capabilities. However, it remains unclear whether empathy, encompassing understanding, ’feeling with’, and caring, is perceived differently when attributed to AI versus humans. We conducted nine studies (N = 6,282) where AI-generated empathic responses to participants’ emotional situations were labeled as either provided by humans or AI. Human-attributed responses were rated as more empathic and supportive, and elicited more positive and fewer negative emotions, compared to AI-attributed ones. Moreover, participants own uninstructed belief that AI aided human-attributed responses, reduced perceived empathy and support. These effects replicated across varying response lengths, delays, iterations and LLMs, being primarily driven by responses emphasizing emotional sharing and care. Additionally, people consistently chose human interaction over AI when seeking emotional engagement. These findings advance our general understanding of empathy, and specifically human–AI empathic interactions
EVALUACIÓN DE EVIDENCIAS MEDIANTE INDICADORES CLAVE EN INVESTIGACIÓN BASADA EN DISEÑO : ESTUDIO DE CASO EN PROYECTO ERASMUS+ PARA EDUCACIÓN SECUNDARIA
No full textEn este trabajo se instrumentaliza un proyecto Erasmus+ desarrollado en un centro de educación secundaria de Alicante (España), vinculado a las alianzas de innovación promovidas por las Acciones Klave 2 (KA201). Utiliza el método de estudio de caso, ex post facto con observación participante bajo el paraguas metodológico de investigación basada en diseño (IBD). Está enmarcado en un proyecto de investigación en cuyas etapas previas se obtuvieron resultados en forma de indicadores clave de diseño (KDIs) junto con indicadores clave de aprendizaje (KLIs), instrumentos formulados con el propósito de ser utilizados en la fase de evaluación de los diseños implementados en este proyecto de innovación desde los planteamientos y objetivos iniciales extraídos en la fase de análisis de contexto. Se persigue una aproximación metrológica que permita realizar una evaluación cualitativa y cuantitativa bidimensional tanto de los componentes de diseño como de los objetivos de aprendizaje que estos persiguen. Los resultados generan un instrumento de validación matricial cualitativa, desde el enfoque GQM (Goal-Question-Metric), y dos fórmulas de agregación cuantitativa para ambas dimensiones (KDIs y KLIs) aplicables a las evidencias en soporte digital generadas por el proyecto. Se extraen de este estudio de caso diferentes conclusiones orientadas a enriquecer los principios de diseño en la IBD para proyectos de innovación educativa mediados por tecnologías desde un enfoque basado en métricas estructuradas y sistematizadas de forma que permitan validar las estrategias diseñadas desde las múltiples evidencias de aprendizaje conservadas en el proyecto y desde diferentes dimensiones
Basic Psychological Need Satisfaction and Autonomous Motivation in Coach-Beginning Teacher Dyads: An Actor-Partner Interdependence Model Perspective
No full textWhen Witches Can & Cannot Fly on Broomsticks
No full textExperimental materials, data files and statistical analyses for Witch experiment
20240824-Semantic alignment: A measure to quantify the degree of semantic equivalence for English-Chinese translation equivalents based on distributional semantics
No full textAbstract
The degree of semantic equivalence of a translation pair is typically measured by asking bilinguals to rate the semantic similarity of translation pairs or comparing the number and meaning of dictionary entries. Such measures are subjective, labor-intensive, and unable to capture the fine-grained variation in the degree of semantic equivalence. Thompson et al. (2020) propose a computational method to quantify the extent to which translation equivalents are semantically aligned by measuring the contextual use across languages. Here, we refine this method to quantify semantic alignment of English-Chinese translation equivalents using word2vec based on the proposal that the degree of similarity between the contexts associated with a word and those of its multiple translations vary continuously. We validate our measure using semantic alignment from GloVe and fastText, and data from two behavioral datasets. The consistency of semantic alignment induced across different models confirms the robustness of our method. We demonstrate that semantic alignment not only reflects human semantic similarity judgement of translation equivalents but also captures bilinguals’ usage frequency of translations. We also show that our method is more cognitively-plausible than Thompson et al.’s method. Furthermore, the correlations between semantic alignment and key psycholinguistic factors mirror those between human-rated semantic similarity and these variables, indicating that computed semantic alignment reflects the degree of semantic overlap of translation equivalents in the bilingual mental lexicon. We further provide the largest English-Chinese translation equivalent dataset, encompassing 50,088 translation pairs for 15,734 English words, their dominant Chinese translation equivalents, and their semantic alignment Rc values.
Keywords: Semantic alignment · Semantic equivalence · Translation equivalents · Distributional semantics · Bilingual mental lexico
The Protective Role of School Belonging for Behavioral Problems in Elementary Students
No full textBehavioral problems in elementary students are a significant concern. We investigated the protective role of school belonging against these problems and examined the underlying mechanisms from an attachment theory perspective. A two-wave longitudinal study was conducted with 393 Chinese elementary students (mean age = 11.01, 44.8% girls) over a one-year interval. A cross-lagged path model was used to examine the reciprocal relationship between school belonging and behavioral problems, and a serial mediation model tested the roles of cognitive reappraisal and rejection sensitivity. Results from the cross-lagged analysis revealed that Time 1 (T1) school belonging significantly and negatively predicted Time 2 (T2) behavioral problems, whereas the reverse path was non-significant. Mediation analysis further indicated that the relationship between T1 school belonging and T2 behavioral problems was serially mediated by T1 cognitive reappraisal and T2 rejection sensitivity. These findings emphasize the importance of creating supportive school environments to promote positive social and emotional development in children
Data Collection and Measurement of Mental Health and Wellbeing of Adolescents with SEN Within Special Schools and Alternative Provision Settings: A Scoping Review Protocol
No full textObjective: This scoping review will explore the factors associated with school-based data collection and measurement of mental health and wellbeing of adolescents with special educational needs (SEN) within special schools and alternative provision (AP) settings.
Introduction: There are over 160,000 children and young people (CYP) in special schools and AP settings in the UK, most of whom have an SEN status. CYP with SEN are at risk of worse mental health and wellbeing, and schools are increasingly trying to identify these pupils to improve support. However, there are challenges in school-based measurement of mental health and wellbeing problems for schools and for researchers, such as staff time and training, competing school demands, and a lack of validated mental health and wellbeing measures for use with young people with SEN. This review aims to explore these challenges.
Inclusion criteria: This scoping review will include studies from 2014-2024, published in English. Studies will be with secondary-school aged young people with SEN, within special schools or AP settings. Studies need to reference school-based measurement of mental health and wellbeing; for example, studies assessing validity and reliability of mental health and wellbeing measures for this population. However, measures used in a clinical setting exclusively, or studies discussing an intervention, will not be included.
Methods: A literature search of key words in titles and abstracts will be carried out on PubMed, PsycINFO (via OVID), ERIC (via ProQuest) and Web of Science. Additionally, a subject heading search will be carried out on PsycINFO. The reference lists of identified papers will be hand-searched for additional sources. Additional papers identified via this method will also have the reference list examined. Finally, a hand search for grey literature will be carried out
