European Journal of Education最新文献

As artificial intelligence (AI) tools reshape second language (L2) writing, how technological and psychological factors jointly shape English-medium instruction (EMI) students' writing feedback literacy remains underexplored. Using an online questionnaire with 500 Chinese EMI university students, this study employed structural equation modelling (SEM) to examine how generative AI acceptance, writing anxiety and writing self-efficacy shape students' writing feedback literacy. The results showed that (1) generative AI acceptance positively and significantly predicts students' writing feedback literacy and writing self-efficacy, and negatively predicts writing anxiety; (2) writing self-efficacy positively mediated the relationship between generative AI acceptance and writing feedback literacy, while writing anxiety mediated the relationship by reducing negative emotions; and (3) generative AI acceptance improves writing feedback literacy by raising students' self-efficacy and lowering their writing anxiety. This study broadens understanding of writing feedback literacy in the AI era by clarifying the psychological mechanism linking generative AI acceptance to writing feedback literacy in EMI writing, and offers pedagogical implications for integrating AI-based support to improve EMI students' emotions and feedback engagement.

Teacher professional identity is essential to understanding teachers' professional growth, especially in the context of music education. This study adopts a person-centered approach to examine variations in professional identity among Chinese in-service music teachers, with a specific focus on how resilience serves as a differentiating factor. Drawing on survey data from 395 teachers across diverse educational institutions in China, the study employs latent profile analysis (LPA) and identifies three distinct identity profiles: Identity-Challenged, Developing Professionals, and Empowered Professionals. Subsequent multinomial logistic regression analysis reveals that teachers with higher levels of resilience are significantly more likely to belong to the Empowered group, suggesting that resilience is a critical psychological resource in shaping identity coherence. The findings highlight the heterogeneity of teacher identity development, particularly in emotionally demanding educational contexts like music education. This study contributes to teacher education research by highlighting the value of professional development that combines emotional support, resilience-building, and identity-focused reflection, which aims at fostering both the cognitive and emotional growth of teachers as they navigate complex role expectations and institutional pressures.

As technology continues to grow in significance and popularity, a noticeable gap persists in understanding its potential to support formative assessment in science, technology, engineering, and mathematics (STEM) classroom practices for K–12 students. This study systematically reviewed 39 empirical articles to identify the prerequisites that teachers should possess to use technology-supported formative assessment in STEM classrooms. This study conducted a systematic review, in which the educational settings, teaching subjects, research methodologies, technology types, and teacher prerequisites were coded and analysed. The review revealed that the number of articles focusing on junior high education exceeded those on primary and senior high education. Science and mathematics were the most frequently researched subjects, with very few studies exploring integrated STEM teaching. Qualitative and mixed-method approaches dominated the research methodologies. Both hardware, such as interactive whiteboards, and software tools, such as Math-Mapper, were identified as essential components facilitating formative assessments, with a trend toward their combined use to enhance classroom interaction and provide more effective student feedback. The findings also highlight 16 teacher prerequisites for effective technology-supported formative assessment in STEM education, which are organised into three overarching categories: knowledge and skill factors, social factors, and psychological factors, echoing classifications found in prior research. This review provides actionable insights and recommendations to inform STEM teacher professional development programs and contributes to the broader discourse on technology integration in STEM education.

This study developed and validated the Artificial Intelligence Pedagogical Content Knowledge (AIPACK) scale to assess the AI-related instructional knowledge of primary and secondary school teachers in Taiwan. The scale includes four dimensions: AI Knowledge (AIK), AI Content Knowledge (AICK), AI Pedagogical Knowledge (AIPK), and AI Pedagogical Content Knowledge (AIPACK). Valid responses from 200 participants were analysed using confirmatory factor analysis (CFA), resulting in a finalised 26-item scale with a four-factor structure. The CFA results indicated good model fit. All factor loadings exceeded 0.60, and both the average variance extracted (AVE) and composite reliability (CR) met the recommended thresholds, indicating strong construct validity and internal consistency. Measurement invariance was established across gender and teaching roles (preservice vs. in-service), enabling valid cross-group comparisons. The findings support the AIPACK scale's reliability and validity as a tool for evaluating teachers' AI-related instructional knowledge and offer a foundation for future research, training, and policy.

Early Childhood Music Education (ECME) has been recognised for its significant contributions not only to the development of musical abilities and creative thinking, but also to children's academic achievement, socio-emotional competence, and language acquisition. However, systematic reviews focusing specifically on music education within early childhood contexts remain scarce. To address this gap, this study conducted a bibliometric analysis of 643 publications using CiteSpace and VOSviewer. The analysis identified key contributors (e.g., Ilari, Beatriz; Trainor, Laurel J.; Bautista, Alfredo), high-output countries (e.g., the United States, China, Canada), leading institutions (e.g., McMaster University, University of Toronto, University of Southern California, and The Education University of Hong Kong), and core journals (e.g., Music Education Research, International Journal of Music Education, Journal of Research in Music Education). Four major thematic directions were extracted: perception of musical elements and neural plasticity, ECME curriculum development and teacher professionalism, music training and its effects on cognitive and language development, and children's behaviour and mental health. Additionally, burst term analysis revealed two emerging research frontiers: the impact of ECME on children's self-regulation and the integration of digital technologies to enhance teacher development. These findings offer a comprehensive overview of the field and provide valuable insights for future research and practice in ECME.

Despite growing interest in robotics-based education, limited research has examined how such learning environments influence both cognitive development and problem-solving skills in tertiary education, particularly regarding their interactive effects on student engagement. Addressing this gap, the present study investigates the impact of robotics classrooms on 508 college students' cognitive development, problem-solving abilities, and engagement with robotics activities. Data were collected through Wenjuanxing, an online platform, using a structured questionnaire comprising four sections: demographic information, the Cognitive Development Scale (CDS), the Problem-Solving Inventory (PSI), and the Robotics Engagement and Attitudes Questionnaire (REAQ). After data cleaning, analyses were conducted using SPSS (version 27) for descriptive statistics, correlations, and regression, and AMOS (version 24) for structural equation modelling (SEM). Results indicated a significant positive correlation among cognitive development, problem-solving skills, and robotics engagement (r = 0.52–0.68, p < 0.001). Cognitive development emerged as a moderate to strong predictor of robotics engagement (β = 0.47, p < 0.001), while problem-solving skills also served as a strong predictor (β = 0.51, p < 0.001). Together, these variables explained a substantial proportion of variance in robotics engagement (adjusted R² = 0.56). SEM analyses further revealed that problem-solving skills partially mediated the relationship between cognitive development and robotics engagement (indirect effect = 0.24, p < 0.001). These findings highlight the value of robotics-based education, providing practical guidance for instructors and curriculum designers to create learning experiences that foster higher-order thinking and strategic problem-solving, thereby enhancing technology-enhanced educational outcomes in higher education.

The use of cutting-edge technologies like artificial intelligence (AI) and robotics in language education has recently captured the attention of many scholars across the world. However, the potential and challenges of incorporating such technologies in Chinese as a second language (CSL) at the higher education level have been widely overlooked in the literature. Most of the studies in this area revolve around the English language. To address the gaps, this study adopted a qualitative study through a semi-structured interview, held in English, with a sample of 42 higher education Chinese teachers teaching CSL. The results of thematic analysis demonstrated that incorporating AI and robotics in CSL classes had been beneficial for ‘reducing teachers’ workload and burden’, ‘diversifying teaching resources and materials’, ‘personalizing and aligning teaching to students' needs and interests’ and ‘increasing teachers’ teaching efficacy’. Moreover, it was found that using these technologies produces some challenges for CSL teachers because they need ‘teacher training’ and ‘financial support and infrastructures' and reduces CSL students’ ‘creativity and critical thinking’ and ‘interpersonal communication and interaction’. The findings are discussed and implications at the theoretical and practical levels are presented to increase CSL teachers’ knowledge and skills pertaining to AI and robot-mediated language education in light of educational technology frameworks. Moreover, actionable recommendations for CSL professional development, policy enactment and resource allocation in technology-enhanced classrooms are provided.