European Journal of Education最新文献

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.

Business English is the established lingua franca of global commerce and a core academic discipline worldwide. Despite growing research interest in the field, it still lacks a comprehensive analysis of the latest developments from a global perspective, particularly regarding technological shifts and the post-pandemic context. This study aims to bridge this gap by employing bibliometric analysis with VOSviewer to map the knowledge domain and trace the development of business English research through an examination of 381 Scopus-indexed publications (2000–2025), including the various facets of analysis, design, development, implementation, and evaluation. Results show a consistent rise in global research output and a thematic evolution from traditional pedagogy to innovative approaches across three stages. The analysis identifies leading countries, authors, organisations, and sources, while text mining reveals current research hotspots. The study provides a comprehensive domain overview, concluding with implications and insights to guide future research and practice in the field.

Against the backdrop of education systems' digital transformation, a central question concerns how teachers' beliefs are translated into innovative classroom practice. Drawing on Value–Belief–Norm (VBN) theory, this study examines whether and how teachers' technology integration beliefs shape diversified student assessment practices through the serial mediating roles of digital teaching self-efficacy and technology integration practices. Using data from 1298 teachers in the Shanghai (China) sample of the Organisation for Economic Co-operation and Development (OECD) Teaching and Learning International Survey (TALIS) 2024, we conducted serial mediation analyses using PROCESS Model 6, with bootstrapped confidence intervals. The findings show that: (1) technology integration beliefs significantly and positively predict digital teaching self-efficacy, technology integration practices and diversified student assessment practices; (2) digital teaching self-efficacy and technology integration practices each exert significant independent mediating effects; and (3) they also operate sequentially, revealing a coherent mechanism through which beliefs are converted into professional behaviour. By validating the applicability of VBN theory to educational technology research, this study offers a theoretically grounded account of teachers' assessment innovation and provides implications for policy and practice: strengthening teachers' technology integration beliefs and enhancing digital teaching self-efficacy may be pivotal levers for advancing diversified assessment practices.

Artificial intelligence (AI) has transformed higher education. The research shows that university students use AI and acquire academic ideas to develop innovative solutions to their problems. However, there is limited research on how AI feedback helps students improve their idea implementation. This study was rooted in the Technology Acceptance Model (TAM) and the Social Cognitive Theory (SCT). The objective was to examine how the AI assessment feedback (AIAF), trust in AI (TAI) and AI literacy (AIL) influence university students' idea implementation skills (IIS). A descriptive survey design was used to collect data from 486 university students. The analysis revealed that AIAF significantly contributes to students' IIS at the university level. TAI was found to partially mediate the relationship between university students' AIAF and IIS. AIL was found to contribute to students' IIS individually. However, the interaction effect (AIAF*AIL) was not found to be a significant contributor to students' IIS. We found that the interplay among the study's variables (AIAF, TAI and AIL) positively enhances university students' IIS in solving their academic problems. It was recommended that students should use self-regulated learning in AI feedback to improve their achievements. In addition, the current study has several practical, research and policy implications.

As blended learning becomes a new norm in higher education, students' learning behaviours in this context have drawn great concern. To accurately assess these behaviours, this TAM-based mixed-method study first developed a semi-structured interview protocol via literature review, interviewed 10 university students with blended learning experience to preliminarily construct a 3-dimensional framework (behavioural intention, process, feedback), then 297 students from a university in China who have participated in blended learning are randomly selected as the research objects. Exploratory factor analysis, confirmatory factor analysis and reliability tests showed good model fit and scale reliability/validity, indicating the scale has application value and provides ideas for universities to enhance students' positive learning behaviours in blended learning.

This study aimed to examine the effectiveness of ChatGPT-generated read-aloud plans compared to teacher-prepared read-aloud plans for third-grade elementary school students, specifically focusing on their vocabulary and inference skills. A total of 93 students from a public elementary school participated in the study. The treatment group engaged in ChatGPT-generated read-aloud plans, while the control group participated in teacher-prepared ones. A MANCOVA analysis was conducted to assess the impact of these activities on students' vocabulary and inference skills, controlling for potential covariates. Despite the adequate sample size and statistical power, the results revealed no significant differences between the treatment (ChatGPT-generated) and comparison (teacher-prepared) groups, and the observed effect size was small. These findings suggest that there was no significant difference between the read-aloud plans generated by ChatGPT and those prepared by the teacher, indicating that both types of plans had a similar impact on students' vocabulary and inference skills under the current conditions. The implications of these results for educational practice and the use of AI tools in the classroom are discussed.