European Journal of Education最新文献

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.

As generative artificial intelligence (GenAI) gains popularity, there is still a significant gap in understanding its potential to enhance STEM education. To address this gap, we conducted a systematic review of 55 studies through the lens of the five-dimensional technology-based learning framework (devices, domains, methods, participants and research issues). Following PRISMA guidelines, we extracted and coded each paper's GenAI tool, subject area, methodology, participant profile, sample size, and the reported benefits and barriers. The majority of articles focused on GenAI-assisted science education, followed by technology education, while research on engineering, mathematics and integrated STEM learning remained relatively limited. Text-based GenAI was the most commonly used tool, while image-based, video-based, music-based and audio-based GenAI were rarely featured. Most studies concentrated on higher STEM education, with limited research focusing on K-12 STEM education. Quantitative methods were predominantly used to assess the effectiveness of GenAI. GenAI offered significant benefits in STEM education, positively influencing individual learners, teachers, and the learning and teaching process. However, GenAI also posed challenges for learners, including unreliability and inaccuracy, ethical and academic integrity concerns, dependence, reduced critical thinking skills and learning efficiency, and accessibility barriers. Teachers faced challenges like limited instructional support and the need to adopt more student-centred approaches. Based on the results, we suggested several research priorities to further explore GenAI-assisted STEM education. Additionally, we outlined key implications for learners and educators to fully leverage GenAI's benefits and overcome the obstacles.

This study examines the integration of artificial intelligence (AI) in creative arts education within Chinese higher education, addressing a critical gap in the literature on AI's role in artistic disciplines. Using a mixed-methods approach, the research combines surveys of 734 students and semi-structured interviews with faculty from core art universities to explore perceptions of AI's impact on teaching, learning, and creativity. Findings reveal that gender significantly influences students' perceived value of AI, with female students reporting higher confidence in AI's educational benefits. Faculty acknowledge AI's potential to enhance efficiency but highlight discipline-specific limitations, ethical concerns, and institutional barriers such as inadequate training and infrastructure. While AI fosters exploratory thinking, the risks of creativity homogenization and the reduction of traditional skills persist. The study underscores the need for structured pedagogical frameworks, ethical guidelines, and institutional support to balance AI's advantages with artistic integrity. These insights contribute to ongoing debates on technology integration in arts education, offering practical recommendations for equitable and ethically grounded AI adoption in creative disciplines.

This study examines how teachers' collectivist cultural values (TCCV) relate to teacher professional learning (TPL) in Türkiye's highly centralised public-school system, and whether shared instructional leadership (SIL) helps explain this relationship. Drawing on survey data from 628 teachers working in public schools across 12 NUTS regions, we estimated a structural equation model linking TCCV, SIL and TPL. The results show that TCCV is only weakly but positively associated with both SIL and TPL, whereas SIL is moderately and positively associated with TPL. When SIL is included in the model, the direct association between TCCV and TPL becomes non-significant and TCCV is linked to TPL primarily through SIL. In statistical terms, SIL fully mediates the TCCV–TPL relationship, but the size of the TCCV effects is small, while SIL explains a substantial share of the variance in TPL. These findings suggest that in centralised, collectivist settings, cultural value orientations support teacher learning chiefly by enabling principals and teachers to share instructional leadership, rather than through TCCV alone. This study explains how culture and leadership influence professional learning in a way that is specific to the context. The study highlights the significance of drawing on existing collectivist norms and advancing SIL practices. The cross-sectional, single-source design and the focus on public schools in Türkiye limit the generalizability and causal interpretation of the findings.