Search Results (71)

Metacognition and self-regulated learning (SRL) are widely recognized as key mechanisms for academic achievement and skill development, yet in music education they have rarely been examined through explicit instructional interventions to enable causal testing and effect evaluation. To address this gap, this study followed PRISMA guidelines and conducted a systematic review of 31 studies (including seven for meta-analysis) to identify intervention types and mechanisms, and to quantify their overall effects and moderating factors. Results indicate the following: (1) the intervention ecology is grounded in structured learning support (SLS), frequently combined with strategy teaching (ST) or technology-enhanced interventions (TEI), with full integration concentrated at the university level. (2) The mechanisms operate primarily along four pathways: structure facilitates a “plan–practice–reflection” loop, strategy instruction makes tacit experience explicit, technological feedback provides a third-person perspective, and teacher support stabilizes motivation. (3) The meta-analysis revealed a significant positive medium effect overall. (4) Intervention structure moderated outcomes, though not as a single or stable determinant. (5) Effects followed a U-shaped pattern across educational stages, strongest in secondary school, followed by university, and weaker in preschool and primary. Future research should employ proximal, task-aligned measures, conduct parallel multi-indicator assessments within the same stage, and expand evidence for multi-mechanism integration in primary and secondary school contexts. Experimental designs manipulating levels of SLS are needed to test whether ST + TEI remain effective under low-structure conditions, thereby identifying the minimum structural threshold. Extending samples to informal and professional music learners would further enhance robustness and generalizability. Full article

This study proposes an integrated TAM–TPACK framework that explicates how teachers’ technology acceptance progresses to lesson design and enactment in elementary physical education, using augmented reality (AR) climbing as the focal case. A qualitative case study was conducted with three elementary teachers. Data comprised semi-structured interviews, classroom videos, lesson plans, and satisfaction surveys, and were interpreted through directed content analysis. The findings indicate that perceived usefulness (PU) and perceived ease of use (PEU) systematically informed goal setting, feasibility judgments, and content–curriculum alignment, whereas behavioral intention (BI) shaped pedagogical intent and design decisions. Technological Content Knowledge (TCK) and Technological Pedagogical Knowledge (TPK) functioned as core mechanisms that structurally aligned AR functionalities with curricular aims and instructional procedures. Clear correspondences—PU↔TCK, PEU↔TPK, and BI↔TPACK—were identified, conceptually mapping a pathway from technology acceptance to lesson design and classroom enactment. The study advances a concise, empirically grounded, teacher-centered model for digital physical education and underscores the need for standardized adoption criteria, structured professional development, implementation incentives, and equitable access to local infrastructure to support sustained practice as part of a sustainability-oriented digital transformation of physical education systems. Full article

Computational thinking (CT) is recognized as a core competency for the 21st century, and its development is shaped by multiple factors, including students’ individual characteristics and their use of information and communication technology (ICT). Drawing on large-scale international data from the 2023 cycle of the International Computer and Information Literacy Study (ICILS), this study analyzes a sample of 81,871 Grade 8 students from 23 countries and one regional education system who completed the CT assessment. This study is the first to apply a predictive modeling framework that integrates two machine learning techniques to systematically identify and explain the key variables that predict CT and their nonlinear effects. The results reveal that various student-level predictors—such as educational expectations and the number of books at home—as well as ICT usage across different contexts, demonstrate significant nonlinear patterns in the model, including U-shaped, inverted U-shaped, and monotonic trends. Compared with traditional linear models, the SHapley Additive exPlanations (SHAP)-based approach facilitates the interpretation of the complex nonlinear effects that shape CT development. Methodologically, this study expands the integration of educational data mining and explainable artificial intelligence (XAI). Practically, it provides actionable insights for ICT-integrated instructional design and targeted educational interventions. Future research can incorporate longitudinal data to explore the developmental trajectories and causal mechanisms of students’ CT over time. Full article

Generative Artificial Intelligence (GAI) is advancing rapidly and is increasingly integrated into visual communication design education. How to effectively and sustainably leverage GAI to support visual communication design teaching has thus become a critical issue faced by educators. While prior studies have focused on GAI’s impact on student learning outcomes and creativity, limited research has explored its effects on emotions and student engagement. This study aims to investigate the impact of customized GAI integration on visual communication design students’ learning engagement and to qualitatively explore the emotions that occur throughout the learning process. Using a quasi-experimental design, 96 students were randomly assigned to either a control group using traditional instruction or an experimental group using a customized GAI. Student engagement was measured using pre- and post-assessment scales, and semi-structured interviews were conducted to analyze students’ emotional changes. The results show that customized GAI integration effectively enhanced students’ cognitive, emotional, and behavioral engagement. Moreover, students experienced diverse and dynamic emotions during the learning process, which influenced their engagement. This study provides empirical support for the application of GAI in visual communication design education, highlighting the importance of balancing technology integration with emotional regulation, thereby informing the responsible and sustainable integration of GAI in design education. Full article

Artificial Intelligence (AI), particularly Generative AI (GenAI) and Large Language Models (LLMs), is rapidly reshaping higher education by transforming teaching, learning, assessment, research, and institutional management. This entry provides a state-of-the-art, comprehensive, evidence-based synthesis of established AI applications and their implications within the higher education landscape, emphasizing mature knowledge aimed at educators, researchers, and policymakers. AI technologies now support personalized learning pathways, enhance instructional efficiency, and improve academic productivity by facilitating tasks such as automated grading, adaptive feedback, and academic writing assistance. The widespread adoption of AI tools among students and faculty members has created a critical need for AI literacy—encompassing not only technical proficiency but also critical evaluation, ethical awareness, and metacognitive engagement with AI-generated content. Key opportunities include the deployment of adaptive tutoring and real-time feedback mechanisms that tailor instruction to individual learning trajectories; automated content generation, grading assistance, and administrative workflow optimization that reduce faculty workload; and AI-driven analytics that inform curriculum design and early intervention to improve student outcomes. At the same time, AI poses challenges related to academic integrity (e.g., plagiarism and misuse of generative content), algorithmic bias and data privacy, digital divides that exacerbate inequities, and risks of “cognitive debt” whereby over-reliance on AI tools may degrade working memory, creativity, and executive function. The lack of standardized AI policies and fragmented institutional governance highlight the urgent necessity for transparent frameworks that balance technological adoption with academic values. Anchored in several foundational pillars (such as a brief description of AI higher education, AI literacy, AI tools for educators and teaching staff, ethical use of AI, and institutional integration of AI in higher education), this entry emphasizes that AI is neither a panacea nor an intrinsic threat but a “technology of selection” whose impact depends on the deliberate choices of educators, institutions, and learners. When embraced with ethical discernment and educational accountability, AI holds the potential to foster a more inclusive, efficient, and democratic future for higher education; however, its success depends on purposeful integration, balancing innovation with academic values such as integrity, creativity, and inclusivity. Full article

This paper reports a study conducting an in-depth analysis of the impacts of ethical training on the adoption of AI tools among computer science students in higher vocational colleges. These students will serve as the pivotal human factor for advancing the field of AI. Aiming to explore practical models for integrating AI ethics into computer science education, the research seeks to promote more responsible and effective AI application and therefore become a positive influence in the field. Employing a mixed-methods approach, the study included 105 students aged 20–24 from a vocational college in Guangdong Province, a developed region in China. Based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, a five-point Likert scale was used to evaluate the participants’ perceptions of AI tool usage based on ethical principles. The Structural Equation Modeling (SEM) results indicate that while participants are motivated to adopt AI technologies in certain aspects, performance expectancy negatively impacts their intention and actual usage. After systematically studying and understanding AI ethics, participants attribute a high proportion of responsibility (84.89%) to objective factors and prioritized safety (27.11%) among eight ethical principles. Statistical analysis shows that habit (β = 0.478, p < 0.001) and hedonic motivation (β = 0.239, p = 0.004) significantly influence behavioral intention. Additionally, social influence (β = 0.234, p = 0.008) affects use behavior. Findings regarding factors that influence AI usage can inform a strategic framework for the integration of ethical instruction in AI applications. These findings have significant implications for curriculum design, policy formulation, and the establishment of ethical guidelines for AI deployment in higher educational contexts. Full article

Integrating information and communication technology (ICT) into STEM education enhances instructional quality and cultivates students’ interdisciplinary problem-solving. STEM teachers’ attitudes—driven by perceived ease of use (PEOU) and perceived usefulness (PU)—are pivotal in ICT adoption, and pre-service training offers a vital opportunity to shape these attitudes. Yet, empirical studies investigating how specific training strategies influence ICT attitudes via PEOU and PU remain scarce. Using a mixed-methods approach combining questionnaires and interviews, the results indicate that pre-service training significantly improved STEM teachers’ attitudes toward ICT-enhanced teaching. Socially interactive strategies (role models and collaboration) enhanced attitudes via PEOU by boosting confidence and reducing technology-related anxiety, cognitive design strategies (reflection and instructional design) operated through PU by emphasizing ICT’s pedagogical value, and experiential feedback strategies (authentic experience and feedback) influenced attitudes through both PEOU and PU, fostering integrated technical and pedagogical development. These findings support an integrated SQD–TAM framework and provide practical guidance for designing pre-service STEM teacher programs to promote sustained ICT adoption in China, and meanwhile highlights the importance of strategically sequencing training to cultivate both technological competence and pedagogical insight among future STEM educators. Full article

Integrating digital technologies into mathematics teaching remains a complex task for teachers, despite its recognized potential to enhance learning. Research on teacher professional development (TPD) for integrating technology in the mathematics classroom is therefore essential to inform the design of effective TPD programs. This narrative review aims to support researchers by providing an overview of theoretical frameworks employed in the literature on TPD for teaching mathematics with technology. Specifically, we address the following research questions: what theoretical frameworks are employed in research that focuses on TPD for teaching mathematics with technology? In what ways are these theoretical frameworks used? Drawing from a set of studies identified in a recent systematic review, we identify ten theoretical frameworks and classify their use into three categories: design tools, analytical tools, and instructional tools. While most frameworks are used to inform TPD design and data analysis, their use as reflective tools with teachers is less common. This review contributes to clarifying the role of theoretical frameworks in the field and suggests future research directions, including the exploration of their metacognitive potential in TPD settings and the comparison of multiple frameworks through networking. Full article

There is increasing interest in integrating various forms of Information and Communication technologies (ICT) into education. Well-established theoretical guidelines should guide the integration of these technologies. A flipped classroom is an example of an educational approach that integrates ICT and is guided by an active learning philosophy. The current study aims to evaluate participants’ acceptance of the flipped learning instructional model using six indicators—perceived usefulness, ease of use, hedonic motivation, attitude, self-efficacy, and educational quality—and to assess overall satisfaction. Additionally, it examines how these factors relate to overall satisfaction with this approach. The study utilized a descriptive cross-sectional research design with an exploratory and correlational orientation. The target population for this study included undergraduate students enrolled in the “Computer Applications in Education” course offered by the College of Education over three consecutive semesters: the second semester of the 2023/2024 academic year and the first and second semesters of 2024/2025. All students in this course experienced the flipped learning model as part of their instructional activities. Out of the 180 students, 137 completed the data collection tool, which was a questionnaire. The results showed that participants’ acceptance of the flipped learning approach was generally positive, ranging from moderate to high across all measured dimensions. The majority reported high levels of hedonic motivation, positive attitudes, perceived educational quality, and ease of use of the flipped learning requirements. Students found the flipped learning experience enjoyable, effective, and manageable. They believed it enhanced their learning and reported moderate self-efficacy and perceived usefulness. While satisfaction with flipped learning was moderate overall, it was strongly associated with enjoyment, positive attitudes, self-efficacy, and perceived educational quality, as evident in the results of the correlation analysis. Regression analysis revealed that these four factors were significantly associated with students’ satisfaction, whereas perceived usefulness and ease of use were not significantly associated when considered alongside other variables. These results suggest that emotional engagement, confidence, and perceived educational value are key contributors to students’ satisfaction with flipped learning. Full article

This systematic review investigates the integration of artificial intelligence (AI) and information and communication technologies (ICT) in physical education across all educational levels. Physical education is uniquely centered on motor skill development, physical activity engagement, and health promotion—outcomes that require tailored technological approaches. Through the analysis of recent empirical studies, the main areas where digital technologies contribute to pedagogical innovation are highlighted—such as personalized learning, real-time feedback, student motivation, and educational inclusion. The findings show that AI-assisted tools facilitate differentiated instruction and self-regulated learning by adapting to students’ individual performance levels. Technologies such as wearables and augmented reality (AR)/virtual reality (VR) systems increase engagement and support the participation of students with special educational needs. Furthermore, AI contributes to more efficient and objective assessment of motor performance, coordination, and movement quality. However, significant structural and ethical challenges persist, such as unequal access to digital infrastructure, lack of teacher training, and concerns related to personal data protection. Teachers’ perceptions reflect both openness to the educational potential of AI and caution regarding its practical implementation. The review concludes that AI and ICT can substantially transform physical education, provided that coherent policies, clear ethical frameworks, and investments in teachers’ professional development are in place. Full article

Artificial intelligence (AI)-generated content (AIGC) is an innovative technology that utilizes machine learning, AI models, reward modeling, and natural language processing (NLP) to create diverse digital content such as videos, images, and text. It has the potential to support various human activities with significant implications in teaching and learning, facilitating heuristic teaching for educators. By using AIGC, teachers can create extensive knowledge content and effectively design instructional strategies to guide students, aligning with heuristic teaching. However, incorporating AIGC into heuristic teaching has controversies and concerns, which potentially mislead outcomes. Nevertheless, leveraging AIGC greatly benefits teachers in enhancing heuristic teaching. When integrating AIGC to support heuristic teaching, challenges and risks must be acknowledged and addressed. These challenges include the need for users to possess sufficient knowledge reserves to identify incorrect information and content generated by AIGC, the importance of avoiding excessive reliance on AIGC, ensuring users maintain control over their actions rather than being driven by AIGC, and the necessity of scrutinizing and verifying the accuracy of information and knowledge generated by AIGC to preserve its effectiveness. Full article

This study investigated pre-service teachers’ (PTs) intentions to adopt generative AI (GenAI) tools in future classrooms by applying an extended Technology Acceptance Model (TAM). Participants were enrolled in multiple teacher-preparation programs within a single U.S. higher education institution. Through a structured GenAI-integrated activity using Khanmigo, a domain-specific AI platform for K-12 education, PTs explored AI-supported instructional tasks. Post-activity data were analyzed using PLS-SEM. The results showed that perceived usefulness (PU), perceived ease-of-use (PEU), and self-efficacy (SE) significantly predicted behavioral intention (BI) to adopt GenAI, with SE also influencing both PU and PEU. Conversely, personal innovativeness in IT and perceived cyber risk showed insignificant effects on BI or PU. The findings underscored the evolving dynamics of TAM constructs in GenAI contexts and highlighted the need to reconceptualize ease-of-use and risk within AI-mediated environments. Practically, the study emphasized the importance of preparing PTs not only to operate AI tools but also to critically interpret and co-design them. These insights inform both theoretical models and teacher education strategies, supporting the ethical and pedagogically meaningful integration of GenAI in K-12 education. Theoretical and practical implications are discussed. Full article

Based on flipped learning, digital competence, and inclusive instructional design, this study employs a mixed-method approach (quantitative and qualitative) to evaluate the pilot and involves academics from six European universities. Teacher participants co-designed and implemented flexible learning scenarios using the FLeD tool, which integrates pedagogical patterns, scaffolding strategies, and playful features. Using a mixed-methods research approach, this study collected and analyzed data from 34 teachers and indirectly over 800 students. Results revealed enhanced student engagement, self-regulated learning, and pedagogical innovation. While educators reported increased awareness of inclusive teaching and benefited from collaborative design, challenges related to tool usability, time constraints, and the implementation of inclusivity also emerged. The findings support the effectiveness of structured digital tools in promoting pedagogical transformation in online, face-to-face, and hybrid learning. This study contributes to the discussion on the digitalization of higher education by illustrating how research-informed design can enable educators to develop engaging and flexible inclusive learning environments in line with the evolving needs of learners and the opportunities presented by technology. Full article

In this post-digital age, writing assessment has been markedly influenced by advancements in artificial intelligence (AI), emphasizing the role of automated formative feedback in supporting second language (L2) writing. This study investigates how Norwegian teachers use an AI-driven automated feedback tool, the Essay Assessment Technology (EAT), in process writing for the first time. Framed by the second and third-order barriers framework, we looked at teachers’ beliefs and the design level changes that they made in their teaching. Data were collected in Autumn 2022, during the testing of EAT’s first prototype. Teachers were first introduced to EAT in a workshop. A total of 3 English as a second language teachers from different schools were informants in this study. Teachers then used EAT in the classroom with their 9th-grade students (13 years old). Through individual teacher interviews, this descriptive qualitative study explores teachers’ perceptions, user experiences, and pedagogical decisions when incorporating EAT into their practices. The findings indicate that teachers’ beliefs about technology and its role in student learning, as well as their views on students’ responsibilities in task completion, significantly influence their instructional choices. Additionally, teachers not only adopt AI-driven tools but are also able to reflect and solve complex teaching and learning activities in the classroom, which demonstrates that these teachers have applied design thinking processes in integrating technology in their teaching. Based on the results in this study, we suggest the need for targeted professional development to support effective technology integration. Full article

The rapid development of generative artificial intelligence (AI) systems such as ChatGPT (GPT-4) could transform teaching and learning. Yet, integrating these tools requires insight into what drives students to adopt them. Research on ChatGPT acceptance has so far focused on university settings, leaving school contexts underexplored. This study addresses the gap by surveying 506 upper secondary students in Baden-Württemberg, Germany, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Performance expectancy, habit and hedonic motivation emerged as strong predictors of behavioral intention to use ChatGPT for school purposes. Adding personality traits and personal values such as conscientiousness or preference for challenge raised the model’s explanatory power only marginally. The findings suggest that students’ readiness to employ ChatGPT reflects the anticipated learning benefits and enjoyment rather than the avoidance of effort. The original UTAUT2 is therefore sufficient to explain students’ acceptance of ChatGPT in school contexts. The results could inform educators and policy makers aiming to foster the reflective and effective use of generative AI in instruction. Full article