Background: Clinical teaching involves two essential parts, theory and clinical experience. The latter requires students to have an adequate level of contact with patients, but it can be affected by limited resources such as inadequate clinical facilities and months of industrial actions by healthcare workers in countries such as Nigeria. Although such a loss in learning opportunities can be lessened in other contexts with the use of clinical simulation tools, a lack of such resources is a challenge in resource-limited settings. Aim: The aim of this article is to present the use of an artificial intelligence (AI)-generated visual clinic, consisting of videos on the management of different diseases/conditions. Method: In this paper, topmedia AI (https://www.topmediai.com/app/text-to-video/) was used to generate videos of patients with stroke performing constraint-induced movement therapy (CIMT), motor imagery, sit-to-stand exercises, over-ground gait training and treadmill gait training using text prompts. After that, these different videos were combined together to form a stroke rehabilitation clinic. Result: A video of seemingly high quality showing patients undergoing stroke rehabilitation was produced. Conclusion: It is possible to generate videos of different diseases/conditions using AI, as it is very efficient, and catalogue them as a clinic for use in place of, or as an adjunct to, traditional clinical teaching. This may provide advantages for adequate clinical experience, access to rare and under-referred conditions/diseases, serving as succor during industrial actions, and as a means to reduce students’ exposure to dangers such as infections and harms. In addition, unlike traditional clinical hours, the AI-generated visual clinic will be opened for use to students for 24 hours.

Introduction:

The rapid emergence of generative artificial intelligence (GenAI) has prompted the need for future teachers to balance innovation and creativity with ethics, child safety and responsible classroom practice. This study reports on the design and initial implementation of AI in Education, an undergraduate course for second-year pre-service early childhood education (ECE) students at a federal university in the United Arab Emirates. The course aims to support ethical and pedagogically grounded AI integration in future ECE classrooms and was launched in early 2026.

Methods:
A design-based implementation approach has been used. The course combines interactive lectures, guided workshops, sandbox experimentation, and a simulated teaching practicum in which students explore child-facing and teacher-facing AI tools. This study examines course artefacts, simulated practicum evidence, student-generated lesson projects, and individually designed AI toolkits, along with students’ ethical reflections. Qualitative thematic analysis examines evidence of AI literacy development, ethical awareness, and practical classroom application.

Results:
Findings indicate that structured exposure to AI concepts, ethical frameworks, and supervised simulated practicum experimentation support measurable growth in students’ conceptual understanding, critical evaluation of risks and benefits, and ability to design developmentally appropriate AI-supported learning activities. Ethical reflection emerges as a central mechanism linking technological competence with responsible professional practice.

Conclusions:
Embedding ethical AI integration within teacher education through practice-based coursework offers a viable model for preparing future educators in rapidly evolving digital contexts. This course model demonstrates how structured design, supervised experimentation, and reflective artefacts can support responsible AI adoption in pre-service educators and provides a transferable framework for similar programs internationally.

While the technological capabilities of Generative Artificial Intelligence (GenAI) are extensively documented, understanding how teachers exercise professional agency in AI-mediated instruction remains underexplored, particularly in non-Western contexts where institutional structures, technological infrastructures, and policy frameworks differ markedly from settings where most existing research has been conducted. This study addresses this gap by examining how Chinese university English teachers enact agency when integrating localized AI platforms into their pedagogical practices. Drawing on the Ecological Approach to Agency (Priestley et al., 2015), this qualitative inquiry examines the lived experiences of university English teachers who employ domestically developed AI tools in their instruction. Data were collected through semi-structured interviews and teacher reflection journals, and analyzed using thematic analysis. The ecological framework enables examination of agency across multiple temporal and contextual dimensions: past technological experiences, future professional aspirations, and immediate institutional constraints. Findings reveal that teacher agency in AI-mediated contexts operates as a dynamic negotiation rather than a binary acceptance-rejection decision. Teachers demonstrate varied approaches to AI integration, shaped by factors including research performance pressures, student assessment requirements, peer knowledge networks, and ethical concerns regarding academic integrity. The study contributes empirical evidence of teacher agency beyond Western settings and highlights the role of localized technological ecologies in shaping pedagogical decision-making. These findings offer implications for professional development programs that honor teachers' situated expertise in navigating technological change, and for policies aimed at supporting context-sensitive AI integration in higher education.

This article examines the challenges and barriers faced by educators in two key areas: first, access to professional development opportunities and institutional support related to Artificial Intelligence (AI); and second, the effective integration of AI tools into classroom practice and instructional design. As AI technologies increasingly influence teaching and learning, teachers are expected to develop new forms of digital competence and pedagogical understanding. This study critically reviews existing theoretical frameworks and professional development models designed to support teachers’ engagement with emerging technologies, with a particular focus on AI in education settings.

The review identifies a range of interconnected structural, institutional, and pedagogical barriers that hinder effective participation in AI-related professional development. These include limited availability and accessibility of relevant training opportunities, time constraints within teachers’ professional workloads, disparities in digital confidence and expertise, and the frequent misalignment between professional development initiatives and the practical realities of classroom contexts. In addition, the study highlights how broader systemic factors, such as policy expectations and resource allocation, shape teachers’ capacity to engage meaningfully with AI-focused learning opportunities.

Special attention is given to the role of school leadership in mediating these challenges. The findings emphasise the importance of supportive, compassionate, and visionary leadership, particularly the role of school principals, in fostering a culture that encourages professional learning, experimentation, and technology integration. This article presents strategic recommendations for teacher education, curriculum design, and institutional planning, based on current research. These recommendations support school educators in developing the competencies, confidence, and pedagogical orientations needed for the ethical and responsible integration of Artificial Intelligence (AI) into the teaching and learning process.

The use of social media in education, especially in English as a Foreign Language (EFL) classrooms, continues to generate discussion among educators and researchers. Although many institutions have integrated social media into teaching for communication, collaboration, and access to learning materials, its actual influence on students’ academic performance remains uncertain. Most previous studies have examined social media in education more broadly, with limited attention to how EFL learners specifically use these platforms to support their language learning. To address this research gap, this study investigates social media as a developing educational tool among EFL students, focusing on their perceptions, preferred platforms, patterns of use, time management, and account ownership, in order to understand its role in their academic development. The study adopted a mixed-methods approach, using an online questionnaire to gather quantitative data and identify key trends, alongside semi-structured interviews to provide deeper insights into students’ experiences. The findings reveal that although students recognize the educational value of social media, they mainly use it for entertainment. They rely on different platforms for different purposes, with Google, YouTube, and WhatsApp emerging as the most frequently used for academic activities. Additionally, the results indicate that distance learning is still developing and faces several challenges, leading students to favor a blended learning model that combines traditional classroom instruction with online resources for a more balanced learning experience.

The integration of Artificial Intelligence (AI) into Primary Education poses a pedagogical dilemma between technological efficiency and the development of logical reasoning. This qualitative study examines the perceptions and lived experiences of 32 pre-service teachers enrolled in the Primary Education Degree at the Faculty of Education and Psychology (University of Extremadura). Through content analysis supported by RLQDA software, participants' narratives regarding the deployment of AI in mathematics education were coded to identify patterns in curricular articulation and professional identity.

The results reveal a dualistic perception of technology. On the one hand, future teachers positively value AI as a tool for inclusion and personalization, highlighting its ability to reduce abstraction in content blocks such as geometry and fractions through dynamic visualizations and immediate feedback. Conversely, a transversal concern emerges regarding "cognitive dependency" and the risk of students substituting intellectual effort with automated results. Furthermore, a gap is observed between the participants' personal use of AI (instrumental and administrative, e.g., using ChatGPT for summarizing or planning) and the didactic use they project for the classroom (gamification and level adaptation). The study concludes that while pre-service teachers do not reject the technology, they demand training that empowers them to lead its ethical use, reaffirming their irreplaceable role as guides against the automation of learning.

Abstract

Artificial intelligence (AI) is increasingly integrated into higher education, offering scalable tools for developing interactive learning resources. In medical education, large language models such as ChatGPT show potential for generating clinical simulation cases; however, their educational validity and clinical reliability remain insufficiently examined. Ensuring the accuracy and pedagogical quality of AI‑generated materials is essential before integrating them into formal training programs.

Methods

The study was conducted by 1 author of the prompts; 2 authors of the expert (human) clinical scenarios; and 5 independent expert reviewers. A comparative validity study was conducted using 50 multiple‑choice clinical scenarios (MCQs) in emergency medicine: 25 developed by experienced instructors and 25 generated using ChatGPT (GPT‑5 mini). The scenarios covered five core emergency topics: cardiac arrest, shock, trauma and accidents, acute coronary syndrome, and acute respiratory failure. Five independent experts evaluated all cases using ten predefined content validity criteria, including clinical accuracy, completeness, structural clarity, realism, educational value, error‑free presentation, applicability, coherence between scenario and question, uniqueness, and distractor homogeneity. Quantitative assessment included the Item Content Validity Index (I‑CVI) and Aiken’s V coefficient.

Results

Instructor‑developed cases demonstrated significantly higher overall quality than AI‑generated cases (3.8 ± 0.13 vs. 3.0 ± 0.59; p < 0.001). Expert-developed cases showed excellent content validity (I‑CVI = 0.984; S‑CVI/Ave = 0.99), whereas AI‑generated cases demonstrated substantially lower validity (I‑CVI = 0.496; S‑CVI/Ave = 0.50). Aiken’s V indicated very high expert agreement for instructor‑developed cases (0.936) and moderate agreement for AI‑generated cases (0.671). Common issues in AI‑generated cases included insufficient clinical detail, heterogeneous distractors, and occasional logical inconsistencies.

Conclusion

ChatGPT can serve as an efficient support tool for the rapid generation of emergency medicine simulation cases. However, expert review and pedagogical refinement remain essential to ensure clinical accuracy and educational quality. AI‑generated content should complement, rather than replace, expert‑designed instructional materials.

Introduction: Understanding how young people perceive Artificial Intelligence (AI) is crucial for promoting its ethical and responsible adoption. The Positive Youth Development (PYD) framework proposes a strength-based approach centered on the 5Cs, which promote overall well-being. While PYD has proven effective in mitigating various digital risks, its role in shaping AI-related attitudes remains largely unexplored. Therefore, this study analyzed the relationship between these attitudes and the 5Cs of PYD within the university context.

Method: The sample consisted of 407 Spanish undergraduate students (63.1% women; M = 21.93) enrolled in 10 universities in Andalusia (Spain). Participants completed the PYD-SF and AIAS-4 scales during the year 2025.

Results: The findings indicated a weak but significant correlation between both constructs, suggesting that specific PYD dimensions are aligned with a favorable view of AI. Hierarchical regression models found that higher levels of Confidence, Competence, and degree area emerged as significant positive predictors of AI favorability. Conversely, Character showed a negative association. Students in Science, Engineering, and Architecture reported the most favorable attitudes compared to their peers in Arts and Humanities, who obtained the lowest scores. Competence and degree area were found to be significant predictors of the perception of AI as a positive technology for humanity. No significant gender differences were observed.

Discussion: These results suggest that certain strengths, such as self-efficacy (Competence) and self-esteem (Confidence), facilitate technology adoption. However, students with higher moral awareness (Character) appear to maintain a more critical stance regarding ethical risks and social inequalities. Future research should address the limitations of this cross-sectional design and explore specific AI applications. Finally, practical implications underscore the need for governments to increase transparency in AI regulation to foster public trust.

This study analyses the impact of an instructional intervention based on Scratch for teaching statistical knowledge in Year 5 of Primary Education, with the aim of improving the learning of statistics (traditionally less addressed in the classroom) and promoting more active methodologies in the area of mathematics. To this end, a quasi-experimental design with a pre-test–post-test and a control group was conducted, with a sample of 47 students from a school in the Autonomous Community of Extremadura. The experimental group (n = 23) worked on the contents through Scratch-based activities, while the control group (n = 24) followed a traditional methodology supported by the textbook. The intervention addressed data collection and organisation, graphical representation, and the calculation of measures of central tendency, integrating elements of computational thinking. Likert-type questionnaires and a written test common to both groups were used for data collection. Data analysis was carried out using inferential statistical tests. The results show that students in the experimental group achieved significantly higher academic performance in learning statistical knowledge. In addition, students who used Scratch produced higher-quality definitions of fundamental statistical concepts and rated the tool as more usable and understandable than the textbook. Therefore, this study confirms Scratch as an effective instructional resource for teaching statistics in Primary Education, as it enhances academic performance, understanding of basic statistical knowledge, and a positive perception of the learning experience.

The increasing presence of social media in students’ everyday lives has generated growing interest in its perceived influence on learning processes and cognitive functioning within educational contexts. This research forms part of an ongoing doctoral project that builds on previous work conducted with university students and is currently being expanded to different educational stages and national contexts.

The initial phase of the research explored university students’ perceptions of the impact of social media use on cognitive functioning, academic performance, study organisation, and rest. This phase employed an online self-report questionnaire distributed via Google Forms, complemented by qualitative data collected through semi-structured interviews. Drawing on the findings and methodological foundations of this earlier work, the doctoral project now adopts a broader and comparative perspective.

At present, two complementary research plans are being developed. The first continues data collection with university students in Spain, allowing for the replication and extension of previous results. The second extends the study to secondary education contexts in England, incorporating both the original questionnaire and the Teenage Executive Functioning Inventory (TEXI) to explore perceived cognitive and learning-related processes across educational stages.

The overall research design follows a non-experimental, mixed-methods approach and focuses on students’ perceptions rather than objective performance measures. By examining how learners at different educational levels perceive the role of social media in relation to their learning and everyday cognitive functioning, this study contributes to current discussions in technology-enhanced education. The research aims to inform educational practice and future research by highlighting the importance of self-regulation, digital awareness, and students’ perspectives in contemporary learning environments.