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Purpose In the context of far-reaching changes in higher education and the labour market, there has been extensive discussion on what constitutes graduate employability and what shapes graduates’ labour market outcomes. Many of these discussions are based on skills-centred approaches and related supply-side logic. The purpose of this paper is to develop an alternative, relational conceptualisation of employability based on the concept of capitals. It discusses how this provides a more detailed and multi-dimensional account of the resources graduates draw upon when transitioning to the labour market. Design/methodology/approach The paper presents a new model on graduate employability, linked to five areas of capital which are seen as constitutive of graduates’ employability and significant to their transitions to the labour market. The paper draws together existing conceptual approaches and research studies to illustrate the different features of the model and how they relate to graduate employability. It also discusses some practical implications for those helping to facilitate graduates’ transitions to the job market. Findings The paper argues that the graduate capital model presents a new way of understanding graduate employability which addresses the challenges of facilitating graduates’ transitions and early career management. The forms of capital outlined are conceived as key resources that confer benefits and advantages onto individuals. These resources encompass a range of human, social, cultural, identity and psycho-social dimensions and are acquired through graduates’ formal and informal experiences. Research limitations/implications Whilst this is a conceptual model, it has potentially strong implications for future research in this area in terms of further research exploration on the core components and their application in the labour market. Practical implications This re-conceptualization of graduate employability has significant implication for graduates’ career management and strategising in developing resources for enhancing their transitions to and progression within the labour market. It also has implications for career educators in developing practical employability strategies that can be used within institutional settings. Social implications The paper raises salient implications for the effective and equitable management of graduate outcomes post-graduation which has clear relevance for all stakeholders in graduate employability, including students/graduates, career educators and employers. Originality/value The paper develops a new model for conceptualising graduate employability and illustrates and applies this to discussion of graduate employability. It also raises practical applications around the different components of the model.

Technological advancements, particularly in the field of artificial intelligence (AI) have played an increasingly important role in transforming education. More recently, ground-breaking AI applications like ChatGPT have demonstrated the potential to bring radical changes to the educational landscape due to their capability to understand complex questions, generate plausible responses and human-like writing, and assist with the completion of complex tasks. However, ChatGPT has limitations in the quality of its output, such as the inclusion of inaccurate, fabricated and biased information and the lack of critical thinking and in-depth understanding. The combinations of these capabilities and limitations along with external factors (e.g., the growing demand for personalized learning support, the irresponsible and unethical use of AI) presents a range of opportunities and challenges to the potential use of ChatGPT in education. This paper presents a thorough SWOT (strength, weakness, opportunity, threat) analysis of ChatGPT, based on which we propose how ChatGPT can be properly integrated into teaching and learning practice to harness its potential in education.

The integration of Artificial Intelligence (AI) in education has transformed academic learning, offering both opportunities and challenges for students’ development. This study investigates the impact of AI technologies on students’ learning processes and academic performance, with a focus on their perceptions and the challenges associated with AI adoption. Conducted at the National University of Science and Technology POLITEHNICA Bucharest, this research involved second-year students who had direct experience with AI-enhanced learning environments. Using purposive sampling, 85 participants were selected to ensure relevance. Data were collected through a structured questionnaire comprising 11 items as follows: seven closed-ended questions assessing perceptions, usage, and the effectiveness of AI tools; and four open-ended questions exploring experiences, expectations, and concerns. Quantitative data were analyzed using frequency and percentage calculations, while qualitative responses were subjected to thematic analysis, incorporating both vertical (individual responses) and horizontal (cross-dataset) approaches to ensure comprehensive theme identification. The findings reveal that AI offers significant benefits, including personalized learning, improved academic outcomes, and enhanced student engagement. However, challenges such as over-reliance on AI, diminished critical thinking skills, data privacy risks, and academic dishonesty were also identified. The study underscores the necessity of a structured framework for AI integration, supported by ethical guidelines, to maximize benefits while mitigating risks. In conclusion, while AI holds immense potential to enhance learning efficiency and academic performance, its successful implementation requires addressing concerns related to accuracy, cognitive disengagement, and ethical implications. A balanced approach is essential to ensure equitable, effective, and responsible learning experiences in AI-enhanced educational environments.

This study aimed to investigate structural relationships between TPACK, teacher self-efficacy, perceived ease of use, and perceived usefulness for preservice teachers who intend to use technology, based on the Technology Acceptance Model (TAM). A total of 296 responses from the College of Education from three Korean universities were analyzed by employing the structural equation modeling methods. The results indicated that preservice teachers' TPACK significantly affected teacher self-efficacy and perceived ease of using technology. The teachers' TPACK also positively influenced their perceived ease of using technology and perceived usefulness of technology in the classroom. Finally, teacher self-efficacy, perceived ease of use, and perceived usefulness of using technology affected teachers' intention to use technology. However, TPACK did not directly affect their intention to use technology. Based on the findings, we discuss implications and suggest future research directions for preservice teachers' intention to use technology.

This article focuses on a key issue for the sustainability of bilingual education: teacher training for content and language integrated learning (CLIL). It begins by canvassing the main innovations affecting teacher training within the current CLIL panorama in terms of the CLIL teacher training profile and the interesting evolution that research into the topic has undergone. The chief challenges stemming from aforementioned innovations are then examined on 7 main fronts: linguistic competence; methodology; scientific knowledge; organizational, interpersonal, and collaborative competence; and ongoing professional development. Finally, 5 concrete lines of action are proposed to address these niches, illustrated with specific practical examples from diverse educational contexts. The updated research evidence and specific actions presented in this article provide a comprehensive overview of where educators stand and where they need to go in the area of teacher training to guarantee a success-prone implementation of CLIL in the European continent and beyond it.

BackgroundHandover is an important clinical skill combining knowledge, prioritisation and communication. This has been highlighted within the emergency working patterns essential for patient care during the pandemic. Effective handover can reduce the incidence of adverse events and benefit patients, clinicians and the hospital. The aim of this project is to assess and improve the quality of departmental verbal and written handover.MethodsA quality improvement method was used to assess the quality of verbal and written handover using verified opinion-based questionnaires for participants. The verbal handover was also assessed by measuring objective parameters including duration, participants and presenter type, number of patients discussed, situational awareness and task management, time pressure and distractions and teamwork factors.Following the initial audit, standardised handover formats and a traffic light system were implemented to assist with prioritising patients. Results were compared before and after intervention with descriptive statistics.Results20 members of our department completed our pre intervention questionnaire and results showed that most participants were dissatisfied with the verbal handover and 50% thought that this might result in adverse events. The disadvantages identified were lengthy duration, multiple interruptions and inappropriate team member leading handover. The objective assessment tool showed that areas of weakness were communication, accountability, task management and distractions. Following intervention with standardisation, objective measures remained stable but subjective responses improved. The satisfaction on general process, duration and person leading handover increased.Conclusions‘Handover’ is highlighted as an important clinical skill especially when working in unfamiliar team and shifts patterns. Implementing a standardised departmental handover has had a positive effect on the team and the process. We intend to continue to improve the handover process by implementing a standardised structure for written handover and continued integrated teaching highlighting communication, prioritisation and verbal handover.

Artificial Intelligence (AI) is revolutionizing many industries and becoming increasingly ubiquitous in everyday life. To empower children growing up with AI to navigate society’s evolving sociotechnical context, we developed three middle school AI literacy curricula: Creative AI, Dancing with AI, and How to Train Your Robot. In this paper we discuss how we leveraged three design principles—active learning, embedded ethics, and low barriers to access – to effectively engage students in learning to create and critique AI artifacts. During the summer of 2020, we recruited and trained in-service, middle school teachers from across the United States to co-instruct online workshops with students from their schools. In the workshops, a combination of hands-on unplugged and programming activities facilitated students’ understanding of AI. As students explored technical concepts in tandem with ethical ones, they developed a critical lens to better grasp how AI systems work and how they impact society. We sought to meet the specified needs of students from a range of backgrounds by minimizing the prerequisite knowledge and technology resources students needed to participate. Finally, we conclude with lessons learned and design recommendations for future AI curricula, especially for K-12 in-person and virtual learning.

Artificial intelligence is the unification of philosophy, cognitive science, mathematics, neurophysiology, psychology, computer science, information theory, cybernetics, and uncertainty theory. Therefore, it is feasible and necessary to utilize STEAM (Science, Technology, Engineering, Liberal Arts, and Mathematics) education to learn artificial intelligence courses. Computational thinking skills are of vital importance to high school students. This paper integrates artificial intelligence education with STEAM model with the aim of enhancing students’ computational thinking skills. First, we investigate the feasibility of this model and set teaching objectives about artificial intelligence curricula. Second, artificial intelligence curricula with STEAM model is proposed to carry out interdisciplinary artificial intelligence knowledge acquisition. Finally, the effects of this model on students’ computational thinking skills, learning motivation, and self-efficacy are evaluated. One hundred thirty-six participants are recruited from a high school in Beijing. The results reveal that the integration of artificial intelligence education with STEAM is able to promote computational thinking skills, learning motivation, and self-efficacy of the students in the experimental group. The main implication of this study is that artificial intelligence education in light of STEAM model can be used as a teaching guide for the combination of artificial intelligence curricula with multi-disciplinary knowledge at the primary and secondary levels.

This study highlights the importance of an educational design that includes robotics and programming through a visual programming language as a means to enable students to improve substantially their understanding of the elements of logic and mathematics. Gaining an understanding of computational concepts as well as a high degree of student participation and commitment emphasize the effectiveness of introducing robotics and visual programming based on active methodologies in primary education. Implementation of this design provides sixth-grade elementary education students with activities that integrate programming and robotics in sciences and mathematics; these practices allow students to understand coding, motion, engines, sequences and conditionals. A quasi-experimental design, descriptive analysis and participant observation were applied across various dimensions to 93 sixth-grade students in four primary education schools. Programming and robotics were integrated in one didactic unit of mathematics and another in sciences. Statistically significant improvements were achieved in the understanding of mathematical concepts and in the acquisition of computational concepts, based on an active pedagogical practice that instills motivation, enthusiasm, commitment, fun and interest in the content studied.

Navigating the current STEM agendas and debates is complex and challenging. Perspectives on the nature of STEM education and how it should be implemented without losing discipline integrity, approaches to incorporating the arts (STEAM) and how equity in access to STEM education can be increased are just a few of the many issues faced by researchers and educators. There are no straightforward answers. Opinions on how STEM education should be advanced vary across school contexts, curricula and political arenas. This position paper addresses five core issues: (a) perspectives on STEM education, (b) approaches to STEM integration, (c) STEM discipline representation, (d) equity in access to STEM education and (e) extending STEM to STEAM. A number of pedagogical affordances inherent in integrated STEM activities are examined, with the integration of modelling and engineering design presented as an example of how such learning affordances can be capitalized on.