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This chapter provides an overview of the potential use of Intelligent Transport Systems (ITS) and associated artificial intelligence (AI) techniques in the land transport sector in an attempt to achieve related United Nations Sustainable Development Goals (SDGs) targets. ITS applications that have now been extensively tested worldwide and have become part of the everyday transport toolkit available to practitioners have been discussed. AI techniques applied successfully in specific ITS applications such as automatic traffic control systems, real-time image processing, automatic incident detection, safety management, road condition assessment, asset management and traffic enforcement systems have been identified. These methods have helped to provide traffic engineers and transport planners with novel ways to improve safety, mobility, accessibility and efficiency in the sector and thus move closer to achieving the various SDG targets pertaining to transportation.

Leadership for learning emerged as an integrated leadership framework; however, attempts to establish an empirical measurement model have been limited. Critically, not much is known about how much teachers' beliefs (e.g. self-efficacy) can mediate leadership for learning impact on teacher behaviors. This study establishes a leadership for learning measurement model and examines whether teacher self-efficacy mediates the effect of leadership for learning tasks on teacher collaboration, instructional quality, intention to leave current schools and their confidence in equitable teaching practice.

Drawing on the most recent 2018 Teaching and Learning International Survey (TALIS), the study employed a structural equation modeling mediation approach.

Results suggested that teacher self-efficacy statistically significantly mediated 16 out of 20 of the relationships between leadership for learning task domains and teacher outcomes. Especially, in explaining the variance in instructional quality and teacher confidence in implementing equitable teaching practices, considerable proportions of the predictive power of leadership for learning tasks were accounted for (i.e. mediated) by teacher self-efficacy.

School-wide efforts to craft the school vision for learning must be coupled with enhancing teacher self-efficacy. Critically, leadership efforts may fall short of implementing equitable teaching practice and quality instruction without addressing teacher confidence in their ability in instruction, classroom management and student engagement.

This study is the first of its kind to evidence teacher self-efficacy mediates leadership for learning practice impact on teacher behaviors.

This chapter discusses the coupling of High Impact Educational Practices with an Active Learning pedagogical approach applied within an introductory undergraduate Visual Communication course (VC1). The course involves several high impact educational practices, such as collaborative assignments, community-based learning, and ePortfolios as reflective tools. VC1 is also open across the School of Art, Design, and Media and accordingly attracts a diverse, multicultural cohort. This heterogeneity provided an ideal circumstance to encourage the exploration of differing cultural perspectives, life experiences, and worldviews and, subsequently, an opportunity for students to better connect with the subject matter on an intercultural level. While the entire course successfully implemented several high impact practices (HIP), this chapter aims to provide a concise overview of these methods before differing to a more microanalysis; focusing on an integrated, preventing visual plagiarism workshop, which leveraged global knowledge, active learning, and collaborative discourse to facilitate improved academic integrity among the student body. The workshop engaged students by posing ethically driven questions through active learning exercises, such as case study discussions and reflective making activities, to open dialogues and encourage debate on various, and often opposing, ethical perspectives. The overarching objective of this workshop was for students to develop best practice ethical frameworks to subsequently inform and underpin their creative practice, both within higher education and in a professional industry context.

Central to the entire discipline of construction safety management is the concept of construction accidents. Although distinctive progress has been made in safety management applications over the last decades, construction industry still accounts for a considerable percentage of all workplace fatalities across the world. This study aims to predict occupational accident outcomes based on national data using machine learning (ML) methods coupled with several resampling strategies.

Occupational accident dataset recorded in Turkey was collected. To deal with the class imbalance issue between the number of nonfatal and fatal accidents, the dataset was pre-processed with random under-sampling (RUS), random over-sampling (ROS) and synthetic minority over-sampling technique (SMOTE). In addition, random forest (RF), Naïve Bayes (NB), K-Nearest neighbor (KNN) and artificial neural networks (ANNs) were employed as ML methods to predict accident outcomes.

The results highlighted that the RF outperformed other methods when the dataset was preprocessed with RUS. The permutation importance results obtained through the RF exhibited that the number of past accidents in the company, worker's age, material used, number of workers in the company, accident year, and time of the accident were the most significant attributes.

The proposed framework can be used in construction sites on a monthly-basis to detect workers who have a high probability to experience fatal accidents, which can be a valuable decision-making input for safety professionals to reduce the number of fatal accidents.

Practitioners and occupational health and safety (OHS) departments of construction firms can focus on the most important attributes identified by analysis results to enhance the workers' quality of life and well-being.

The literature on accident outcome predictions is limited in terms of dealing with imbalanced dataset through integrated resampling techniques and ML methods in the construction safety domain. A novel utilization plan was proposed and enhanced by the analysis results.

This study aims to explore the transformative potential of integrating artificial intelligence (AI) and the metaverse into academic libraries, envisioning a future where personalized, immersive and accessible user experiences redefine the traditional concept of libraries. The purpose is to analyse the synergy between AI and the metaverse in enhancing library services and user interactions.

The study uses a literature review approach to synthesize current knowledge on AI, the metaverse and their integration into academic libraries. It delves into the functionalities of AI in cataloguing, personalization and predictive analysis, coupled with the immersive experiences offered by the metaverse. The design envisions metaverse-infused academic libraries as digital spaces hosting AI-driven virtual assistants, personalized learning paths and collaborative environments.

The integration of AI and the metaverse in academic libraries presents opportunities for personalized learning experiences, efficient resource management and global collaboration. The findings suggest that the synergy enhances accessibility, inclusivity and efficiency in library services, albeit with challenges such as the digital divide, privacy concerns and technical complexities.

This study contributes to the discourse on the future of academic libraries by proposing a comprehensive vision of metaverse-infused libraries empowered by AI. It underscores the originality and value of the integration, emphasizing the potential for personalized, interactive and globally accessible learning and research environments.

The study aims to examine how organizational socialization occurs for interns transitioning from onsite to telecommuting work, particularly in a context where traditional supports have been reduced due to the pandemic.

Drawing from interviews (n = 22) of undergraduates interning at advertorial and marketing firms, the study conducted a thematic analysis of workplace learning experiences of undergraduate interns─newcomers at the workplace when disruption of traditional ways of performing work activities occurred. In particular, the enforced telecommuting work-from-home arrangements due to the pandemic provided a unique setting for this study of internship learning in changing contexts. The analyses reveal differences in undergraduate interns’ experiences of organizational socialization when they were at the physical workplace as compared to when they had to work remotely.

Interns reported benefitting from structured onboarding, supportive peer systems, and regular face-to-face meetings with supervisors, which facilitated their socialization and understanding of workplace culture before the pandemic. However, as telecommuting became the norm during the pandemic, these experiences shifted. Interns adapted by engaging in digital interactions to mirror office dynamics, extending work hours due to blurred work-life boundaries, and independently seeking information in the absence of direct guidance. When adapting to digital communication and independent learning, interns faced challenges like longer working hours and reduced spontaneous interactions, indicating a preference for the traditional, in-person socialization methods of the pre-pandemic workplace.

This study provides insight into interns’ experiences during the global shift to hybrid work as a result of the pandemic, contributing fresh insights into organizational socialization processes amidst workplace disruptions. The conclusions offer valuable implications for future adaptive onboarding practices in educational and professional settings.

From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries.

This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development.

ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals.

This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.

To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.

The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.

The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

This study aims to explore vocational teachers' perceptions regarding workplace learning that align with students' training models and collaborative teaching involving specialised professionals within the context of industry-university collaboration.

Using a qualitative approach, the study conducted nine semi-structured interviews from three subject areas to better understand how vocational teachers’ work-based learning enhances their pedagogical practice in guiding students’ professional training. Thematic analysis was adopted to identify patterns that emerged from concepts and theories related to coding categories.

The authors identified three key components: vocational teachers’ workplace learning in connection with students’ training models, collaborative teaching with specialised professionals and teachers’ regular interactions with enterprises. The findings demonstrate that vocational teachers’ engagement in workplace learning pertaining to specific subjects provides a valuable avenue for enhancing curriculum design with collaboration with industry experts. This is key for supporting vocational students’ transitions into the workplace and ensuring their knowledge and skills are tailored to the industry-standard practice.

The data are limited to the review of interviews from three vocational subject areas as the representative sector in the study. However, this research implies effective knowledge transfer between workplace settings and vocational institutions, and vocational teachers need to integrate work-based vocational knowledge and skills in a relevant and applicable way across diverse classroom settings.

Fostering collaborative partnerships with local industries and professionals can be a primary way to facilitate authentic learning experiences that are linked to a specific vocational field and bridge the gap between diverse classroom learning and real-world work scenarios.

This study combines contemporary workplace learning theories with the conceptual understanding of vocational teachers’ involvement with industry-specific practice. Connecting teachers’ knowledge to the industry extends the input and collaboration from professionals and field experts to the diverse vocational classrooms.

This study aims to examine the impact of environmental scanning on organizational resilience through organizational learning based on organizational information processing theory (OIPT) in Egyptian small and medium-sized enterprises (SMEs) during the COVID-19 pandemic. Furthermore, this study aims to examine the moderating role of environmental uncertainty in this relationship.

The data for the mediation analysis was obtained using a cross-sectional design. Using a self-administered questionnaire, the authors collected data from a sample of 249 Egyptian SMEs. The authors tested the hypotheses using the smart partial least square structural equation modeling approach.

Organizational learning affects organizational resilience. Environmental scanning does not have a direct effect on organizational resilience. However, organizational learning fully mediates the relationship between environmental scanning and organizational resilience. Furthermore, environmental uncertainty does not moderate the indirect relationship between environmental scanning and resilience.

The sample included only Egyptian manufacturing SMEs. The results in the service sector and in other countries may differ. This study was cross-sectional, which was limited in its ability to trace the long-term effects of environmental scanning and organizational learning on organizational resilience.

Egyptian SMEs’ managers should experience organizational learning as a pathway for environmental scanning to build organizational resilience.

To the best of the authors’ knowledge, this study is the first to investigate the role of environmental scanning in building organizational resilience through organizational learning and the moderating role of environmental uncertainty in this relationship.