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This study employs Stein Rokkan's methodological approach to analyse state formation in the Greater Middle East. It develops a conceptual framework distinguishing colonial, populist and democratic pacts, suitable for analysis of state formation and nation-building through to the present period. The framework relies on historical institutionalism. The methodology, however, is Rokkan's. The initial conceptual analysis also specifies differences between European and the Middle Eastern state formation processes. It is followed by a brief and selective discussion of historical preconditions. Next, the method of plotting singular cases into conceptual-typological maps is applied to 20 cases in the Greater Middle East (including Afghanistan, Iran and Turkey). For reasons of space, the empirical analysis is limited to the colonial period (1870s to the end of World War 1). Three typologies are combined into one conceptual-typological map of this period. The vertical left-hand axis provides a composite typology that clarifies cultural-territorial preconditions. The horizontal axis specifies transformations of the region's agrarian class structures since the mid-19th century reforms. The right-hand vertical axis provides a four-layered typology of processes of external intervention. A final section presents selected comparative case reconstructions. To the authors' knowledge, this is the first time such a Rokkan-style conceptual-typological map has been constructed for a non-European region.

The purpose of this chapter is to highlight how universities got into the predicament in which they currently find themselves in, or somewhat planned to be in, in the 2020s. The historical account outlines the purpose of higher education and who it was for throughout the last few centuries, before a more in-depth analysis of the last few decades will highlight how, and why, neoliberal and subsequently managerial aspects of leadership and performance metrics crept into universities before an analysis of the last 5–10 years, including the onset and consequences of COVID, will demonstrate that many ‘COVID recovery plans’ around staff cuts and course reforms were already in place before COVID, but it was COVID that allowed these plans in most cases to be escalated.

This study investigates the impact of large-scale teaching in higher education on students’ preparedness for the workforce within the context of evolving labour market demands, the expansion of higher education and the application of high-impact teaching strategies. It synthesizes perspectives on employer work readiness, the challenges and opportunities of large-scale teaching and strategies for fostering a dynamic academia-industry feedback loop. This multifaceted approach ensures the relevance of curricula and graduates’ preparedness while addressing the skills gap through practical recommendations for aligning teaching methodologies with employer expectations.

The research methodically examines the multifaceted challenges and opportunities inherent in large-scale teaching. It focuses on sustaining student engagement, maintaining educational quality, personalizing learning experiences and cultivating essential soft skills in extensive student cohorts.

This study highlights the critical role of transversal skills in work readiness. It also uncovers that despite its challenges, large-scale teaching presents unique opportunities. The diversity of large student groups mirrors modern workplace complexities, and technological tools aid in personalizing learning experiences. Approaches like peer networking, innovative teaching methods, real-world simulations and collaborative resource utilization enrich education. The importance of experiential learning for augmenting large-scale teaching in honing soft skills is emphasized.

This manuscript contributes to the discourse on large-scale teaching, aligning it with employer expectations and the dynamic requirements of the job market. It offers a nuanced perspective on the challenges and opportunities this educational approach presents, providing insights for crafting engaging and effective learning experiences in large cohorts. The study uniquely integrates experiential learning, co-creation in education and industry-academia feedback loops, underscoring their importance in enhancing student work readiness in large-scale teaching.

Child malnutrition is a grave concern for Pakistan, as the country has one of the highest incidences of child stunting in the developing world. The present study examines the relative significance of parents' education on a child's nutritional status in Pakistan.

For analysis, the study has used data from Phase 7 of the Pakistan Demographic and Health Survey (PDHS) (2017–2018). Since the dependent variable ranges from 0 to 1 (1 indicates not a stunted child, while 0 represents a stunted child), binary logistic regressions are used for the analysis.

The results show that mothers' and fathers' education positively contributes to a child's nutrition. However, mothers' education is considered more significant, especially in the long run. The mother's education categories are positive and significant in the long run, while only their higher education is positive and significant for the father's. Moreover, the magnitude of the effect also shows that the probability of stunting is less if the mothers are educated. The long-run coefficient for mothers' higher education is 0.752, while that of fathers' higher education is only 0.232.

The present study compares the importance of mothers' and fathers' education in child nutrition and concludes that the role of the mother is more crucial for child upbringing. There are rarely any studies that focus on the role of fathers' education in child nutrition and compare whose role, mothers' or fathers,' is more important for child well-being.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2023-0483

In recent decades, higher education institutions (HEIs) have increasingly adopted marketing-oriented approaches. While the adoption of marketing was slower in Europe and Spain, it has become a vital tool for HEIs, both to stay competitive in a changing socio-economic context and to face the challenges posed by the transition to the University 2.0 model. This study aims to analyse the historical evolution of communication techniques used by universities, bringing into focus the relevance of social networks in the most recent decades.

This research methodology consists of two components. Firstly, a comprehensive analysis of the available data is conducted to investigate the earliest marketing and communication actions involving universities, as well as their evolution over time, contextualizing this within the significant shifts in the social, political and technological background. Secondly, a specific focus is placed on the contribution of social media, particularly Twitter, as a powerful tool in creating a university brand and effectively promoting educational institutions, especially during the last stage of this historical evolution. To identify and analyse these trends, Natural Language Processing is used, specifically by leveraging topic modelling techniques.

The results of this analysis offer insights into the evolution of marketing communication applied by Spanish universities and show the increasing importance of social networks and the use of specific topics and contents to enhance their impact on engagement.

This study contributes to the literature by using a novel methodological approach to the research on the historical development of communication in universities in Spain, providing guidance to manage their social media strategy to differentiate themselves, increase engagement and foster brand loyalty.

This study builds on previous theoretical work that considered artificial intelligence (AI) and its potential for creating “teacher-centaurs” whose labor could be accelerated through the use of generative AI (Fassbender, in review). The purpose of this paper is to use empirical methods to study centaur teachers and the division of labor (Durkheim, 1893/2013) that arise from outsourcing teaching tasks to AI.

Multiple case study (Stake, 2006) was used to collect data on two secondary English teachers who were early adopters of generative AI. Data included semi-structured interviews as well as ChatGPT chat logs, which helped in describing how teaching approaches evolved using AI technology.

Results showed that teachers used AI for planning, instruction and assessment. AI-augmented teaching practices allowed teachers to complete tasks with greater speed, which in turn increased stamina and short-term work–life balance. Given the novelty of AI, concerns about data privacy and academic integrity raised ethical questions.

ChatGPT’s rise to popularity in 2023 brought with it significant discussions about education, specifically how students would use AI primarily as a tool for plagiarism. This study takes a different focus, considering how early adoption of AI has begun changing teacher labor, offering implications for the future of the teaching profession.

The study directs attention to the psychological conditions experienced and knowledge management practices leveraged by faculty in higher education institutes (HEIs) to cope with the shift to emergency remote teaching caused by the COVID-19 pandemic. By focusing attention on faculty experiences during this transition, this study aims to examine an under-investigated effect of the pandemic in the Indian context.

Interpretative phenomenological analysis is used to analyze the data gathered in two waves through 40 in-depth interviews with 20 faculty members based in India over a year. The data were analyzed deductively using Kahn’s framework of engagement and robust coding protocols.

Eight subthemes across three psychological conditions (meaningfulness, availability and safety) were developed to discourse faculty experiences and challenges with emergency remote teaching related to their learning, identity, leveraged resources and support received from their employing educational institutes. The findings also present the coping strategies and knowledge management-related practices that the faculty used to adjust to each discussed challenge.

The study uses a longitudinal design and phenomenology as the analytical method, which offers a significant methodological contribution to the extant literature. Further, the study’s use of Kahn’s model to examine the faculty members’ transitions to emergency remote teaching in India offers novel insights into the COVID-19 pandemic’s effect on educational institutes in an under-investigated context.

Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.

As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.

The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.