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The serious social, economic and environmental problems facing humanity require inspiring and proactive leaders. Indeed, achieving the Sustainable Development Goals (SDGs) in 2030 requires people who drive the transformation and empowerment of their communities. In this context, educational leaders are called to play a key role, since education is one of the main ways to guarantee the well-being and prosperity of humanity. Among other aspects, they must ensure the necessary organizational conditions in the institution to generate a shared vision committed to sustainability, as well as align curricular practices and the institutional approach with the principles of Education for Sustainable Development (ESD).
The 21st century is the era in which most educational technologies are created; Emerging technologies have been promising innovations in teaching and learning for decades. At the same time, inclusive education aims to create equal opportunities for all, which can sometimes be counterintuitive to individual learning needs and experiences, especially when it comes to students with specific needs. While assistive technologies have been extremely useful for specific types of disabilities in the context of special education, this is not always the case when it comes to educational technologies (tools and conceptual frameworks), which are sometimes too generic and not easily applied to specific contexts. On the other hand, current technological advances, such as generative artificial intelligence, although they can provide new opportunities and avenues for research and development, if not problematized or theorized enough, they could even increase existing gaps and favor different types of exclusion.
Artificial intelligence (AI) has received a lot of attention as education systems are increasingly affected by rapid technological changes. We invite proposals that examine the dynamic interaction between human educators, students, and artificial intelligence systems in contemporary educational spaces. Recent advances in AI have fundamentally altered the educational landscape, creating new forms of cognitive extension and pedagogical possibility. This transformation requires rigorous transdisciplinary research into how AI systems, human actors, and learning environments merge into dynamic educational ensembles. We seek contributions that explore these emerging phenomena through multiple theoretical lenses and methodological approaches. As AI systems become increasingly sophisticated anthropomorphic participants in educational processes, we must reconceptualize traditional teaching and learning frameworks. This requires examining how cognition is distributed across human-AI networks, how agency emerges from these interactions, and how educational spaces are transformed by these new configurations. Critical questions arise about the nature of knowledge construction, the role of embodied experience, and the ethical implications of AI-mediated learning. AI will have an exponential impact on pedagogy and also has broader implications that may require thoughtful policy reviews. We seek transdisciplinary perspectives to facilitate a fertile dialogue on the transformative impact of AI in education and business, as these fields are uniquely intertwined. Many questions that can be addressed include: • How will AI simulations be designed and experimented across various disciplines? • How can teachers/lecturers be encouraged to embrace gamification without compromising student engagement? • What will be the role of AI in the fundamental learning, communication and cognitive development of students? • Will there be effects on embodied cognition and experiential learning? What are they? • How should educational governance units respond to the integration of AI in educational and business environments and address data privacy issues? • What are the ethical challenges of AI in business education and how can AI be incorporated effectively to create workforce-ready business graduates with a foundation in business ethics? • How do companies mitigate the potential for misleading marketing in an AI world, while maintaining profitability? • What are the concerns about the possible centralization of power of educational technology companies? • How can educational leaders ensure that AI prevents educational inequalities and biases rather than encouraging them? • What is the exponential impact of AI on pedagogy? • What are the opportunities and potential frameworks for AI's potential to personalize learning and enhance interactive dynamics? • What are the risks of diminishing human roles in education? For this collection of articles, we sought AI-focused manuscripts that address some of these questions. We are particularly interested in addressing AI at all three levels of multi-tiered systems of supports (MTSS) in terms of academic, social, emotional and behavioral development. For this research topic, we welcome a breadth of research from a wide range of contexts, perspectives, and methodological approaches to provide further research possibilities on innovative, collaborative, and ethical classroom AI practices for teachers.
Knowledge and technology transfer (KTT) between scientific organizations and firms takes a central role in driving technological progress, growth, and societal transformations (Robin and Schubert, 2013; Maietta, 2015; Schubert and Kroll, 2016; Giannopoullo et al., 2019; Schubert et al. 2023) Conceptual and qualitatively empirical research on KTT has identified a large number of channels by which knowledge from science is transferred to industry and vice versa. Yet, despite the great economic and societal relevance of KTT, quantitative assessments have in large numbers focused onlimited sets of the identified channels where the selection is typically guided by the specifics of the available data rather than theoretical considerations. For example, historically many studies focused on patents as an indicator for KTT between science and industry (e.g. Jaffe 1989;Mowery and Ziedonis, 2002;Crespi et al. 2011; Lawson 2013), whereas more recently researchers have concentrated on joint publications(e.g.; Calvert and Patel, 2003; Giunta et al. 2016; Krieger et al. 2022; Arora et al. 2023).Survey analyseshave also been used to provide insights on the importance of different channels of KTT (e.g.; Mansfield, 1995;Arvanitis et al., 2008; Scandura, 2016). However,gatheringcomprehensive evidence on KTTat a large scale remainschallenging due to the tacitness of KTT, which is only partially reflected in generally available co-patenting or co-publication indicators.The lack of data that is both broadly available and encompasses measuresof KTT holistically not only slowed down conceptual progress on KTT. It has also made informed decision-making science organizations on matters related to KTT difficult. Thus, there is a dire need to leverage new data sources beyond the already existing but limited sources. In recent years, with progress in machine learning, natural language processing, and big data approaches, new ways of creating KTT indicators research haves moved to make use of unstructured data sources (e.g., Arora et al., 2016;Abbasiharofteh et al., 2024; Abbasiharofteh et al., 2023). However, as handling this data effectively is often challenging, the research in this field is still in an early stage. Moreover, it is still unclear how these kinds of data sources coming with their own challenges in particular concerning reliability issues in particular in scaled contexts (Hajikhani et al, 2023) would affect or maybe even invalidate results. Recent work has explored the use of large language models for innovation analyses (e.g., Pelaez et al., 2024). However, while AI-based methods can help leverage a large reservoir of so far unstructured data sources, it is not clear whether the resulting data is of practical use for researchers or decision-making in KTT. This special issue invites contributions that leverage these innovative data sources and methods in KTT research. Submissions to this special issue should seek to make a dual contribution, which should on the one hand leverage new data sources or methodologies (e.g. LLMs, webscraping, machine learning) while critically examining both chances and pitfalls. Simultaneously, submissions should make a strong contribution to further research on KTT and should offer a potential to support practical decision-making in KTT. While purely data or methods-driven submissions are not in focus of this call, the potential array of thematic approaches and practical applications to decision-making is large: Leveraging new methods and data: How can AI-enabled methods, such as large language models or machine learning be applied to measure KTT more holistically? How can reliability issues be managed? Where are the limitationsand where are the biggest potentials? How can unconventional data sources and methods, such as large-scale textual data from news items and Twitter, be employed to map the geographies of knowledge production and knowledge relations? Can natural language processing enhance existing indicator approaches based on e.g. trademarks, patents, or publications? Under which conditions can specialized surveys provide meaningful information for research/decision-making in KTT? Charting KTT: What are the most common KTT channels? Are there differences between firms and academic organizations? What are the dynamics in the prevalence of different KTT channels during the last decades? Has the prevalence of different channels changed? Measuring the value of KTT: Which KTT mechanisms deliver the greatest (economic or societal) impact? What is the contribution of different KTT channels to the novelty of scientific or industrial discoveries? What is the contribution of different KTT channels on the diffusion of discoveries among scientists, industry, and society? What are the unintended, negative effects of KTT? How can they be measured? Does KTT strengthen or weaken the other missions, notably research and teaching? Organizational management of and decision-making on KTT: How can high-impact KTT be incentivized both in firms and academic organizations? How can resources between KTT and the other scientific missions be allocated? What is the potential of KTT to enhance decision-making and planning for sustainable transitions in policy circles?
The personalization of learning has emerged as a central theme in both educational research and pedagogical practice. The challenge of addressing the individual needs of students becomes evident in a traditional model, limiting the possibility of offering personalized support from teachers. This context has driven the development of approaches and tools that seek to adapt the teaching-learning process to individual characteristics, rhythms and motivations, especially highlighting the role of educational technology as a key facilitator in this transformation. The problem arises in relation to; how to address different needs, interests, abilities, cognitive styles and learning styles. Throughout history, multiple efforts have been developed to use technology as a means to promote the personalization of learning. The incorporation of technological tools in this area seeks to adapt educational processes to the individual needs and characteristics of the students, approaching this task from various perspectives. These include the design of algorithms capable of responding to individual differences, the implementation of learning analytics, the use of educational recommendation systems, the integration of chatbots and, more recently, the application of technologies based on generative artificial intelligence. These innovations represent a significant advance in the search for a more equitable and personalized education, supported by the transformative potential of technology.
Institutions and innovation: An international comparative perspective In this special issue, we are seeking studies that examine the effects of institutions on innovation drawing on an international comparative perspective. Papers, which examine the roles played by various institutions (including formal and informal institutions) in the cross-national diversities in innovation (all types of innovation) at either the country or firm level, are welcomed. Innovations are of importance to both firms and countries; yet, there are significant differences in innovation among countries, existing at both the country and firm levels (e.g., Deng et al., 2023; Janger et al., 2017; Nam et al., 2014; Sharma et al., 2022). At the country-level, countries exhibit high variations in innovation quality, quantity, type, mode, and others (Cinar et al., 2022; Wang, 2018). For instance, given that the population skews toward “base of the pyramid” consumers in developing countries, frugal innovation, which reflects affordable innovation, emerges as a novel type of innovation which does not exist in developed countries (Adomako et al., 2023; Lim and Fujimoto, 2019). At the firm-level, one firm often undertakes different innovation behavior in different countries (Castellani & Lavoratori, 2020; Liu and Li, 2022). For instance, a number of multinational companies develop new products at their R&D centers around their headquarters, whereas their R&D centers in other countries mainly focus on product localization (Belderbos et al., 2023; Berry, 2014). Accordingly, a serious question emerges: what cause the differences in innovation among countries? Institutions matter. Specifically, as humanly devised constraints to shape the “rule of game”, institutions include formal and informal ones (North, 1990). Formal institutions are written rules devised by the government and other authoritative bodies, such as laws and political regulations; informal institutions refer to unwritten norms that are embedded in culture and ideology, such as conventions, customs, and traditions (Su, 2021). They offer “a framework for collective actions and furnish an alternative mechanism in enforcing the rules of the game and facilitating transactions between economic actors” (Nee, 1998, p. 85). By reducing uncertainty, setting decision guidelines, and offering meaning (Su, 2021), institutions profoundly affect various behavior and outcomes at differing levels (e.g., Bao et al., 2020; Chowdhury et al., 2019; Fan et al., 2023; Fuentelsaz et al., 2020). Studies have reported that institutions play critical roles in innovation (Nam et al., 2014; Sharma et al., 2022; Uzuegbunam and Geringer, 2021). Thus, institutions may serve as key sources of the differences in innovation among countries (Nam et al., 2014; Wang, 2018). However, we lack knowledge on the roles that institutions play in generating the differences in innovation among countries. This is primarily due to this issue lies at the intersection of three research domains: institution, innovation, and international business. While scholars have paid attention to the intersection of such domains, especially following the special issue of Journal of International Management that “Emerging Market Firm Competitiveness: Internationalization, Innovation and Institutions (3Is)” (Kumar et al., 2013), most of them focused on the intersection of either two domains rather than all of three (Donges et al., 2023). Hence, little is known about how institutions lead to the differences in innovation among countries (Nam et al., 2014; Sharma et al., 2022). But, understanding this question is important, because it not only directs toward a novel research area and develops an international comparative perspective that theoretically contributes to institution, innovation, and international business domains, but also helps firms better develop cross-national border innovations and can guide countries facilitate innovations more effectively. Thus, we organize this special issue.