The full impact of artificial intelligence tools (e.g., GPT-3, ChatGPT, DALL-E) on teaching, learning, and assessment is evolving rapidly. By extension, questions arise about the ethical implications of artificial intelligence tools used for writing, coding, fine arts, and other educational applications. In this thematic collection, we welcome papers regarding the impact of artificial intelligence on academic integrity. We define academic integrity broadly, including but not limited to student conduct, ethical teaching, ethical feedback and assessment, and the ethical application and development of new technologies for learning.
Children & Schools invites submissions for a special issue dedicated to exploring innovative school-based interventions and programs that address the structural determinants of health (SDOH), with a particular focus on tackling structural racism and going beyond individual-level interventions to examine the root causes of health disparities in educational settings. We welcome original research articles, systematic reviews, case studies, and conceptual papers that address topics such as school-based interventions that directly confront structural racism and its impact on student health outcomes; collaborative models between schools, systems, and community organizations to confront structural health inequities; school-based programs that address intergenerational trauma and historical injustices; and the role of school social workers and health professionals in addressing structural barriers to health equity. We particularly encourage submissions that employ rigorous quantitative, qualitative, or mixed-methods approaches; present innovative theoretical frameworks for understanding SDOH in educational contexts; offer practical implications for educators, school health professionals, policymakers, and community partners; highlight the voices and perspectives of students, families, and communities most affected by structural health inequities; or address the long-term impacts of SDOH interventions
We welcome submissions for this special collection of articles that explore development of academic identity among early career academics in or across higher education systems worldwide. The early career phase is critical in developing academic identity. Conceptually, identity is "a continuing sense of self through a whole human life, in which there may have been significant, even dramatic, changes, but the past, present, and future are integrally linked" (Henkel, 2012, p. 156). Academic identity, a subset of self-identity, can be described as a professional identity and shares common traits with it (Henkel, 2000). Those in the early career phase include doctoral and postdoctoral researchers who aspire to work in higher education in a longer term and academics who are working as lecturers/assistant professors (or other equivalent roles). The development of academic identity among early career academics may be influenced by a confluence of disciplinary, institutional, and societal factors (Dai & Hardy, 2023; Henkel, 2005; McAlpine et al., 2009). Early career academics often encounter a variety of challenges, including managing the competing demands and expectations of their academic roles. They may experience inherent tensions: within themselves, for example, constant self-negotiation between who they want to be and who are ‘currently being’ to cope with the professional odds; between individuals’ professional aspirations and their institutional requirements; and with the societal and neoliberal demands. We welcome contributions that centrally address, but are not limited to, the following high-level questions: How do early career academics address the challenges they face in their academic fields? How do external factors such as governmental, institutional, and disciplinary influence the academic identity of early career academics? How do social-cultural contexts influence the academic identity development of early career academics within the academic community? We are open to submissions that offer theoretical and/or empirical insights to advance understanding of the issues on academic identity development. We hope that these collective insights will support early career academics’ process of identity development and inform university management to formulate and implement more effective support policies and structures for this group of academics in a rapid changing academic landscape. Challenges in academic identity development; Role of contextual factors (social context, governmental, institutional, disciplinary) in shaping academic identity; Impact of neoliberalism and related issues; Academic identity development in transnational context; Management and governance in academic identity development
Intelligent agents, defined as systems or programs capable of autonomous execution and decision-making without direct human intervention (Kijima, 1996), have evolved significantly with the advancement of artificial intelligence (AI). The emergence of generative artificial intelligence has not only boosted the capacities of individual intelligent agent (Koraishi, 2023; Hu et al., 2024), but also promoted the evolution of multi-agent networks or ecosystems. These multi-agent settings represent a collective of autonomous entities that collaborate within a shared environment, opening up diverse applications across multiple domains, including education. The potential of multiple intelligent agents in education goes beyond simply enhancing individual learning experiences; they represent a profound shift in how education can be delivered, optimized, and understood. In multiple intelligent agent settings, educational environments can be significantly optimized and re-imaged. At the individual level, these agents can deliver highly personalized learning experiences by continuously analyzing real-time data, monitoring student progress, and dynamically adjusting instructional strategies. Acting as personalized tutors, assistants, and learning partners (Kim & Baylor, 2016), multiple intelligent agents can significantly reduce achievement gaps and elevate overall educational outcomes by providing targeted support that evolves with the learner (Chiquet et al., 2023; Martha & Santoso, 2019; Tegos & Demetriadis, 2017). Recent advancements of multiple intelligent agents, especially those utilizing large language models (LLMs), have demonstrated their potential in areas requiring sophisticated decision-making and real-time adaptation. For instance, multiple intelligent agents based on LLMs can assume specific roles such as companions, assistants, or mentors to facilitate learning interactions (Nguyen, 2023). They can also dynamically adjust curricula, optimize resource allocation (such as teacher time and learning materials), and then teach these materials in the most appropriate form and pace according to the students' backgrounds and learning capabilities. As multiple intelligent agents become more sophisticated, they are transforming learning methods and processes, significantly influencing human cognition. They facilitate personalized learning paths, adapt to real-time data, and create engaging, interactive educational experiences. These multiple intelligent agents can identify at-risk students, detect emotional states, and recommend appropriate learning activities, thereby enhancing the overall learning experience and outcomes. On a broader scale, multiple intelligent agents facilitate collaborative learning experiences that mirror real-world social interactions, preparing students for the interconnected, team-oriented nature of the modern workforce. For instance, assigning fictional yet potentially future societal roles to multi-agents anticipates social evolution (Wang et al., 2024). In educational contexts, these settings have the capacity to optimize entire educational ecosystems by coordinating the actions of multiple intelligent agents to manage resource allocation, instructional content, and the structure of learning environments. They are not merely tools for enhancing education but are pivotal in architecting new educational paradigms. As these intelligent agents continue to evolve, the synergy among multiple agents is set to propel research from traditional social sciences to the new domain of AI-driven social sciences (Xu et al., 2024). This shift drives significant advancements in the conceptualization, delivery, and study of education, leading to learning environments that are more personalized, adaptive, and equitable. This special issue aims to gather high-quality research papers that explore not only the innovative applications of multiple intelligent agents in learning environments but also their design and theoretical foundations. We seek contributions that demonstrate how multiple intelligent agents can be used to personalize learning, adapt to individual student needs, foster collaboration, and provide real-time feedback. Additionally, we welcome papers that delve into the design and development of multiple intelligent agents, as well as theoretical studies that advance our understanding of these systems. By advancing the understanding and application of multiple intelligent agents in education, we aim to contribute to the development of more adaptive, personalized, and effective learning environments. ➢ Theories and models of multiple intelligent agents in education and human learning. ➢ Design and development of multiple intelligent agents in education and human learning. ➢ Adaptive learning environments using multiple intelligent agents. ➢ Personalized learning paths facilitated by multiple intelligent agents.