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ChatGPT’s intuitive interface and instant feedback can either support learning or enable superficial task completion, depending on how it is integrated into academic work. While generative AI has rapidly entered higher education, much

ChatGPT’s intuitive interface and instant feedback can either support learning or enable superficial task completion, depending on how it is integrated into academic work. While generative AI has rapidly entered higher education, much existing guidance focuses on policy and academic integrity rather than explaining how AI use interacts with established mechanisms of learning. This conceptual paper contributes to this discussion by developing an explanatory framework for students’ ChatGPT use grounded in foundational learning theories. Drawing on cognitive load theory and goal orientation theory and incorporating self-efficacy and task relevance as mediating constructs, the study undertakes a theory-driven synthesis of four perspectives relevant to how students engage with complex academic tasks. The framework brings these perspectives together to clarify how cognitive demands, motivational orientations, perceived competence, and perceived task value may shape students’ decisions about when and how to rely on generative AI tools during academic work. The resulting framework illustrates how these mechanisms may influence whether generative AI is used as a scaffold supporting thinking and knowledge construction or as a shortcut that bypasses essential cognitive processes. The article concludes by illustrating how the framework may inform instructional design that aligns AI use with meaningful engagement in disciplinary tasks in higher education.

With the rise of GenAI, business schools must prepare students for a future workforce integrated with this technology. This paper details a case study from a UK business school that introduced GenAI into its curriculum for first-year

With the rise of GenAI, business schools must prepare students for a future workforce integrated with this technology. This paper details a case study from a UK business school that introduced GenAI into its curriculum for first-year undergraduates. Through a mixed-methods survey of 149 students, the study gathered feedback on newly implemented AI-focused learning activities. The findings reveal that students are highly engaged and recognise the necessity of developing GenAI skills for their future careers. Key areas of interest include the real-world business applications of AI, ethical considerations, and future technological advancements. A significant concern for students is the desire to use GenAI in their academic work without unintentionally committing academic misconduct. Based on student feedback and existing research, this study presents a new conceptual framework for integrating GenAI education throughout business degree programmes. It offers practical guidance for educators, including a scaffolded approach to learning activities and assessments across all undergraduate levels. This research contributes an innovative model for applied GenAI learning in business education, adding to the growing body of literature on student AI literacy.

This study aims to systematically map the evolving landscape of artificial intelligence (AI) integration into business education, highlighting dominant research themes, emerging gaps, and future research directions. Adopting a bibliometric

This study aims to systematically map the evolving landscape of artificial intelligence (AI) integration into business education, highlighting dominant research themes, emerging gaps, and future research directions. Adopting a bibliometric analysis framework, the study analyzed 213 peer-reviewed journal articles published between 2015 and 2024, extracted from the Dimensions database. Bibliographic coupling and co-word analysis, facilitated through VOSviewer, were employed to uncover intellectual structures, thematic clusters, and conceptual linkages within the field. The results identified four major research clusters: AI-driven business education transformation, innovative digital pedagogies, AI-enhanced personalization of learning, and business education aligned with the digital economy. Co-word analysis further revealed three conceptual trends: technological transformation, integration of generative AI tools, and advancements in educational quality. Persistent gaps emerged concerning curriculum coherence, educator readiness, and assessment validity. This study offers actionable guidance for educators, curriculum developers, and policymakers to design AI-integrated, ethically grounded, and adaptable business education models that meet the demands of an AI-driven global economy. This study provides a comprehensive, empirical foundation for future research agendas and strategic educational reforms

This conceptual paper aims to identify the characteristics of Design-Based Research (DBR) projects that support the sustainable, expansive or adaptive implementation of technological learning innovations in higher education and thus

This conceptual paper aims to identify the characteristics of Design-Based Research (DBR) projects that support the sustainable, expansive or adaptive implementation of technological learning innovations in higher education and thus address this gap in the literature. Four examples of Design-Based Research implemented in different discipline contexts through technology enhanced learning interventions in higher education are retrospectively analysed and compared. The higher education discipline contexts of the projects include health education, music performance, biomedical engineering, electrical and mechanical engineering. The analysis uses a shared framework based upon identifying five characteristics of design research and activity theory. Shared characteristics include addressing an identified pedagogical problem or innovation through iteratively designing and evaluating an intervention that enhances learning while developing theoretical insights. Transdisciplinary collaboration enhances the transferability of design principles derived from these projects into wider contexts. Activity Theory analysis is utilised to identify context-specific contradictions and strengths in each example. These four longitudinal project designs illustrate how Design-Based Research can be combined with Activity Theory and practically implemented as a transferable educational design framework to generate impact on teaching and learning that is either sustained, expansive or adaptive.

This paper critically evaluates the third prototype design iteration of an immersive reality (XR) enhanced simulation project in healthcare higher education. Drawing from design principles developed since the project’s inception in 2016

This paper critically evaluates the third prototype design iteration of an immersive reality (XR) enhanced simulation project in healthcare higher education. Drawing from design principles developed since the project’s inception in 2016, the research addresses a gap in current literature: whilst XR has demonstrated positive impacts on clinical skill development, examples of longitudinal studies and engagement with learning theory to inform the design of these environments remain limited. The MESH360 project applies design-based research (DBR) to explore the impact of XR-enhanced simulation for training novice and professional paramedics, triangulating participant feedback, observation, and biometric data. The resulting XR design principles provide a framework for transferable learning designs in high-risk, practice-based environments.