Artificial intelligence and organisational change: a social complexity perspective

Teacher education students play double role as present learners and future educators. Hence, they need targeted training to navigate the growing influence of Generative Artificial Intelligence (GenAI) on teaching, learning and professional identity. However, existing artificial intelligence (AI) literacy programmes predominantly emphasize technical AI knowledge and pre‐GenAI tools or are offered by GenAI platforms that focus on their own technologies' features, thereby lacking pedagogically structured frameworks that address non‐technical dimensions such as ethics, contextual understanding and the development of human‐centered, critical and lifelong learning mindsets to adapt to rapid technology changes. Furthermore, limitation of time in teacher education syllabus and faculty's lack of AI literacy require targeted intervention. This design‐based research (DBR) aims to fill this gap by developing and evaluating a set of design principles for GenAI literacy training in teacher education. Integrating contemporary AI competency frameworks for learners and teachers, the study implements these principles in a workshop that serves as the research prototype. The prototype was first piloted with 14 master's students and then evaluated with 29 teacher education (TE) students. Results show significant gains in participants' AI competence self‐efficacy, attitudes towards GenAI and commitment to critical, ethical and pedagogical engagement with GenAI tools. The findings highlight the need for teacher education programmes to integrate GenAI literacy that supports teachers' evolving roles as reflective practitioners, co‐creators and lifelong learners in an AI‐driven world. Practitioner notes What is already known about this topic Students' self‐efficacy and mindset influence their adoption of GenAI in learning and teaching. GenAI competency is essential for teacher education students, encompassing technological, ethical and sociocultural dimensions. Implementing AI literacy training faces challenges such as educators' limited AI literacy, the lack of pedagogical frameworks and the need to integrate technical knowledge with ethical reasoning, self‐regulated learning and metacognitive skills. Transformative learning is crucial in preparing teacher education students as co‐creators and facilitators in AI‐enhanced education. What this paper adds This study presents the design and evaluation of an AI competency training programme to enhance teacher education students' self‐efficacy, critical and human‐centered AI mindsets and human–AI interaction skills. Using design‐based research, it identifies key principles for AI literacy training, demonstrating how transformative, active and experiential learning approaches can prepare teacher education students for evolving roles in AI‐enhanced education. Implications for practice and/or policy Educational technologists can apply the proposed design principles to develop GenAI tools and AI systems that enhance AI competency and self‐regulated learning (SRL). Educators can integrate these AI training principles into their teaching strategies and enhance their task assignments and assessments to foster deep learning, self‐regulated learning and metacognitive skills in the age of GenAI. Policymakers should update teacher education curriculum to include AI competency, strengthen content and pedagogical knowledge, especially in social emotional learning, formative and authentic assessment methods and establish guidelines for selecting GenAI tools that support human intelligence and mitigate over‐reliance.

Purpose Generative Artificial Intelligence (GenAI) is rapidly transforming business strategy, innovation processes and governance practices. While prior research has focused primarily on technological implementation and performance outcomes, limited attention has been paid to how senior executives interpret, frame and adapt to GenAI as a strategic and ethical phenomenon. This study aims to explore executives’ sensemaking and adaptive responses to GenAI and to develop a conceptual model that captures this process. Design/methodology/approach The study adopts a qualitative exploratory design based on semi-structured interviews with 15 senior executives from diverse industries in Greece. Data were analyzed using thematic analysis to identify recurring patterns in executives’ perceptions, decision rationales and ethical considerations related to GenAI adoption and integration. Findings The analysis reveals four interrelated dimensions shaping executive adaptation: strategic integration and decision-making, business value and innovation, human–AI collaboration and workforce transformation and ethics, governance and adoption barriers. Cross-thematic synthesis indicates that executives perceive GenAI as a decision-support and innovation amplifier rather than an autonomous decision-maker. These findings inform an Emergent Integrative Model of Executive Adaptation to GenAI, conceptualized as a dynamic cycle comprising strategic sensemaking (Curate), operational experimentation (Create) and ethical consolidation (Consolidate). Research limitations/implications Several executives cited technical and resource limitations as obstacles to effective GenAI implementation. Challenges include legacy systems, lack of skilled artificial intelligence (AI) engineers and limited integration between AI platforms and enterprise software. Practical implications This model advances understanding of executive cognition and adaptive intelligence in the AI era, positioning leadership as a process of continuous learning, sensemaking and ethical stewardship. Practically, the research offers a roadmap for organizations and policymakers for aligning GenAI-driven innovation with responsible governance and leadership development. Social implications By highlighting the role of executive stewardship, the study underscores how ethical leadership in GenAI adoption influences public trust, workforce well-being and organizational legitimacy. Originality/value Existing research on AI in business has predominantly focused on technological implementation, efficiency gains and economic outcomes. Studies emphasize measurable benefits such as productivity enhancement, improved decision-making speed and customer experience optimization. However, fewer studies explore how executive cognition and strategic reasoning shape the trajectory of AI adoption – particularly regarding GenAI technologies that introduce new forms of creative automation. The study advances leadership and sensemaking research by shifting the focus from GenAI adoption outcomes to executive cognition and ethical stewardship. It offers a novel integrative model that explains how strategy, innovation and governance co-evolve in the GenAI era.

Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.

Technologically advanced artificial intelligence (AI) is making a significant contribution to strengthening national security. AI algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making. Artificial intelligence and machine learning (AI/ML) are crucial for state and integrated hybrid attacks and protecting new threats in cyberspace. Existing AI capabilities have significant potential to impact national security by leveraging existing machine learning technology for automation in labor-intensive activities such as satellite imagery analysis and defense against cyber attacks. This article examines selected aspects of the impact of artificial intelligence on enhancing a state’s ability to protect its interests and its citizens., artificial intelligence through the use of neutron networks, predictive analytics and machine learning algorithms enables security agencies to analyse vast amounts of data and identify patterns indicative of potential threats. Integrating artificial intelligence into surveillance, border control and threat assessment systems enhances the ability to respond preemptively to security challenges. In addition, artificial intelligence algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making by police authorities. The rapid development of AI raises a number of questions for its use in securing not only national security but protecting all citizens. In particular, it is worth answering the question How does artificial intelligence affect national security and clarifying the issue of how law enforcement agencies can use artificial intelligence to maximise the benefits of the new technology in terms of security and protecting communities from rising crime. The analysis is based on a descriptive method in describing the phenomenon; by explaining the concepts and applications of artificial intelligence to determine its role in the national security sphere. An analysis of the usefulness of artificial intelligence in particular in police operations is undertaken, with the aim of defending the thesis that, despite some threats to the protection of human rights from AI, it is becoming the best tool in the fight against all types of crime in the country. Technological advances in AI can also have many positive effects for law enforcement, and useful for law enforcement agencies, for example in facilitating the identification of persons or vehicles, predicting trends in criminal activities, tracking illegal criminal activities or illegal money flows, flagging and responding to fake news. Artificial intelligence (AI) has emerged as one of the biggest threats to information security, but efforts are being made to mitigate this new threat, but also to find solutions on how AI can become an ally in the fight against cyber-security, crime and terrorist threats. Artificial intelligence algorithms search huge datasets of communication traffic, satellite images and social media posts to identify potential cyber security threats, terrorist activities and organized crime. It is advisable, when analyzing the opportunities and threats that AI poses to national and public security, to gain a strategic advantage in the context of rapid technological change and also to manage the many risks associated with AI. The conclusion highlights the impact of AI on national security, creating a range of new opportunities coupled with challenges that government agencies should be prepared for in addressing ethical and security dilemmas. Furthermore, AI improves predictive analytics, thereby enabling security agencies to more accurately anticipate potential threats and enhance their preparedness by identifying vulnerabilities in the national security infrastructure

The current debate on artificial intelligence (AI) tends to associate AI imaginaries with the vision of a future technology capable of emulating or surpassing human intelligence. This article advocates for a more nuanced analysis of AI imaginaries, distinguishing “strong AI narratives,” i.e., narratives that envision futurable AI technologies that are virtually indistinguishable from humans, from "weak" AI narratives, i.e., narratives that discuss and make sense of the functioning and implications of existing AI technologies. Drawing on the academic literature on AI narratives and imaginaries and examining examples drawn from the debate on Large Language Models and public policy, we underscore the critical role and interplay of weak and strong AI across public/private and fictional/non-fictional discourses. The resulting analytical framework aims to empower approaches that are more sensitive to the heterogeneity of AI narratives while also advocating normalising AI narratives, i.e., positioning weak AI narratives more firmly at the center stage of public debates about emerging technologies.

Generative artificial intelligence in dentistry: A narrative review of current approaches and future challenges

As organizations increasingly navigate the complexities of the digital age, the incorporation of artificial intelligence (AI) into knowledge management (KM) frameworks is becoming indispensable for the attainment of sustained competitive advantage. This chapter explores the transformative capabilities of AI within KM, underscoring the pivotal significance of human–AI collaboration. We will investigate how emerging technologies, including AI, machine learning (ML) and natural language processing (NLP), can enhance key KM dimensions: the creation, storage, retrieval, sharing and application of knowledge. Furthermore, the chapter considers the essential contributions of human expertise, contextual understanding and decision-making in KM practices. By elucidating the synergy between the advantages of AI, like scalability, data analysis, human competencies, creativity and empathy, the chapter further explores specific implementations of AI–human collaboration, encompassing automated knowledge repositories for knowledge generation, NLP for knowledge retrieval and tailored recommendations for knowledge application. We will also address the ethical challenges and implications that accompany AI integration, focusing on issues such as bias, the need for transparency, trust in AI systems, data privacy and the balance between human oversight and AI control. Finally, we outline practical implications for organizations aiming to empower talent, create AI-friendly infrastructures and streamline knowledge processes to foster successful human–AI partnerships. We conclude by discussing the future of AI–human collaboration in KM, identifying emerging trends and the long-term benefits of integrating AI into organizational learning and innovation.

There are strong economic incentives to develop increasingly potent artificial intelligence (AI) algorithms. This development process may culminate in superhuman artificial general intelligence (AGI), “the last invention we will ever make” (I. J. Good). The AGI is the ultimate knife-edge of the human civilization: both a promise of unimaginable prosperity and an existential threat. A nascent AGI may undergo an intelligence explosion, a cascade of cumulative improvements in cognitive capabilities, allowing it to dramatically improve its optimization power. There are meaningful differences between man-made AI algorithms and biological life which has developed through Darwinian evolution. These considerations have a bearing on the conditions for the doomsday scenario and the ones needed for the emergence of a friendly AGI. The current chapter also touches upon the related question of transhumanism, focusing specifically on human–AI integration. Finally, it briefly elaborates on alternative future scenarios in which AGI never comes into existence.

Background: Generative Artificial Intelligence (AI), particularly large language models like ChatGPT, has rapidly entered medical education. In pharmacology instruction within MBBS programs, these AI tools offer novel educational benefitsfrom ondemand tutoring to personalized learningbut also pose risks due to "hallucinations, " i. e., AI -generated misinformation. Objective: This review explores how generative AI is being integrated into pharmacology education, the educational advantages it provides, the nature and causes of AI hallucinations, and the potential risks of relying on inaccurate AI -generated content in medical learning. Mitigation strategies, best practices, case examples, ethical considerations, and future directions are discussed to inform balanced and safe use. Methods: A literature review was conducted focusing on recent (2020-2025) peer -reviewed sources and authoritative reports on generative AI in medical and pharmacology education. Results: Generative AI can enhance pharmacology education through adaptive learning, rapid information access, and interactive simulations, which students have found useful for studying and clinical preparation. However, AI hallucinationsplausible -sounding yet incorrect answersare common, with studies reporting a substantial fraction of AI -generated medical responses containing errors. These inaccuracies can mislead learners and threaten patient safety if not recognized. Strategies such as careful validation of AI -provided information, AI literacy training, and technical approaches (e. g. retrieval -augmented generation) are identified to mitigate risks. Conclusions: Generative AI holds significant promise in medical pharmacology education by augmenting learning and engagement. Yet, educators and students must remain vigilant about AI hallucinations. A balanced approachleveraging AI's benefits while implementing safeguards and ethical guidelinesis essential to ensure that AI becomes a reliable adjunct in pharmacology education rather than a source of misinformation. Ongoing research, policy development, and training are needed to fully realize generative AI's potential in medical education while minimizing its risks.

In today’s evolving world of work, Artificial Intelligence (AI) has emerged not merely as a technological tool but as a catalyst for rethinking how jobs are structured and performed (Bankins et al., 2024; Parker & Grote, 2022). Across diverse job families and organizational contexts, tools like generative AI (GAI) go beyond mere instruments for data analysis or structuring and can adopt increasingly far-reaching roles through rapid progress in generating new graphic or textual content or algorithmic decision making (Ferràs- Hernández, 2018; Walsh et al. 2019). Central to this symposium is the recognition that this changing nature of work due to digitalization, propelled by AI and related tools, calls for a fresh look at established theories of job design and strategies for human resource management. Traditionally, research has illuminated how fostering characteristics like autonomy, skill and task variety, or adequate demands affect employee motivation, satisfaction, and well-being and thereby further organizational outcomes (Oldham & Fried, 2016; Parker et al., 2017). Yet the introduction of AI – particularly generative AI – adds complexity: Who performs which tasks? How are decisions made, and by whom? Which skills remain uniquely human, and which may be augmented or even entirely adopted by AI? Such questions demand a more nuanced and strategic approach to designing work in the future that balances efficiency and innovation with psychological and ethical considerations. The four presentations in this symposium aim to contribute to theory building on how AI affects work in terms of work design, the interplay of employees and AI use, and work outcomes. In particular, they highlight potential risks or unique challenges when working with AI (e.g., in terms of less enriched work, lowered autonomy levels, threats to job identity or risks due to disclosure of use) and path the way for interventions to achieve benefits of such technologies at work. In detail, the empirical studies in this symposium illuminate these issues at three interconnected levels: 1. Task-Level Insights: Two contributions focus on the more granular task characteristics (e.g., autonomy, skill variety) in modern, AI-enhanced work contexts. They demonstrate how AI use influences the allocation of specific tasks when designing work roles and how the perception of task characteristics changes when AI is used in a decision-making task. 2. Job- and Identity-Level: One paper zooms out from individual tasks to jobs as a whole and explores how AI-driven change might disrupt employees’ job identity. This lens demonstrates how task changes cascade into bigger questions of role definition and identity. 3. Organizational Context: Finally, one presentation adopts the broadest perspective and investigates managerial and organizational factors that might shape employees’ willingness to disclose and ethically integrate AI tools. Taken together, these presentations point toward a future in which AI’s transformative potential hinges on thoughtful coordination across tasks, roles, and organizational systems. By combining established frameworks with new insights drawn from (quantitative and qualitative) surveys and experiments conducted in Germany, Austria, the United States, the United Kingdom, and Australia, these contributions illustrate how AI can enrich, rather than diminish, work and thereby strengthen human-centered organizations. By focusing on variables at different levels, the studies path the way for applications in practice, emphasizing the importance of individual approaches (e.g., training for job design, supporting self-efficacy), or on the organizational level (e.g., aligning HR practices, develop clear and supportive organizational policies and guidelines for the use of AI at work). Investigating the Influence of Work Design Perspective and AI Integration on Enriched Job Design Author: Tanja Bipp; Heidelberg University Author: Thea Ebert; Aalen University Collaborative Intelligence: Integrating Generative AI in Knowledge Work and its Impact on Job Design Author: Sabina Hodzic; University of Graz Author: Simon Grob; University of Graz Author: Bettina Kubicek; University of Graz Job Identity as Starting Point for SHRM in AI-Driven Organizational Change Author: Sophie Berretta; Ruhr University Bochum Author: Annette Kluge; Ruhr University Bochum Silent Adoption: Understanding the Drivers of Non-Disclosure of Generative AI Tools by Employees Author: Fangfang Zhang; Curtin University - Perth

In modern society, the MZ generation (Millennials and Generation Z) reads fewer paper books than in the past, as smartphone usage has become prevalent. This generation is accustomed to watching videos that present summarized information on online platforms. Consequently, they typically acquire information about books through these summaries available online. This study aims to propose the use of artificial intelligence (AI) human characters based on Generative AI to stimulate reading interest among the MZ generation. The recent advancements in Generative AI technology allow for experiences that simulate communication with real people. In addition, various services communicate with users through AI human characters that resemble real individuals. This research investigates whether providing a chatbot, based on AI human characters, with actual conversations from the author of a book can serve as a positive service for users. To explore this concept, the study developed an AI character based chatbot representing Kim, a renowned Korean novelist, as a Generative AI. A prototype was created to facilitate discussions about the book’s content with users. The research employed the System Usability Scale (SUS) test on subjects from the MZ generation using the prototype app service. The SUS test yielded a score of 79.5 points, indicating that it is usable. Furthermore, the normality of the SUS test results for each subject was assessed, confirming that the results are normally distributed, which enhances the reliability of the findings. This research aspires to contribute positively to the reading culture through digital devices and online services utilizing Generative AI. Received: 20 February 2025 | Revised: 14 July 2025 | Accepted: 29 July 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Eunwoo Choi: Writing – original draft, Writing – review & editing, Investigation, Supervision. Gahyeon Kim: Visualization, Investigation. Gyeongmi Kim: Visualization, Investigation, Writing – review & editing. Dabin Choe: Visualization, Investigation, Writing – review & editing. Soonkyu Jang: Conceptualization, Methodology, Validation, Formal analysis, Project administration.

Generative artificial intelligence (AI) has taken the world by storm, with notable tension transpiring in the field of education. Given that Generative AI is rapidly emerging as a transformative innovation, this article endeavors to offer a seminal rejoinder that aims to (i) reconcile the great debate on Generative AI in order to (ii) lay the foundation for Generative AI to co-exist as a transformative resource in the future of education. Using critical analysis as a method and paradox theory as a theoretical lens (i.e., the “how”), this article (i) defines Generative AI and transformative education (i.e., the “ideas”), (ii) establishes the paradoxes of Generative AI (i.e., the “what”), and (iii) provides implications for the future of education from the perspective of management educators (i.e., the “so what”). Noteworthily, the paradoxes of Generative AI are four-fold: (Paradox #1) Generative AI is a ‘friend’ yet a ‘foe’, (Paradox #2) Generative AI is ‘capable’ yet ‘dependent’, (Paradox #3) Generative AI is ‘accessible’ yet ‘restrictive’, and (Paradox #4) Generative AI gets even ‘popular’ when ‘banned’ (i.e., the “what”). Through a position that seeks to embrace rather than reject Generative AI, the lessons and implications that emerge from the discussion herein represent a seminal contribution from management educators on this trending topic and should be useful for approaching Generative AI as a game-changer for education reformation in management and the field of education at large, and by extension, mitigating a situation where Generative AI develops into a Ragnarök that dooms the future of education of which management education is a part of (i.e., the “so what”).

An increasingly popular subcategory of Artificial Intelligence (AI) is Generative AI (GAI), which encompasses technologies capable of creating new content, such as images, text, and music, often resembling outputs made by humans. The potential impact by GAI on sustainability is multifaceted. On the positive side, generative AI can aid in optimizing processes, developing innovative solutions, and identifying patterns in large datasets related to sustainability. This can lead to more efficient resource management, reduced energy consumption, and the creation of more sustainable products. However, there are also potential negative impacts, such as increased energy consumption associated with training and running generative AI models, as well as the potential for unintended consequences or biases in the generated content. Additionally, overreliance on generative AI may lead to reduced human oversight, which could undermine holistic, interdisciplinary, and collaborative approaches to sustainability. The aim of this paper is to explore the potential impacts on sustainability by generative artificial intelligence through a review of prior research on the topic. The study was conducted with a scoping literature review approach to identify potential impacts by generative AI on sustainability. Data were collected through a search in the database Scopus during the spring semester of 2024. Keywords, relevant for the study, were combined with Boolean operators. Papers identified through the search underwent a manual screening process by the authors, in which papers were selected for inclusion or exclusion in the study based on a set of criteria. Included paper were then analyzed with thematic analysis, according to the guidelines by Braun and Clarke. A categorization matrix, based in prior research on sustainability, supported the analysis and deductive coding of collected data. Results of the study highlight generative AI’s potential impact on sustainability that relate to both environmental aspects, economic aspects, and social aspects of sustainability. These different aspects of sustainability impact make this research an important contribution for deepening the understanding of generative AI and its potential consequences for society. Findings of the study provide theoretical contribution, implications for practice, and recommendations for future research on generative AI and sustainability.

Artificial General Intelligence (AGI) is set to profoundly impact the traditional upstream geoenergy industry (i.e., geothermal energy, oil and gas industry) by introducing unprecedented efficiencies and innovations. This paper explores AGI's foundational principles and its transformative applications, particularly focusing on the advancements brought about by large language models (LLMs) and extensive computer vision systems in the upstream sectors of the industry. The integration of Artificial Intelligence (AI) has already begun reshaping the upstream geoenergy landscape, offering enhancements in production optimization, downtime reduction, safety improvements, and advancements in exploration and drilling techniques. These technologies streamline logistics, minimize maintenance costs, automate monotonous tasks, refine decision-making processes, foster team collaboration, and amplify profitability through error reduction and actionable insights extraction. Despite these advancements, the deployment of AI technologies faces challenges, including the necessity for skilled professionals for implementation and the limitations of model training on constrained datasets, which affects the models' adaptability across different contexts. The advent of generative AI, exemplified by innovations like ChatGPT and the Segment Anything Model (SAM), heralds a new era of high-density innovation. These developments highlight a shift towards natural language interfaces and domain-knowledge-driven AI, promising more accessible and tailored solutions for the upstream geoenergy industry. This review articulates the vast potential AGI holds for tackling complex operational challenges within the upstream geoenergy industry, requiring near-human levels of intelligence. We discussed the promising applications, the hurdles of large-scale AGI model deployment, and the necessity for domain-specific knowledge in maximizing the benefits of these technologies.

Generative artificial intelligence (AI) represents a crucial subset of AI models characterized by their ability to generate new content based on user input, showing vast potential to transform learning and teaching. However, educators have raised ethical concerns, particularly regarding the adverse effect on students' learning if students simply parrot generative AI-generated content without engaging in critical analysis or original thought. Moreover, there exists the potential of generative AI to perpetuate existing biases in training data. This editorial discusses three major concerns in generative AI use in education and proposes questions (on task-AI fit and people-AI fit) and approaches to address the ethical considerations by adopting five principles of AI ethics. The editorial also discusses developing a classroom AI use policy as one governance mechanism for promoting ethical use of AI. As generative AI technology continues to evolve, so must our educational practices. The editorial ends with a call for readers (educators) to collaboratively define the terms of engagement with generative AI in educational settings and to begin this discourse by sharing insights and experiences with promoting ethical use of generative AI.