Retraction notice: ChatGPT in the classroom: navigating the generative AI wave in management education

Integrating generative AI in management education: A mixed-methods study using social construction of technology theory

This essay explores the nuanced impact of generative AI technologies on management and business education, framed through three paradoxes: the Expertise Paradox suggests that AI’s adequate performance at lower-level tasks may weaken students’ development of higher-level thinking; the Innovation Paradox states that AI’s creativity aid could stifle original thinking; and the Equity Paradox highlights AI’s potential to provide immense gains to experts but disproportionately harm novices. We take the position that without “sensible” AI use guidelines in management education, AI is likely to have a net-negative effect on learning. This stance is based on our trials with ChatGPT on various cognitive tasks organized around the revised Bloom’s Taxonomy of learning. We identify areas where AI tools can enhance learning, such as comprehending established subject domains, as well as areas where they exhibit significant limitations, such as logical reasoning and critical thinking. We caution against the potential deskilling in critical thinking due to students’ overreliance on AI for basic tasks. To alleviate these challenges, we recommend sensible AI uses by students that support skill development without fostering overreliance. We also suggest how faculty, administrators, and employers may support students in getting the most out of this new tool.

This essay examines strategies for thoughtfully integrating generative AI (Gen-AI) into management curricula to enhance student learning while mitigating risks like overreliance. We make the case that outright resistance is counterproductive; instead, management educators should embrace Gen-AI’s potential to create more engaging, experiential learning aligned with andragogical principles. We provide a conceptual framework mapping nine Gen-AI objectives to the principles of andragogy. A semester-long course example illustrates this framework in action through AI activities fostering autonomy, competence, and real-world application. Student surveys revealed overwhelmingly positive perceptions of Gen-AI integration and improved exam scores. However, dependence risks remain. The essay discusses strategies to enhance critical thinking, personal growth, and academic integrity. Overall, we propose that prudent Gen-AI adoption can enrich management education, but long-term vigilance regarding overreliance is vital.

Business education is integrating Generative AI (GAI) so as to transform the conventional teaching model by enhancing learning experiences, promoting innovation, and improving decision-making. This study deals with curriculum design enhancement, personalized learning, and the nurture of essential skills such as problem-solving and strategic thinking with the help of GAI tools. However, despite the emergence of GAI provided the direct application, effectiveness, and scalability in business education but for better understanding a gap still found. A systematic literature review using the PRISMA framework was conducted from 2014 to 2025, identifying studies on GAI application in business schools. Findings reveal that GAI provides personalized learning, assists real-time decision-making, and scales up curriculum delivery. However, challenges like faculty preparedness, ethical concerns, and integration issues remain. The study emphasizes the need for curriculum redesign, faculty capacity building, and the ethical use of AI.

Annotated Bibliography - Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. (Lim et al, 2023)

Generative AI is transforming management education by enhancing learning tools and experiences in today's digital market. While educators often use tools like ChatGPT and DALL·E, many fail to connect them to effective classroom strategies. This research reviews 65 academic studies (2016–2024) and includes 25 expert interviews from North America, Europe, and Asia to develop a practical framework. The proposed model includes four key elements: Strategic Alignment to integrate AI into institutional goals, Adaptive Learning Models to personalize learning through data insights, Reflective Practice to improve critical thinking with AI tools, and Stakeholder Engagement to ensure ethical, transparent use. The study also addresses challenges like algorithmic bias and suggests solutions such as policy standards, skill-building, and pilot programs with clear goals and feedback loops. Results show GenAI increases student engagement, supports practical learning, and enhances teaching without replacing educators.

This study provides empirical data on the impact of generative AI in education, with special emphasis on sustainable development goals (SDGs). By conducting a thorough analysis of the relationship between generative AI technologies and educational outcomes, this research fills a critical gap in the literature. The insights offered are valuable for policymakers seeking to leverage new educational technologies to support sustainable development. Using Smart-PLS4, five hypotheses derived from the research questions were tested based on data collected from an E-Questionnaire distributed to academic faculty members and education managers. Of the 311 valid responses, the measurement model assessment confirmed the validity and reliability of the data, while the structural model assessment validated the hypotheses. The study’s findings reveal that New Approaches to Learning Outcome Assessment (NALOA) significantly contribute to achieving SDGs, with a path coefficient of 0.477 (p < 0.001). Similarly, the Use of Generative AI Technologies (UGAIT) has a notable positive impact on SDGs, with a value of 0.221 (p < 0.001). A Paradigm Shift in Education and Educational Process Organization (PSEPQ) also demonstrates a significant, though smaller, effect on SDGs with a coefficient of 0.142 (p = 0.008). However, the Opportunities and Risks of Generative AI in Education (ORGIE) study did not find statistically significant evidence of an impact on SDGs (p = 0.390). These findings highlight the potential opportunities and challenges of using generative AI technologies in education and underscore their key role in advancing sustainable development goals. The study also offers a strategic roadmap for educational institutions, particularly in Oman to harness AI technology in support of sustainable development objectives.

Expert Systems represent a widespread newer application of Artificial Intelligence. In essence, expert systems use computer programs to accumulate the experience of experts in a given field and then provide either solutions to complex problems or explanations as aids to decision-making. These systems are particularly good at providing information related to problems of a technical nature. However, recent research and literature from the fields of AI, psychology, curriculum theory, management education, professional decision-making and the sociology of knowledge has begun to emphasize the need for approaches to knowledge which go beyond the "technical" and incorporate perspectives of a more "practical" and "critical" nature. It would suggest that designers of expert systems be aware of their epistemological assumptions—that is assumptions about the nature and structure of knowledge and how it might be acquired. This paper elaborates on both traditional and emerging conceptions of learning and knowledge and then discusses the challenges presented to designers of expert systems, as well as higher education, in incorporating forms of knowledge that go beyond the technical.KeywordsExpert SystemManagement EducationProfessional KnowledgePractical InquiryReflective PractitionerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Launched in November 2022, OpenAI’s ChatGPT garnered over 100 million users within two months, sparking a surge in research and concern over potential risks of extensive AI experiments. The article, originating from a conference presentation by Tsinghua University and NTHU, Taiwan, provides a nuanced overview of Generative AI. It explores the classifications, applications, governance challenges, societal implications, and development trajectory of Generative AI, emphasizing its transformative role in employment and education. The piece highlights ChatGPT’s significant impact and the strategic adaptations required in various sectors, including medical education, engineering, information management, and distance education. Furthermore, it explores the opportunities and challenges associated with incorporating ChatGPT in educational settings, emphasizing its support in facilitating personalized learning, developing 21st-century competencies, fostering self-directed learning, and enhancing information accessibility. It also illustrates the integration of ChatGPT and text-to-image models in high school language courses through the lens of new literacies. The text uniquely integrates three layers of discourse: introductions to Generative AI by experts, scholarly debates on its merits and drawbacks, and practical classroom applications, offering a reflective snapshot of the current and potential states of Generative AI applications while emphasizing the interconnected discussions across various layers of discourse.

There is an intense concern in various fields, in order to quantify in the most complete and explicit way the impact that the accelerated development of the technology that is the basis of AI has on education. A very special issue in this context is represented by the impact AI has on the teaching methods and techniques used by teachers. Still, in order to develop and refine new methods and techniques based on AI technology is necessary that perceptions and attitudes toward this technology in general and its application in education in special to become positive and people to be open to new experiences in using it. The present research explores how different variables like perception towards inclusion of Generative AI tools within teaching materials development, degree of familiarity, challenges of AI implementation in education, importance of AI within the teaching process, resilience to change can influence the perceived utility of AI in education fostering positive attitude towards it and usage intention among teachers. The results are showing that the influence exerted by the above variables can be assessed within an empirical model that can explain the intention of teachers to use effectively AI based tools at different levels of the didactic activity. Implications at the level of human resources management in education are also discussed.

This study investigates the current landscape of generative AI and LLM applications in architecture, engineering, and construction (AEC), focusing on trends, practical implications, educational strategies, and imperatives for upskilling. Employing a six-stage systematic review sourced from Google Scholar, Scopus and Web of Science, 120 papers were analyzed to provide a comprehensive understanding of the role of these technologies in shaping the future of the AEC industry. By addressing these objectives, the research contributes to enhancing knowledge about the potential impacts of generative AI and LLMs on the AEC industry and provides insights into strategies for leveraging these technologies effectively. This study underscores the transformative impact of AI and advanced technologies on the AEC sector and education. By enhancing learning experiences and optimizing construction processes, AI fosters personalized education and efficient project management. The study’s significance lies in its identification of necessary skills and competencies for professionals, ensuring effective AI integration. Implications include the need for continuous professional development, formal education, and practical training to leverage AI’s potential fully. This paves the way for sustainable, intelligent infrastructure and accessible, adaptive learning environments, driving innovation and efficiency in both fields.

Retraction notice to "Investigation of management of international education considering sustainable medical tourism and entrepreneurship" [Heliyon 9 (2023) e12691

This chapter explores the evolution of Artificial Intelligence and Machine learning, tracing their development from the foundation period to the modern era of generative AI, highlighting key milestones and breakthroughs. It also anticipates future advancements, focusing on AI's role in automation, decision-making, and AI-human collaboration. The challenges of AI adoption, including implementation barriers and ethical concerns, are also discussed. Additionally, the chapter explores AI's transformative impact on business, enhancing efficiency, predictive analytics, and customer engagement. The importance of AI and ML knowledge in management education is emphasized, preparing future professionals for AI-driven industries. The purpose of this chapter is to provide insights into AI's evolution and its relevance in business and education. By integrating AI into learning, professionals can adapt to digital transformations, fostering innovation, competitiveness, and strategic leadership.

Purpose: This study aims to discuss the impacts of using generative artificial intelligence (GenAI) in education and research in the business and management field, using a virtue ethics lens to reflect on technology’s effects on people. Originality/value: Our analysis considers the potential risks and opportunities of using GenAI, particularly ChatGPT. We categorized the effects of generative AI on education and research into groups by mapping agent-centered or action-centered articles and sorting them by the ethical perspective they come from (deontology, utilitarianism, or virtue ethics), keeping in mind that AI ethics addresses mainly utilitarian rules and principles. Our analysis emphasizes the human element to avoid oversimplifying the effects on people’s formation. Design/methodology/approach: We conducted a semi-systematic review of recent literature on GenAI in management education and research. We used the PRISMA method to collect and select articles from three academic databases: Scopus, Science Direct, and Web of Science, in addition to Google Scholar. From 45 articles, we mapped three main issues: analysis level, ethical perspective, and GenAI impacts. Findings: We point out that using GenIA for student learning and researcher training in virtues or character is incipient, while ethical issues are mentioned implicitly or superficially. GenAI can enhance or reduce human development and research, depending on its appropriate use in learning and research processes. A solid grounding in virtue ethics is essential to deeply understanding the impact of human-AI relationships.

This opinion paper explores the evolving role of revenue management (RM) in hospitality and tourism education and argues for a strategic rethinking of how RM is taught at the undergraduate level. Drawing on the authors experience and a review of academic programs and literature, the authors identify a persistent gap between industry expectations and academic curricula. The paper advocates for RM to be a required course, taught through a commercial strategy lens with a strong emphasis on applied analytics, optimization, and artificial intelligence, including generative AI. It also highlights the importance of critical thinking and data interpretation as essential competencies. Finally, we introduce RevME (Revenue Management and Analytics Educators), an international association supporting faculty through collaborative resources, including a dynamic textbook and training initiatives. Overall, we argue that the goal should be to align RM education with industry needs and better prepare students for data-driven decision-making in a rapidly transforming hospitality and tourism landscape.