- The Use of Generative AI

This research looks at the potential effects of generative artificial intelligence AI on the country's media landscape. Given their pervasiveness, it aims to reveal how AI-powered technologies in media content creation, distribution, and personalisation contribute to the overall process of national progress. Using well-designed questionnaires, the study quantitatively collects data from media professionals, techies, and communication scholars in large cities throughout China. Using statistical tools such as structural equation modelling and regression analysis, one investigated the interplay between the rate of modernisation, the effects of national development, and AI-driven media innovation. Media indices of generative AI demonstrate a clear positive correlation with the effect of modernism and national development programs. As China strives to digitally change its communication infrastructure and increase its cultural influence, technological prowess, and media production, generative AI is playing an increasingly crucial role. This study shows that AI in media may lead to more dynamic stories, practical audience participation, and worldwide outreach, all thanks to modernist techniques. There is no part of this that does not contribute to the advancement of national development goals. The results provide policymakers, media outlets, and AI developers with valuable information for formulating strategies to integrate AI with sustainable development objectives. Via an experimental interaction between generative AI and national development perceived via a modernist lens, this study provides a framework for future research on new media technologies and national change. The discussion of the societal potential presented by AI may now begin.

ABSTRACTThe widespread applications of generative AI (GenAI) have sparked significant interest, with many organisations eager to leverage its transformative potential. Rather than focusing on individual organisations, this study examines GenAI integration within enterprise platforms, which are extensively adopted by many organisations and thus amplify both the benefits and risks of GenAI. We offer targeted recommendations for enterprise platform owners and their complementors, addressing challenges they face when integrating GenAI into these platforms. Drawing on a case study of Salesforce's experience, we recommend actions in three foundational areas – platform capability, architecture and governance – ensuring that our guidance is broadly applicable across enterprise platforms. In platform capability, we advise developing a unified GenAI stack built on existing platform services, offering generic and industry‐specific GenAI use cases to accelerate customer adoption and providing tools for customisation and creation of new use cases to enhance GenAI's transformational impact. For platform architecture, we recommend adding new layers for accommodating diverse GenAI foundation models and creating a trusted environment for secure data access, privacy and content monitoring. We also recommend implementing a prompt architecture to improve content relevance and accuracy. In platform governance, we recommend establishing new mechanisms to mitigate GenAI risks. Partnerships with GenAI providers and proactive investments in GenAI are essential to retain critical GenAI technologies. Personalised consultancy and training along with joint design and implementation with platform customers are also recommended. These combined actions, pursued in parallel across capability, architecture and governance, form a sustainable roadmap for GenAI integration in enterprise platforms.

Generative AI (GenAI) use is increasing across society in many different industries. Despite widespread adoption in workplaces, there is little consensus on the scope of its benefits and challenges at the level of most industries. Universities are being called upon to equip graduates with important knowledge and skills using GenAI for their professional contexts. Higher education, however, faces issues in effectively and sustainability embedding a use of GenAI in the student experience, which requires adjustments to learning and teaching activities, assessment, and learning outcomes and in access to useful GenAI platforms relevant to the various disciplines. As pedagogical models, ethical debates, and technologies continue to develop in this space, university teachers’ experiences of teaching with GenAI have yet to be explored in detail. Adopting a phenomenographic perspective, this study examines university teachers’ conceptions, perceptions, and approaches to using GenAI in teaching. Leveraging semi-structured interviews with 30 teaching academics, variations of teaching using GenAI were identified. Quantitative analysis was also conducted to capture the associations between these variations. By exploring the qualitative differences between these variations, a nuanced and important contribution to the GenAI discussion from the understanding of university teachers is uncovered. The results show that some ways of understanding and teaching with GenAI are more likely to help students develop effective knowledge and skills for the workplace than others. The findings also offer education leaders evidence to help design effective support for teachers using GenAI to innovate in the student experience. Through investigating the university teacher experience of GenAI, this research adds to the growing debate on the GenAI enabled benefits and challenges that are set to shape the higher education sector.

Generative AI (GenAI) has been suggested as a promising approach to lowering R&D costs and enhancing drug efficacy, though its ultimate impact on therapeutic effectiveness remains uncertain. This paper investigates competition between an incumbent and a potential entrant, focusing on how the entrant leverages GenAI for market entry. The entrant’s entry-adoption decision depends on fixed cost thresholds, where low cost induces entry with GenAI, high cost precludes entry, and intermediate cost leads to entry without GenAI. The GenAI adoption incentive exhibits an inverted-U pattern in competition intensity. Specifically, with weak competition, outcome uncertainty dominates and the entrant forgoes GenAI. With moderate competition, GenAI is adopted to secure a quality advantage. In intense competition, aggressive price rivalry dissipates the returns to quality, and adoption again ceases. We further reveal that GenAI exerts a double-edged effect on both firms. It raises profits under mild competition, but may counterintuitively reduce them in relatively intense competition, creating a prisoner’s dilemma. Intriguingly, when fixed cost is relatively large but entry occurs, the incumbent may resort to defensive pricing to deter entry, sacrificing its own profit. Finally, we show that GenAI may diminish patient surplus when the entrant monopolises the market or when competition is weak, despite its quality benefits.

Generative AI (GenAI) promises to revolutionize creative work, but its value is not universal. Using controlled lab settings with students and real-world tests with professional designers, our research shows that GenAI is a double-edged tool. In the initial brainstorming (ideation) stage, GenAI reliably boosts creativity for all users. However, in the execution (implementation) stage, whereas novice designers continue to benefit from GenAI’s assistance, expert designers encounter inefficiencies—spending significantly more time without improving creativity, because GenAI’s methods conflict with experts’ well-established routines. For firms, this means adoption strategies must be nuanced. GenAI delivers the greatest value when applied to brainstorming, early concept development, and work by less-experienced employees. In contrast, deploying GenAI in later-stage production tasks, especially with seasoned professionals, may reduce efficiency. Managers and tool designers should avoid blanket promotion of GenAI across all tasks and instead develop targeted adoption strategies that align with employees’ expertise and the stage of the creative process. By tailoring GenAI use, organizations can harness its creative potential while minimizing risks of counterproductive outcomes.

Generative AI (GenAI) is rapidly transforming creative fields, raising critical questions about its impact on artistic identity and authorship, which also influence art education. While studies have explored GenAI adoption, the diversity of art students’ subjective perspectives on collaborating with these tools remains under-examined. This study uses Q methodology, a mixed-methods approach to investigating subjectivity, to explore the distinct perspectives art students hold regarding their creative identity when collaborating with GenAI. We conducted a Q study with 40 university students from various art-related disciplines (e.g., visual arts, design, music, animation) across different academic levels. Participants sorted 42 statements reflecting diverse viewpoints on GenAI collaboration, authorship, ethics, and creative control. By-person factor analysis revealed five distinct perspectives: (1) Human-centered directors, who treat GenAI as a tool under strong human intention; (2) Authorship guardians, who resist AI co-authorship and prioritise authenticity and ethical boundaries; (3) Conflicted co-creators, who use GenAI but struggle with ownership, disclosure, and earned pride; (4) Pragmatic integrators, who adopt GenAI for workflow efficiency while keeping craft expertise central; and (5) Enthusiastic explorers, who embrace AI’s creative possibilities, open to co-agency and novel expression. These findings offer a nuanced typology of student viewpoints, moving beyond simple acceptance or rejection. We further propose a two-dimensional framework that maps these perspectives according to students’ openness to GenAI and their sharing of creative agency with it. This research contributes empirical insights into how art students negotiate creative identity with GenAI, provides a valuable framework for educators to develop tailored pedagogical strategies addressing diverse student needs and concerns, and offers implications for designing more human-centered GenAI tools for creative practice.

Generative AI (GenAI) in education has become a divisive topic. While some teachers view GenAI as a transformative tool, others caution against harms and impacts on teaching and learning. Since the usefulness of a tool depends on the user, how teachers view and use GenAI and their perceptions of its role in teaching and learning may influence its benefits and risks. The study adopts a qualitative approach, interviewing 23 higher education teachers with teaching experience across disciplines. Using the Technological, Pedagogical, and Content Knowledge (TPACK) and sensemaking frameworks to guide and analyse the interviews, a set of opportunities, challenges, and threats associated with integrating GenAI in higher education was identified. Teachers highlighted opportunities, including using GenAI as a study buddy. Concerns were raised about students’ overreliance on GenAI, GenAI undermining the teaching and learning process, and issues like undetected plagiarism. The findings suggest a need for professional development to help teachers understand GenAI and how it can be effectively used in teaching. Some teachers warned against the paradox of using a tool to save time, only to find that it might increase workload and frustration. These insights contribute to developing guidelines and informing policymaking to ensure integration of GenAI in education.

Generative AI (GenAI) is a disruptive technology likely to generate a major impact on faculty and learners in medical education. This work aims to measure the perception of GenAI among medical educators and to gain insights into its major advantages and concerns in medical education. A survey invitation was distributed to medical education faculty of colleges of allopathic and osteopathic medicine within a single university during the fall of 2023. The survey comprised 12 items, among those assessing the role of GenAI for students and educators, the need to modify teaching approaches, GenAI's perceived advantages, applications of GenAI in the educational context, and the concerns, challenges, and trustworthiness associated with GenAI. Responses were obtained from 48 faculty. They showed a positive attitude toward GenAI and disagreed on GenAI having a very negative effect on either the students' or faculty's educational experience. Eighty-five percent of our medical schools' faculty responded to had heard about GenAI, while 42% had not used it at all. Generating text (33%), automating repetitive tasks (19%), and creating multimedia content (17%) were some of the common utilizations of GenAI by school faculty. The majority agreed that GenAI is likely to change its role as an educator. A perceived advantage of GenAI in conducting more effective background research was reported by 54% of faculty. The greatest perceived strengths of GenAI were the ability to conduct more efficient research, task automation, and increased content accessibility. The faculty's major concerns were cheating in home assignments in assessment (97%), tendency for blunder and false information (95%), lack of context (86%), and removal of human interaction in important feedback processes (83%). The majority of the faculty agrees on the lack of guidelines for safe use of GenAI from both a governmental and an institutional policy. The main perceived challenges were cheating, the tendency of GenAI to make errors, and privacy concerns.The faculty recognized the potential impact of GenAI in medical education. Careful deliberation of the pros and cons of GenAI is needed for its effective integration into medical education. There is general agreement that plagiarism and lack of regulations are two major areas of concern. Consensus-based guidelines at the institutional and/or national level need to start to be implemented to govern the appropriate use of GenAI while maintaining ethics and transparency. Faculty responses reflect an optimistic and favorable outlook on GenAI's impact on student learning.

The advent of Generative Artificial Intelligence (Generative AI or GAI) marks a significant inflection point in AI development. Long viewed as the epitome of reasoning and logic, Generative AI incorporates programming rules that are normative. However, it also has a descriptive component based on its programmers' subjective preferences and any discrepancies in the underlying data. Generative AI generates both truth and falsehood, supports both ethical and unethical decisions, and is neither transparent nor accountable. These factors pose clear risks to optimal decision-making in complex health services such as health policy and health regulation. It is important to examine how Generative AI makes decisions both from a rational, normative perspective and from a descriptive point of view to ensure an ethical approach to Generative AI design, engineering, and use. The objective is to provide a rapid review that identifies and maps attributes reported in the literature that influence Generative AI decision-making in complex health services. This review provides a clear, reproducible methodology that is reported in accordance with a recognised framework and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 standards adapted for a rapid review. Inclusion and exclusion criteria were developed, and a database search was undertaken within four search systems: ProQuest, Scopus, Web of Science, and Google Scholar. The results include articles published in 2023 and early 2024. A total of 1,550articles were identified. After removing duplicates, 1,532articles remained. Of these, 1,511 articles were excluded based on the selection criteria and a total of 21 articles were selected for analysis. Learning, understanding, and bias were the most frequently mentioned Generative AI attributes. Generative AI brings the promise of advanced automation, but carries significant risk. Learning and pattern recognition are helpful, but the lack of a moral compass, empathy, consideration for privacy, and a propensity for bias and hallucination are detrimental to good decision-making. The results suggest that there is, perhaps, more work to be done before Generative AI can be applied to complex health services.

PurposeMost recent C-suite surveying suggests current applications of generative AI, although hyped, are fragmented and unlikely to yield major financial returns anticipated. Instead, business leaders expect major value from generative AI will be achieved through application of generative AI to innovation: operational innovation, product and service innovation, and most elusive of all, business model innovation.Design/methodology/approachFindings and analysis presented draws on data from several surveys of C-level executives conducted by IBM Institute for Business Value in collaboration with Oxford Economics during 2023. Each survey focused on the potential of generative AI in a particular business area. The n-count of each survey ranged from 100-3000.Findings1. Business leaders expect generative AI to build on returns achieved from investments in traditional AI, with 10 percent RoI expected on generative AI investments by 2025. 2. Executives anticipate that generative AI will have most impact when implemented to expand innovation. 3. Specific examples provided for operational innovation, product innovation, and business model innovationResearch limitations/implicationsWe are still very early in the generative AI development cycle. We have made best efforts to project, but only time will tell for sure.Practical implicationsBusiness application of generative AI are extremely fragmented. Despite the desire to throw investments at the wall to see what sticks, it is important that leaders take a structured approach to generative AI, focusing on RoI from innovation investments.Social implicationsTo alleviate negative impacts of generative AI, focusing on innovation potential and value maximization is crucial.Originality/valueThis research is based on completely new surveying and data. This papers adds to the sum total of new knowledge in the generative AI domain.

This study examined the guidelines issued by the top 50 U.S. universities regarding the use of Generative AI (GenAI) in academic and administrative activities. Employing a mixed methods approach, the research combined topic modeling, sentiment analysis, and qualitative thematic analysis to provide a comprehensive understanding of institutional responses to GenAI. Topic modeling identified four core topics: Integration of GenAI in Learning and Assessment, GenAI in Visual and Multimodal Media, Security and Ethical Considerations in GenAI, and GenAI in Academic Integrity. These themes were further explored through sentiment analysis, which revealed highly positive attitudes towards GenAI across all institution types, with significant differences between faculty and student-targeted guidelines. Qualitative thematic analysis corroborated these findings and provided deeper insights, revealing that 94% of universities had faculty guidelines emphasizing the importance of establishing and communicating course-specific GenAI policies. This analysis also highlighted recurring themes such as academic integrity and privacy concerns, which aligned with the security and ethical considerations identified in the topic modeling. The study highlights the rapid evolution of GenAI guidelines in higher education and the need for flexible, stakeholder-specific policies that address both the opportunities and challenges presented by this technology.

This paper examines the implications of generative AI (GenAI) emulating human expression, i.e. human communication and human creative expression. While GenAI seems to offer benefits such as increased efficiency and productivity, its use raises significant practical and conceptual concerns: GenAI comes with the increased efforts of prompting, verification and editing, and causes the deskilling of its users. It also comes at a monetary cost and causes various ethical issues e.g., a lack of authenticity. We further show that GenAI’s fundamental issue is that it is by design not able to output human expression but only human-like expression. Using AI for tasks that are fundamentally about communicating is replacing communication with something that is not communication. Finally, we show that the consequences of the use of GenAI cannot be avoided on an individual level by those individuals avoiding the use of GenAI and will necessarily lead to an erosion of human expression in general. This is because GenAI: will lead to a distrust in human expression when the authenticity of authorship over time becomes unclear; will cause a devaluation of human expression when human expression can be mimicked with less effort by GenAI; and, will discourage human expression altogether when GenAI has set the bar too high.

Aim/Purpose: This paper investigates how academics in Egyptian higher education adopt and engage with generative AI tools, addressing the limited understanding of faculty perceptions and the role of technophobia in influencing adoption. Background: Existing research on generative AI adoption primarily focuses on a single tool (e.g., ChatGPT) and overlooks broader organizational and psychological factors. This study extends the Technology Acceptance Model (TAM) to include technophobia and organizational innovative culture, providing a comprehensive explanation of adoption behaviors in the Egyptian higher education context. Methodology: A mixed-methods design was employed. Quantitative data were collected from 195 academics via a structured survey measuring TAM constructs, technophobia, and organizational culture. Qualitative data were obtained through semi-structured interviews to capture experiences, perceived benefits, and concerns regarding generative AI tools. Contribution: The study refines TAM by demonstrating that technophobia indirectly affects adoption through perceived usefulness and perceived ease of use, while organizational innovative culture does not moderate adoption relationships. It offers both theoretical insights and practical guidance for the responsible use of generative AI in higher education. Findings: Perceived usefulness was the strongest predictor of adoption intention, whereas perceived ease of use was not significant. Technophobia reduced perceived usefulness and ease of use but did not directly affect adoption intention. Organizational innovative culture did not moderate relationships. Interviews highlighted efficiency benefits of generative AI alongside concerns about ethics, originality, and policy gaps. Recommendations for Practitioners: Universities should establish clear policies for the use of generative AI in teaching, assessment, and research, and provide regular training and awareness programs to support responsible adoption. Institutions should encourage critical and purposeful use rather than dependence on generative AI. Recommendation for Researchers: Future studies may expand TAM by including constructs such as trust, perceived risk, and institutional policy support, explore discipline-specific adoption patterns, and examine long-term impacts on teaching and learning. Impact on Society: Generative AI has the potential to enhance academic productivity while raising ethical and integrity concerns. Balanced and responsible implementation can maintain the educational and social mission of universities. Future Research: Further research should involve a wider range of institutions, consider moderators such as digital literacy and organizational readiness, and develop ethical and pedagogical frameworks for the constructive use of AI in higher education.

Generative AI (GenAI) technologies are transforming B2B sales, yet both academic and practical understanding of their distinct capabilities and implications remain limited. This article establishes a conceptual foundation for GenAI in sales by introducing three definitional perspectives, technical, functional, and institutional, and examining how they manifest in research and practice. We show that the institutional perspective bridges the technical and functional perspectives, providing a grounded framework for understanding GenAI’s role in B2B sales. Building on this foundation, we present the AGA framework, which identifies autonomy, generativity, and adaptability as core dimensions of GenAI. Configurations of these dimensions enable distinct GenAI sales roles, conceptualized as sociotechnical systems that augment and extend human and organizational capabilities to enhance sales effectiveness. The GenAI roles of assistant, analyst, and coach each offer specific capabilities for coordinating sales activities while also presenting potential risks. We develop a set of propositions and research questions to guide future research and practice. Overall, this study clarifies GenAI’s conceptual dimensions, links them as configurations of sales roles, and outlines a roadmap to guide future research and practice, as well as responsible deployment in B2B sales contexts.

The integration of AI, especially Generative AI (GenAI), is producing a significant change in the ever-changing higher education scene. This chapter investigates how GenAI is transforming teaching, learning, and academic literacy. Academic literacy facilitators must now negotiate a complex landscape that includes conventional materials, digital resources, and AI-enhanced texts. They train scholars in GenAI tools and pioneer creative teaching methodologies. This chapter provides GenAI ontology to help guide you through this revolutionary journey. It prepares facilitators and students to utilize GenAI successfully by promoting specialized teaching techniques and individualized literacy evaluations. In conclusion, this chapter discusses GenAI's potential to innovate, improve access, and boost intellectual prowess in higher education.