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This study explored the themes and sentiments of online learners regarding the use of Generative Artificial Intelligence (AI) or “generative AI” technology in higher education.

English-language tweets were subjected to topic modelling and sentiment analysis. Three prevalent themes were identified and discussed: curriculum development opportunities, lifelong learning prospects and challenges associated with generative AI use.

The results also indicated a range of topics and emotions towards generative AI in education, which were predominantly positive but also varied across male and female users.

The findings provide insights for educators, policymakers and researchers on the opportunities and challenges associated with the integration of generative AI in educational settings. This includes the importance of identifying AI-supported learning and teaching practices that align with gender-specific preferences to offer a more inclusive and tailored approach to learning.

The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI).

Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research.

The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices.

This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields.

The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.

Generative conversational artificial intelligence (AI) demonstrates powerful conversational skills for general tasks but requires customization for specific tasks. The quality of a custom generative conversational AI highly depends on users’ guidance, which has not been studied by previous research. This study uses social exchange theory to examine how generative conversational AI’s cognitive and emotional conversational skills affect users’ guidance through different types of user engagement, and how these effects are moderated by users’ relationship norm orientation.

Based on data collected from 589 actual users using a two-wave survey, this study employed partial least squares structural equation modeling to analyze the proposed hypotheses. Additional analyses were performed to test the robustness of our research model and results.

The results reveal that cognitive conversational skills (i.e. tailored and creative responses) positively affected cognitive and emotional engagement. However, understanding emotion influenced cognitive engagement but not emotional engagement, and empathic concern influenced emotional engagement but not cognitive engagement. In addition, cognitive and emotional engagement positively affected users’ guidance. Further, relationship norm orientation moderated some of these effects such that the impact of user engagement on user guidance was stronger for communal-oriented users than for exchange-oriented users.

First, drawing on social exchange theory, this study empirically examined the drivers of users’ guidance in the context of generative conversational AI, which may enrich the user guidance literature. Second, this study revealed the moderating role of relationship norm orientation in influencing the effect of user engagement on users’ guidance. The findings will deepen our understanding of users’ guidance. Third, the findings provide practical guidelines for designing generative conversational AI from a general AI to a custom AI.

Current knowledge and research on students’ utilization and interaction with generative artificial intelligence (AI) tools in their academic work is limited. This study aims to investigate students’ engagement with these tools.

This research used survey-based research to investigate generative AI literacy (utilization, interaction, evaluation of output and ethics) among students enrolled in a four-year public university in the southeastern USA. This article focuses on the respondents who have used generative AI (218; 47.2%).

Most respondents used generative AI to generate ideas for papers, projects or assignments, and they also used AI to assist with their original ideas. Despite their use of AI assistance, most students were critical of generative AI output, and this mindset was reflected in their reported interactions with ChatGPT. Respondents expressed a need for explicit guidance from course syllabi and university policies regarding generative AI’s ethical and appropriate use.

Literature related to generative AI use in higher education specific to ChatGPT is predominantly from educators’ viewpoints. This study provides empirical evidence about how university students report using generative AI in the context of generative AI literacy.

This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.

The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.

Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.

While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.

This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

This study aims to uncover the ongoing discourse on generative artificial intelligence (AI), literacy and governance while providing nuanced perspectives on stakeholder involvement and recommendations for the effective regulation and utilization of generative AI technologies.

This study chooses generative AI-related online news coverage on BBC News as the case study. Oriented by a case study methodology, this study conducts a qualitative content analysis on 78 news articles related to generative AI.

By analyzing 78 news articles, generative AI is found to be portrayed in the news in the following ways: Generative AI is primarily used in generating texts, images, audio and videos. Generative AI can have both positive and negative impacts on people’s everyday lives. People’s generative AI literacy includes understanding, using and evaluating generative AI and combating generative AI harms. Various stakeholders, encompassing government authorities, industry, organizations/institutions, academia and affected individuals/users, engage in the practice of AI governance concerning generative AI.

Based on the findings, this study constructs a framework of competencies and considerations constituting generative AI literacy. Furthermore, this study underscores the role played by government authorities as coordinators who conduct co-governance with other stakeholders regarding generative AI literacy and who possess the legislative authority to offer robust legal safeguards to protect against harm.

This study aims to investigate a mediation model in which generative learning positively affects marketing innovation and both organizational control and relationship openness mediate the relationship between learning intent and generative learning of international joint ventures (IJVs) in emerging markets. We also decipher the degree of necessity of these factors for generative learning and of generative learning for marketing innovation.

A sample of 181 marketing managers of IJVs in Vietnam, an emerging market, was surveyed to collect data. Partial least squares structural equation modeling (PLS-SEM) was employed to test the net effect, and necessary condition analysis (NCA) was used to decipher the degree of necessity.

The PLS-SEM results demonstrate that the effect of learning intent on generative learning is fully mediated by organizational control and relationship openness, which in turn leads to marketing innovation. The NCA findings reveal that all three factors, namely learning intent, organizational control and relationship openness, serve as necessary conditions for generative learning. However, generative learning does not play the role of a necessary condition for marketing innovation.

The study findings suggest that IJVs in emerging markets should pay attention not only to the net effects of those factors but also to their degrees of necessity for generative learning in order to achieve marketing innovation.

The study contributes to the literature by confirming the mediating roles of organizational control and relationship openness in the relationship between learning intent and generative learning. Furthermore, it is among the first to decipher the degrees of necessity of these factors for generative learning and of generative learning for the marketing innovation of IJVs in emerging markets.

Applying traditional grounded theory techniques, the present research reanalyzed secondary data from four previously conducted studies to explore how generativity is manifested in young adults.

A new conceptual model of generativity was developed to depict how generativity manifests among this age group.

This study's findings provide leadership educators with a refined approach to interacting with this construct while simultaneously increasing young adults’ potential ability to experience the benefits available to them through generativity at an earlier stage in their lives.

This study advances the field of leadership education by establishing foundational insight into the uniqueness of generativity’s development in young adulthood.

The purpose of this paper is to share findings from empirically driven conceptual research into the implications for English teachers of understanding generative AI as a “tool” for writing.

The paper reports early findings from an Australian National Survey of English teachers and interrogates the notion of the AI writer as “tool” through intersectional feminist discursive-material analysis of the metaphorical entailments of the term.

Through this work, the authors have developed the concept of “coloniser tool-thinking” and juxtaposed it with First Nations and feminist understandings of “tools” and “objects” to demonstrate risks to the pursuit of social and planetary justice through understanding generative AI as a tool for English teachers and students.

Bringing together white and First Nations English researchers in dialogue, the paper contributes a unique perspective to challenge widespread and common-sense use of “tool” for generative AI services.

This commentary discusses the value of generative artificial intelligence (AI) for qualitative research in phygital settings to understand the customer experience.

The critical and logical analysis is based on current knowledge of generative AI.

Generative AI seems very useful for qualitative research in phygital settings to understand the customer experience and should be used in qualitative research projects. Generative AI can provide much-needed validation of the subjective nature of qualitative research and can also generate insights beyond human intuition.

The study is based on current technology, which changes fast. In the future, the skills of qualitative researchers may become outdated, relegating them to the role of prompt engineers.

Technology, and especially generative AI, will be a key tool for practitioners as they conduct practical research.

Qualitative researchers should overcome potential anti-technology speciesism and embrace the potential of generative AI.

This commentary provides insights into the role of generative AI for qualitative research in phygital settings.