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With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines.

We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected.

This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies.

This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.

Research on artificial intelligence (AI) and its potential effects on the workplace is increasing. How AI and the futures of work are framed in traditional media has been examined in prior studies, but current research has not gone far enough in examining how AI is framed on social media. This paper aims to fill this gap by examining how people frame the futures of work and intelligent machines when they post on social media.

We investigate public interpretations, assumptions and expectations, referring to framing expressed in social media conversations. We also coded the emotions and attitudes expressed in the text data. A corpus consisting of 998 unique Reddit post titles and their corresponding 16,611 comments was analyzed using computer-aided textual analysis comprising a BERTopic model and two BERT text classification models, one for emotion and the other for sentiment analysis, supported by human judgment.

Different interpretations, assumptions and expectations were found in the conversations. Three subframes were analyzed in detail under the overarching frame of the New World of Work: (1) general impacts of intelligent machines on society, (2) undertaking of tasks (augmentation and substitution) and (3) loss of jobs. The general attitude observed in conversations was slightly positive, and the most common emotion category was curiosity.

Findings from this research can uncover public needs and expectations regarding the future of work with intelligent machines. The findings may also help shape research directions about futures of work. Furthermore, firms, organizations or industries may employ framing methods to analyze customers’ or workers’ responses or even influence the responses. Another contribution of this work is the application of framing theory to interpreting how people conceptualize the future of work with intelligent machines.

Artificial intelligence (AI) is a significant game changer in human resource development (HRD). The launch of ChatGPT has accelerated its progress and amplified its impact on organizations and employees. This study aims to review and examine literature on AI in HRD, using a bibliometric approach.

This study is a bibliometric review. Scopus was used to identify studies in the field. In total, 236 papers published in the past 10 years were examined using the VOSviewer program.

The obtained results showed that most cited documents and authors are mainly from computer sciences, emphasizing machine learning over human learning. While it was expected that HRD authors and studies would have a more substantial presence, the lesser prominence suggests several interesting avenues for explorations.

This study provides insights and recommendations for researchers, managers, HRD practitioners and policymakers. Prioritizing the development of both humans and machines becomes crucial, as an exclusive focus on machines may pose a risk to the sustainability of employees' skills and long-term career prospects.

There is a dearth of bibliometric studies examining AI in HRD. Hence, this study proposes a relatively unexplored approach to examine this topic. It provides a visual and structured overview of this topic. Also, it highlights areas of research concentration and areas that are overlooked. Shedding light on the presence of more research originating from computer sciences and focusing on machine learning over human learning represent an important contribution of this study, which may foster interdisciplinary collaboration with experts from diverse fields, broadening the scope of research on technologies and learning in workplaces.

The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots.

This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering.

Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education.

The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective.

This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

Artificial intelligence (AI) is transforming consumers' experiences and how firms identify, create, nurture and manage interactive marketing relationships. However, most marketers do not have a clear understanding of what AI is and how it may mutually benefit consumers and firms. In this paper, the authors conduct an extensive review of the marketing literature, develop an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships, identify research gaps and offer a future research agenda.

The authors first conduct an extensive literature review in 16 top marketing journals on AI. Based on this review, an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships was conceptualized.

The literature review led to a number of key research findings and summary areas: (1) an historical perspective, (2) definitions and boundaries of AI, (3) AI and interactive marketing, (4) relevant theories in the domain of interactive marketing and (5) synthesizing AI research based on antecedents to AI usage, interactive AI usage contexts and AI-enabled value co-creation outcomes.

This is one of the most extensive reviews of AI literature in marketing, including an evaluation of in excess or 300 conceptual and empirical research. Based on the findings, the authors offer a future research agenda, including a visual titled “What is AI in Interactive Marketing? AI design factors, AI core elements & interactive marketing AI usage contexts.”

Purpose: This chapter examines the impact of artificial intelligence (AI) on employability and dispels the misconception that AI negatively affects job opportunities. The study aims to shed light on the ways in which AI can enhance employability by complementing natural intelligence and enabling employees to demonstrate creativity in various aspects of their work.

Need for the study: In the 21st century, AI has become ubiquitous, and governments worldwide are actively promoting its integration into various industries and systems. However, concerns about the potential negative consequences of AI have emerged.

Methodology: It is reviewing commentary secondary sources of data viz. books, articles, journals, newspaper articles, reports which have been considered to bring forth the advent of AI being an important premise for the construct of employability

Findings: The findings of this study reveal that the perceived negative impact of AI on employability is a misconception. AI technology, such as Alexa, ChatGPT, and OpenAI, has made significant advancements in the market but is still unable to pass the Turing test. Consequently, it is recommended that AI companies take a pause to fully understand and address the consequences associated with AI implementation.

Practical implications: The practical implications of this study are twofold. First, it debunks the myth that AI jeopardises employability associated with natural intelligence, highlighting the importance of human skills in conjunction with AI technologies. Second, it calls for a strategic approach for organisations and governments to adapt to AI while ensuring the workforce remains adaptable and equipped with the necessary skills. This study provides insights for policymakers, employers, and individuals to embrace AI to augment human potential and improve global market productivity.

Artificial Intelligence (AI) is an emerging technology and turned into a field of knowledge that has been consistently displacing technologies for a change in human life. It is applied in all spheres of life as reflected in the review of the literature section here. As applicable in the field of libraries too, this study scientifically mapped the papers on AAIL and analyze its growth, collaboration network, trending topics, or research hot spots to highlight the challenges and opportunities in adopting AI-based advancements in library systems and processes.

The study was developed with a bibliometric approach, considering a decade, 2012 to 2021 for data extraction from a premier database, Scopus. The steps followed are (1) identification, selection of keywords, and forming the search strategy with the approval of a panel of computer scientists and librarians and (2) design and development of a perfect algorithm to verify these selected keywords in title-abstract-keywords of Scopus (3) Performing data processing in some state-of-the-art bibliometric visualization tools, Biblioshiny R and VOSviewer (4) discussing the findings for practical implications of the study and limitations.

As evident from several papers, not much research has been conducted on AI applications in libraries in comparison to topics like AI applications in cancer, health, medicine, education, and agriculture. As per the Price law, the growth pattern is exponential. The total number of papers relevant to the subject is 1462 (single and multi-authored) contributed by 5400 authors with 0.271 documents per author and around 4 authors per document. Papers occurred mostly in open-access journals. The productive journal is the Journal of Chemical Information and Modelling (NP = 63) while the highly consistent and impactful is the Journal of Machine Learning Research (z-index=63.58 and CPP = 56.17). In the case of authors, J Chen (z-index=28.86 and CPP = 43.75) is the most consistent and impactful author. At the country level, the USA has recorded the highest number of papers positioned at the center of the co-authorship network but at the institutional level, China takes the 1st position. The trending topics of research are machine learning, large dataset, deep learning, high-level languages, etc. The present information system has a high potential to improve if integrated with AI technologies.

The number of scientific papers has increased over time. The evolution of themes like machine learning implicates AI as a broad field of knowledge that converges with other disciplines. The themes like large datasets imply that AI may be applied to analyze and interpret these data and support decision-making in public sector enterprises. Theme named high-level language emerged as a research hotspot which indicated that extensive research has been going on in this area to improve computer systems for facilitating the processing of data with high momentum. These implications are of high strategic worth for policymakers, library stakeholders, researchers and the government as a whole for decision-making.

The analysis of collaboration, prolific authors/journals using consistency factor and CPP, testing the relationship between consistency (z-index) and impact (h-index), using state-of-the-art network visualization and cluster analysis techniques make this study novel and differentiates it from the traditional bibliometric analysis. To the best of the author's knowledge, this work is the first attempt to comprehend the research streams and provide a holistic view of research on the application of AI in libraries. The insights obtained from this analysis are instrumental for both academics and practitioners.

We use design thinking in the context of accounting pedagogy to exploit recent advances in cybernetics in the form of generative artificial intelligence technology. Relying on the intuition that supplementing or augmenting human argumentation (natural intelligence or NI) with parallel AI output can produce better student written assignments, we posit the “augmentation premise,” that is, ((NI + AI) > AI > NI). To test the augmentation premise, we compare student written submissions in an Accounting Information Systems (AIS) course with and without the benefit of parallel generative AI output. We then evaluate how the generative AI output enhances student-crafted revisions to their initial submissions. Using a summative quality improvement index (QII) consisting of quantitative and qualitative assessments, we present preliminary evidence supporting the augmentation premise. The augmentation premise likely extends to other accounting subdisciplines and merits generalization for enriching accounting pedagogy.