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This paper analyzes the ethics of integrating Artificial Intelligence (AI), particularly regarding AI-generated educational content in academia. It attempts to explore how AI customization mimics human interaction and behavior in education, investigate ethical concerns in educational AI adoption, and assess ChatGPT’s ethical use for nurturing curiosity and maintaining academic integrity in education.

Fictional tales may help us think critically and creatively to uncover hidden truths. The narratives are analyzed to determine the affordances and drawbacks of Artificial Intelligence in Education (AIEd).

The study highlights the imperative for innovative, ethically grounded strategies in harnessing AI/GPT technology for education. AI can enhance learning, and human educators’ irreplaceable role is even more prominent, emphasizing the need to harmonize technology with pedagogical principles. However, ensuring the ethical integration of AI/GPT technology demands a delicate balance where the potential benefits of technology should not eclipse the essential role of human educators in the learning process.

This paper presents futuristic academic scenarios to explore critical dimensions and their impact on 21^st century learning. As AI assumes tasks once exclusive to human educators, it is essential to redefine the roles of both technology and human teachers, focusing on the future.

Circularity has acted as an essential phenomenon for small and medium enterprises (SMEs) in emerging economies, pressuring entrepreneurs to its adoption in their businesses. During the adoption and implementation of circularity, entrepreneurs or circular entrepreneurs (to be precise) are facing various challenges to its effective functioning. However, the scholarly literature has offered limited research into this phenomenon. Thus, the purpose of this research is to identify the various barriers and sub-barriers for circular entrepreneurs to adopt circularity in SMEs of emerging economies.

A combined qualitative and quantitative approach was employed to achieve the objectives of the study. In the first stage, through an extensive literature review, a list of barriers was identified and in the second stage, a deductive approach was employed to finalize the barriers. Finally, Best-Worst Method (BWM), a multi-criteria decision-making (MCDM) method, was used to analyse the significant importance of the barriers.

The findings of the study suggested the “financial barrier” as the first-ranked barrier in the adoption of Circular Business Models (CBMs), followed by the “regulatory and operational barrier” as the top second and third barriers. In terms of sub-barriers, “lack of access to funding and capital” has been identified as the top sub-barrier in the adoption of CBM, followed by “excessive regulations and red tape” and “challenges due to ambiguity of the concept”.

To transition from a circular to a linear business approach considerably quicker and smoother, entrepreneurs may utilize the findings of this study as a blueprint for the steps to overcome the barriers in a linear to a circular transition.

This research differentiates from other studies due to solicited input directly from the people who are most familiar with the challenges of making the transition from linear to CBM, i.e. the entrepreneurs themselves.

Artificial intelligence (AI) can enhance human resource resiliency (HRR) by providing the insights and resources needed to adapt to unexpected changes and disruptions. Therefore, the present research attempts to develop a framework for future researchers to gain insights into the actions of AI to enable HRR.

The present study used a systematic literature review, bibliometric analysis, and network analysis followed by content analysis. In doing so, we reviewed the literature to explore the present state of research in AI and HRR. A total of 98 articles were included, extracted from the Scopus database in the selected field of research.

The authors found that AI or AI-associated techniques help deliver various HRR-oriented outcomes, such as enhancing employee competency, performance management and risk management; enhancing leadership competencies and employee well-being measures; and developing effective compensation and reward management.

The present research has certain implications, such as increasing the HR team's proficiency, addressing the problem of job loss and how to fix it, improving working conditions and improving decision-making in HR.

The present research explores the role of AI in HRR following the COVID-19 pandemic, which has not been explored extensively.

Sustainability performances (SPs) are the most crucial performances for an organisation in today's world, and they can be measured by economic, social, and environmental metrics. Previous research has not been clear on the role of total productive maintenance (TPM) in the context of sustainability, which motivates the authors to investigate the relationship between TPM and various SPs of the manufacturing firm. Therefore, current research investigates the relationship between TPM and the overall sustainability of the manufacturing firm from the perspective of resource-based view (RBV) theory.

The current study proposed, tested and validated a conceptual framework using partial least squares structural equation modelling (PLS-SEM). A total of 326 responses were received to validate the conceptual framework in smartsPLS 3.0 software.

The research outcomes indicate that TPM considerably impacts a manufacturing firm's economic, environmental and social performance.

This research demonstrates that outstanding shop floor behaviour, such as TPM, can become an important asset to offer competitive advantages in a manufacturing firm. Similarly, TPM might serve as a roadmap for leveraging overall sustainability for manufacturing companies. The study indicates the establishment of a sustainability-oriented training protocol while practising TPM.

No past investigation indicates that a shop floor activity like TPM could be used as an input to offer sustainability in a single index for a manufacturing firm from the perception of RBV theory.

This study's purpose is to collate data on the existing Green Public Procurement (GPP) legal policies, frameworks and initiatives at the global level and, second, to identify the conditions required for implementing green procurement practices and programs in developing countries such as India. A structured literature review was conducted using the search terms ‘Green Public Procurement’, 'Policy’ and ‘India’. Reports, technical papers and articles in a language other than English were excluded from the search. Finally, 20 papers were shortlisted and reviewed. The study found that GPP has begun taking shape globally and necessitates capacity building at many levels, including knowledge sharing, sourcing resources, fiscal re-distribution and public awareness. National governments emerge as key players in facilitating sustainable procurement, while international bodies continue to develop prospective policy frameworks. However, GPP is at its nascency in India and will require significant pre-assessment and planning to standardize itself among big and medium enterprises. While GPP allows for environmental consciousness, its use in India also presents the potential for innovation and expanding the consumer market. Its establishment requires the government to implement a centralized body that directs GPP activity, incentivizes green technology and develops policies favouring multi-level collaboration. Further research can involve in-depth interviewing of key people in Public Procurement (PP) processes to gauge the preparedness and seriousness of the government to undertake green procurement and understand the inherent challenges in transitioning to more sustainable procurement modalities.