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This paper does not concern with the “why” of ethics. Such questions are typically of interest to philosophers and are outside the scope of this work. In the next section, the authors offer a look into “what” of ethics, i.e. various types and subtypes of ethics. Subsequently, the authors explore “how” of ethics, by summarising various computational approaches to ethical reasoning offered by researchers in the field.

The approaches are classified based on the application domain, ethical theory, agent type and design paradigm adopted. Moreover, promising research directions towards ethical reasoning are also presented.

Since the field is essentially interdisciplinary in nature, collaborative research from such areas as neuroscience, psychology, artificial intelligence, law and social sciences is necessary. It is hoped that this paper offers much needed insight into computational approaches for ethical reasoning paving way for researchers to further engage with the question.

In this paper, the authors discussed vaious computational approaches proposed by researchers to implement ethics. Although none of the approaches adequately answer the question, it is necessary to engage with the research effort to make a substantial contribution to the emerging research area. Though some effort has been made in the design of logic-based systems, they are largely in stages of infancy and merit considerable research.

This study investigates the correlation between economic development and scientific knowledge production indicators in the BRICS countries from 2000 to 2020, highlighting the importance of human resources, natural resources, and innovation. Addressing a gap in the existing literature, this study aims to contribute significantly to understanding this relationship.

Employing a descriptive statistical approach, this study utilizes GDP and per capita income as economic indicators and scientific data from WoS and SCOPUS databases, focusing on scientific document production and citations per document.

The analysis reveals a strong correlation between economic development and scientific performance within the BRICS nations during the specified period. It emphasizes the interdependence of economic progress and scientific prowess, underscoring that they cannot be considered independently.

However, limitations exist, notably the reliance on specific databases that might not cover the entire scientific output and the inability to capture all factors influencing economic and scientific development.

Understanding this interdependence has crucial originality. Policymakers and stakeholders in BRICS countries can leverage these insights to prioritize investments in human capital development and scientific research. This approach can foster sustainable economic growth by reducing reliance on natural resources.

This article examines the contribution of artificial intelligence to augmenting Intelligent Transportation Systems (ITS) to enhance traffic flow, safety, and sustainability.

The research investigates using AI technologies in ITS, including machine learning, computer vision, and deep learning. It analyzes case studies on ITS projects in Poznan, Mysore, Austin, New York City, and Beijing to identify essential components, advantages, and obstacles.

Using AI in Intelligent Transportation Systems has considerable opportunities for enhancing traffic efficiency, minimizing accidents, and fostering sustainable urban growth. Nonetheless, issues like data quality, real-time processing, security, public acceptability, and privacy concerns need resolution.

This article thoroughly examines AI-driven ITS, emphasizing successful applications and pinpointing significant difficulties. It underscores the need for a sustainable economic strategy for extensive adoption and enduring success.

This article investigates the application of generative artificial intelligence (GenAI) in experiential learning for authentic assessment in higher education. Recognized for its human-like content generation, GenAI has garnered widespread interest, raising concerns regarding its reliability, ethical considerations and overall impact. The purpose of this study is to explore the transformative capabilities and limitations of GenAI for experiential learning.

The study uses “thing ethnography” and “incremental prompting” to delve into the perspectives of ChatGPT 3.5, a prominent GenAI model. Through semi-structured interviews, the research prompts ChatGPT 3.5 on critical aspects such as conceptual clarity, integration of GenAI in educational settings and practical applications within the context of authentic assessment. The design examines GenAI’s potential contributions to reflective thinking, hands-on learning and genuine assessments, emphasizing the importance of responsible use.

The findings underscore GenAI’s potential to enhance experiential learning in higher education. Specifically, the research highlights GenAI’s capacity to contribute to reflective thinking, hands-on learning experiences and the facilitation of genuine assessments. Notably, the study emphasizes the significance of responsible use in harnessing the capabilities of GenAI for educational purposes.

This research showcases the application of GenAI in operations management education, specifically within lean health care. The study offers insights into its capabilities by exploring the practical implications of GenAI in a specific educational domain through thing ethnography and incremental prompting. Additionally, the article proposes future research directions, contributing to the originality of the work and opening avenues for further exploration in the integration of GenAI in education.