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This study aims to explore how closed factories could be transformed and provide a path for sustainable development for a territory. The authors focus on the case of the Great Mine Serbariu, located in Carbonia (Sardinia), which used to be the largest coal mine in Italy between 1939 and 1964.

The authors adopt a qualitative research design based on an exploratory single-case study, drawing on interviews with the main stakeholders, on a survey conducted among 5,158 visitors, and on administrative documentation of the City Council.

The analysis of the Great Mine Serbariu case showed that the regeneration of an exhausted mine serves a model of sustainable development, especially for the redevelopment of other urban and industrial degraded areas. The Great mine Serbariu was restored and turned into a place of culture, tourism, research and higher education, with the Italian Cultural Centre of Coal Mining (ICCCM) establishing its headquarters in the heart of the former mine. It attracted almost 220,000 visitors, generating both domestic and international tourist flows and making an industrial heritage a real resource for the area.

This article advances the authors’ understanding of how closed industries could become an instrument for sustainable development on the social, economic, touristic and cultural levels. This study would help local governments with examples to enhance the historical resources to create a new identity that led to a sustainable development of an urban landscape, and to create networks with other comparable museums all over Europe to better exploit the touristic and cultural potential.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

Financial stability has become a focal point for central banks since the global financial crisis. However, the optimal mix between monetary and financial stability policies remains unclear. In this study, the “soft” approach to such policy mix was tested – how often monetary policy (in inflation reports) analyses financial stability issues. This paper aims to discuss the aforementioned objective.

A total of 648 inflation reports published by 11 central banks from post-communist countries in 1998-2019 were reviewed using a text-mining method.

Results show that financial stability topics (mainly cyclical aspects of systemic risk) on average account for only 2%of inflation reports’ content. Although this share has grown somewhat since the global financial crisis (in CZ, HU and PL), it still remains at a low level. Thus, not enough evidence was found on the use of a “soft” policy mix in post-communist countries.

Given the strong interactions between price and financial stability, this paper emphasizes the need to increase the attention of monetary policymakers to financial stability issues.

The study combines two research areas, i.e. monetary policy and modern text mining techniques on a sample of post-communist countries, something which to the best of the authors’ knowledge has not been sufficiently explored in the literature before.

Drawing on the work of Niklas Luhmann, the paper argues that technology can be viewed as a self-referential system which is autonomous from both human beings and other function systems of society. The paper aims to develop a philosophy of technology from the work of Niklas Luhmann. To achieve this aim, it draws upon the systems-theory work of Jacques Ellul, a philosopher of technology who focuses on the autonomous potential of technological evolution.

The paper draws on the work of Niklas Luhmann and Jacques Ellul to explore the theme of autonomous technology and what this means for our thinking about technological issues in the twenty-first century. Insights from these two thinkers and researchers working in the Luhmannian sociological tradition are applied to remote work.

The sociological approach of Luhmann, coupled with Ellul's insights into the autonomous nature of technology, can help us develop a systems theory of technology which takes seriously its irreducibility to human functions.

The paper contributes to the growing sociological literature that thematizes the Luhmannian approach to technology, helping us better understand this phenomenon and think in new ways about what technological autonomy means.

The paper brings together the work of Luhmann, Ellul and contemporary researchers to advance a new understanding of technology and technological communication.

This chapter conceptualises a link between Industrial Revolution 4.0 (IR 4.0), big data, data science and sustainable tourism.

The author adopts a grounded theory and conceptual approach to endeavour in this exploratory research.

The outcome shows a significant rise of big data in the tourism sector under three major dimensions, i.e. business, governance and research. And, some exemplary evidence of institutions promoting the use of big data and data science for sustainable tourism has been discussed.

The conceptualised interlinkage of concepts like IR 4.0, big data, data science and sustainable development provides a valuable knowledge resource to policy-makers, researchers, businesses and students.