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This study presents a systematic literature review analysing the impact of artificial intelligence (AI) on higher education, focusing on its methods, results, and implications. By synthesising a diverse range of academic papers, the review explores how AI technologies influence educational standards and practices in higher education institutions. Findings reveal that AI has the potential to enhance the quality of higher education by diversifying teaching responsibilities, customising learning experiences, and employing intelligent, adaptive teaching strategies. These capabilities position AI as a transformative tool for improving educational delivery and outcomes. However, the study also highlights significant challenges associated with integrating AI into higher education. These challenges include delineating the appropriate scope of AI use, addressing inequalities in access to digital resources, and ensuring adequate training and support for educators and students. The review underscores the importance of understanding these complexities to guide the development of effective strategies and policies that optimise AI's potential while mitigating its limitations. The review offers critical insights into the dual role of AI in higher education, where it can either advance or hinder educational standards depending on how it is implemented. By examining the advantages, limitations, and broader consequences of AI-powered instructional tools, this study provides a comprehensive perspective on the intricate relationship between AI and educational quality. The findings aim to inform educators, policymakers, and stakeholders about the opportunities and challenges of adopting AI in higher education, contributing to the development of inclusive, innovative, and sustainable educational practices.

This research examined the impact of Basel III capital regulation (BCR) on credit risk (CR) using a sample of 25 commercial banks in Lebanon over the period 2012–2017. BCR is measured using the capital adequacy ratio (CAR) and the common equity tier one ratio (CET1 ratio), CR is measured using net provision for credit losses /total assets. To analyze the data, we constructed a hybrid model based on 3 statistical approaches. First, we modelled the dual impact of BCR and CR using probabilistic inference in the framework of Bayesian Belief Network formalism (BBN). Second, to highlight more about the correlation between BCR and CR, we used Spearman correlation test as a nonparametric approach. Third to study the simultaneous effect of CAR and CET1 ratio on CR we applied multivariate regression analysis. By analyzing the probabilistic inference for the first approach we concluded that there is an effect of BCR on CR especially for the high level of CET1 ratio, but when we investigated more if this effect is significant using the Spearman correlation test and the multivariate regression analysis, we concluded that there is no effect statistically significant of Basel III capital regulation (BCR) on credit risk (CR).

Although Turkey has historically concentrated its trade with the European Union (EU) it has diversified its trade markets with the neighbouring regions and different group of countries during the last decade. Among them, Arab countries have come into prominence. Especially, following the “zero problems with neighbours” policy (ZPN), pursued by Turkey since 2002, the trade volume with the Middle Eastern neighbours has increased faster than that with its traditional partners. Nevertheless so called “Arab Spring” has started to manifest its effects on this trend. It deteriorates not only the economies of the concerned countries but also Turkey’s trade expansion. This paper aims: (i) to test through a gravity model the positive impacts of the ZPN policy and the negative impact of the Arab Spring on the trade expansion with the Spring Countries; and (ii) to reveal the positive impact of the policy change and the negative impact of the uprising movements on the realization of trade potential by Turkey in the Spring Countries.

A number of authors suggested that the impact of the macroeconomic factors on the incidence of the financial distress, and afterward in case of failure of companies. However, macroeconomic factors rarely, if ever, appear as variables in predictive models that seek to identify distress and failure; modellers generally suggest that the impact of macroeconomic factors has already been taken into account by financial ratio variables. This article presents a systematic study of this domain, by examining the link between the failure of companies and macroeconomic factors for the French companies to identify the most important variables and to estimate their utility in a predictive context. The results of the study suggest that several macroeconomic variables are strictly associated to the failure, and have a predictive value by specifying the relation between the financial distress and the failure.