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The media is described as a fourth estate of the realm due to its ability to frame and shape discussions on governance and provide a stimulus for fighting corruption. But is the media really an effective tool for fighting corruption? This question arises due to the possibility of the media being used for propaganda, biased reporting and media owners’ and journalists’ engagement in corruption. The current study addresses the question by exploring the relationship between the media and corruption from the perspectives of Ghanaian justice and anti-corruption officials.

The study adopts a qualitative approach by interviewing justice and anti-corruption officials across three administrative regions in Ghana.

The results show that while justice officials describe the media as a medium for accusing officials unjustifiably and exaggerating the scale of corruption, anti-corruption officials believe the media helps to fight corruption. In addition to uncovering and exposing public officials’ corruption, the media is also a double-edged sword characterised by intra-vigilance: the media hold “their own” (i.e. journalists fighting corruption) accountable through criticism and exposure of wrongdoings.

The double-edged nature of the media can strengthen and enhance the fight against corruption because anti-corruption actors and journalists will be cautious as misjudgements or errors committed will not be overlooked or concealed by the media. Therefore, anti-corruption agencies in Ghana can collaborate with the media to uncover and expose corruption committed by public officials and even journalists or media owners.

This study is the first in Ghana to explore the relationship between the media and corruption from the perspectives of justice and anti-corruption officials. The approach, frameworks and methodology adopted in this study can be applied in similar studies in other countries on the African continent and beyond.

The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

The study utilizes a systematic literature review of over 141 journal papers and psychometric tests to evaluate AQ. Thematic analysis of quantitative and qualitative studies explores domains of AI in education.

Results suggest that assessing the AQ of students with the help of AI techniques is necessary. Education is a vital tool to develop and improve natural intelligence, and this survey presents the discourse use of AI techniques and behavioral strategies in the education sector of the recent era. The study proposes a conceptual framework of AQ with the help of assessment style for higher education undergraduates.

Research on AQ evaluation in the Indian context is still emerging, presenting a potential avenue for future research. Investigating the relationship between AQ and academic performance among Indian students is a crucial area of research. This can provide insights into the role of AQ in academic motivation, persistence and success in different academic disciplines and levels of education. AQ evaluation offers valuable insights into how individuals deal with and overcome challenges. The findings of this study have implications for higher education institutions to prepare for future challenges and better equip students with necessary skills for success. The papers reviewed related to AI for education opens research opportunities in the field of psychometrics, educational assessment and the evaluation of AQ.

This study examines the Robo-Advisors (RA) based on Artificial Intelligence (AI), a new service that digitises and automates investment decisions in the financial and banking industries to provide low-cost and personalised financial advice. The RAs use objective algorithms to select portfolios, reduce behavioural biases, and improve transactions. They are inexpensive, accessible, and transparent platforms. Objective algorithms improve the believability of portfolio selection.

This study adopts a qualitative approach consisting of an exploratory examination of seven different RA case studies and analyses the RA platforms used in the banking industry.

The findings provide two different approaches to running a business that are appropriate for either fully automated or hybrid RAs through the realisation of two platform model frameworks. The research reveals that relying solely on algorithms and not including any services involving human interaction in a company model is inadequate to meet the requirements of customers in decision-making.

This study emphasises key robo-advisory features, such as investor profiling, asset allocation, investment strategies, portfolio rebalancing, and performance evaluation. These features provide managers and practitioners with new information on enhancing client satisfaction, improving services, and adjusting to dynamic market demands.

This study fills the research gap related to the analysis of RA platform models by providing a meticulous analysis of two different types of RAs, namely, fully automated and hybrid, which have not received adequate attention in the literature.