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This paper emphasizes on the possibility of merging Artificial Intelligence and Blockchain technologies to solve academic qualifications forgery issues in the educational sectors. Empirical data is collected through interviews with many specialist and technical people who is interested in the emerging technologies of the Fourth Industrial Revolution and focused group discussion in the field, as well as from reports in the reviewed literary articles. Scientific journals have also been accessed to analyze the paper goals and objectives. The findings are based on the conclusion suggest that emerging technologies can be integrated to become more efficient and effective in detecting fraud and forgery before it occurs. Considerable attention should be given to reducing and combating these issues because they have significant negative impacts on the economy and education. Accordingly, the study makes recommendations based on the results and areas of future research, considering the establishment of a unified and integrated system. Initially will be applied as a pilot in Sultanate of Oman, then gradually will be extended to the Gulf Cooperation Council States (GCC) and internationally particularly the affiliated and the recognized educational institutions to avoid the phenomena that affect the reputation and quality of education institutions and academic qualifications. In the conclusion considering the impacts of the proposed system in the education and economy as well in general. This research aims to investigate academic forgery cases in the world and then focuses in Oman. Furthermore, it explores the possibilities of merging technologies (Artificial Intelligence and Blockchain) to contribute in eliminating fraud before it occurs. In addition, to propose a framework that will help to find solutions based on the suggested integrated technologies. Finally, it assess the probabilities of the proposed solutions performance and its impacts in the academic sector.

This research work reports a systematic analysis of association of age with academic performance in a higher education institution. The correlation coefficient between key parameters of the student data were absorbed to derive the attributes that contributed strong positive influence on student results and also to identify the attributes that donated a negative impact. Further, a predictive model was developed to forecast the student performance in higher level modules based on the contextual factors. The outcome of the work showcased that negative correlation exists between age and the academic performance. On the contrary, positive correlation exists between lower level and higher level modules. Further, future research directions are discussed.

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