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This research was developed to identify artificial intelligence (AI) opportunities and challenges in higher education.

This qualitative research was developed using the six-step thematic analysis method (Braun and Clark, 2006). Participants in this study were AI PhD students from Tehran University in 2022–2023. Purposive sampling was used to select the participants; a total of 15 AI PhD students, who were experts in this field, were selected and interviews were conducted.

The authors considered the opportunities that AI creates for higher education in eight secondary subthemes (for faculty members, for students, in the teaching and learning process, for assessment, the development of educational structures, the development of research structures, the development of management structures and the development of academic culture). Correspondingly, The authors identified and categorized the challenges that AI creates for higher education.

Concerning the intended research, several limitations are significant. First, the statistical population was limited, and only people with characteristics such as being PhD students, studying at Tehran University and being experts in AI could be considered the statistical population. Second, caution should be exercised when generalizing the results due to the limited statistical population (PhD students from Tehran University). Third, the problem of accessing some students due to their participation in research grants, academic immigration, etc.

The innovation of the current research is that the authors identified the opportunities and challenges that AI creates for higher education at different levels. The findings of this study also contribute to the enrichment of existing knowledge in the field regarding the effects of AI on the future of higher education, as researchers need more understanding of AI developments in the future of higher education.

This study aims to analyze the two-dimensional ferrofluid flow in porous media. The effects of changes in parameters such as permeability parameter, buoyancy parameter, Reynolds and Prandtl numbers, radiation parameter, velocity slip parameter, energy dissipation parameter and viscosity parameter on the velocity and temperature profile are displayed numerically and graphically.

By using simplification, nonlinear differential equations are converted into ordinary nonlinear equations. Modeling is done in the Cartesian coordinate system. The finite element method (FEM) and the Akbari-Ganji method (AGM) are used to solve the present problem. The finite element model determines each parameter’s effect on the fluid’s velocity and temperature.

The results show that if the viscosity parameter increases, the temperature of the fluid increases, but the velocity of the fluid decreases. As can be seen in the figures, by increasing the permeability parameter, a reduction in velocity and an enhancement in fluid temperature are observed. When the Reynolds number increases, an increase in fluid velocity and temperature is observed. If the speed slip parameter increases, the speed decreases, and as the energy dissipation parameter increases, the temperature also increases.

When considering factors like thermal conductivity and variable viscosity in this context, they can significantly impact velocity slippage conditions. The primary objective of the present study is to assess the influence of thermal conductivity parameters and variable viscosity within a porous medium on ferrofluid behavior. This particular flow configuration is chosen due to the essential role of ferrofluids and their extensive use in engineering, industry and medicine.