From Data Analysis to Creative Arts: The Ubiquity and Impact of Artificial Intelligence in Academia - Lainjo, Bongs (International Journal of Education, Teaching, and Social Sciences, 2024)
Integrating Artificial Intelligence in academia has revolutionized various fields with new opportunities for innovation, research, and learning.  The capability of AI to analyze enormous amounts of data at such incredibly short times contributes to research advancement across natural sciences, humanities, social sciences, engineering, and healthcare sciences. For instance, in natural sciences, AI algorithms support various types of data analysis and simulation, helping to make new discoveries and provide methods and new approaches to look at existing research methods. AI advances in social sciences employ prediction modeling and machine learning to enhance economic models and other behavioral analyses. AI has presented humanities advancements in text analysis and interpretation of history work, augmenting the research based on historical data with data analysis. In engineering and technology, AI's role is twofold: enhancing physical security and, at the same time, posing new threats in the form of complex cyber threats. In a related context, AI’s application for diagnosis and treatment planning has been observed in the healthcare sector. It has shown the potential capability of improving the care of patients far beyond any imagined capabilities. Nevertheless, the application of AI in academia comes with some challenges. Privacy, protection, ethical views, and prejudice enhancement are some of the most significant issues that should be considered.  Despite these challenges, AI creates multi-professional collaboration and advances in knowledge and performance in various scientific disciplines. AI continues to thrive in the future of academia, as future advancement holds possible new research horizons, educational improvement, and world problem-solving. With the rapid evolution of AI, its incorporation into academia and its abuses, biases, and risks need to be constantly reviewed