Rejection or integration of AI in academia: determining the best choice through the Opportunity Cost theoretical formula.

The abrupt evolution of Artificial Intelligence (AI) in academia has spurred a complex debate regarding its rejection or integration in academia. This study aims to portray a comparative analysis of the risks associated with the integration of AI and the missed opportunities in the absence of AI in academic settings. Utilizing the economic theory of Opportunity Cost as a theoretical framework, the study investigates whether the potential gains from AI adoption outweigh the losses. The Opportunity Cost is a fundamental principle in economics, which determines the best alternative between two choices in a single context, guiding individuals and organizations to make the best choice. Adopting a qualitative methodology for this systematic review, the research employs content analysis. Using the Boolean formula, the researcher constructed precise search queries to retrieve relevant literature across six databases and applied specific protocols of inclusion and exclusion; from an initial pool of 260 existing literature, 72 relevant studies were selected based on bibliometrics for final synthesis to avoid the fallacy of composition, a wrong decision about AI. The findings indicate that the blessings of generative AI in academia significantly outweigh the risks, leading to the decision to integrate AI in academia. Although the study recorded negative aspects, these are not substantial enough to undermine the overall positive impact of AI, as it holds considerable promise for fostering dynamic academic environments. This research aims to inform and shape user attitudes toward its adoption in academia and provides valuable insights for academic institutions, educators, and policymakers. [ABSTRACT FROM AUTHOR]