Acceso usuarios
[1]
Colombia
This article examines the opportunities and challenges presented by artificial intelligence in the scientific evaluation of the social sciences, a field that faces difficulties in quantifying the impact of its output due to the complexity and qualitative nature of its subjects. Unlike the natural sciences, social sciences do not always align well with traditional metrics, such as citations or impact indices. Artificial intelligence, through advanced tools like natural language processing and machine learning, offers alternatives to enhance these evaluation processes. This study follows an exploratory methodology, grounded in a critical literature review and content analysis, aiming to identify the potential of artificial intelligence for measuring academic and social impact within the social sciences. The literature review includes analyses of academic sources and policy documents and is structured around three key areas: improvements in evaluation metrics, innovations in social impact analysis, and proposals for implementation in social sciences. The article concludes that, although artificial intelligence enables more comprehensive evaluations, its application presents ethical challenges, especially regarding algorithmic biases and system transparency. As an original contribution, the article proposes a theoretical model to integrate qualitative and quantitative methods into a more equitable and thorough evaluation adapted to the unique nature of the social sciences. It emphasizes the importance of developing AI tools designed ethically and collaboratively.
