Artificial Intelligence (AI) in Scientific Research

Abstract

Introduction: Artificial Intelligence (AI) has emerged as a key tool in academic research, providing new possibilities for data analysis, task automation, and knowledge generation. Its implementation allows for optimizing research processes, improving efficiency in information collection, and facilitating access to diverse sources. However, its use also poses significant challenges, such as data reliability, technological dependence, and the ethical implications associated with transparency and accountability in knowledge production. This study analyzes the role of AI in research, addressing its potential, limitations, and the ethical considerations that arise in its application.

Objective: To analyze the role of Artificial Intelligence (AI) in research, considering its use as an academic tool, the challenges it presents, and the ethical implications associated with its application in the research field.

Method: This study, based on the interpretive paradigm, employs a qualitative approach and the hermeneutic method to analyze the role of Artificial Intelligence (AI) in academic research, considering its uses, challenges, and ethical implications. Through a documentary analysis of academic sources, critical interpretation will be applied to identify patterns and meanings regarding the role of AI in knowledge production. The process will be carried out in three stages: source collection, critical reading and interpretation, and discussion of findings.

Results: The analysis indicates that AI has become an indispensable tool in various disciplines, streamlining the processing and interpretation of large volumes of data. Previous research has shown that its application contributes to improving the accuracy of scientific studies, reducing bias in source selection, and facilitating the replicability of experiments. However, it was also identified that the use of algorithms can generate problems related to the opacity of processes, the reproduction of algorithmic biases, and the need for human oversight to ensure the validity of results.

Analysis: The impact of AI on research is ambivalent. On the one hand, it allows for faster and more efficient access to knowledge, expanding the possibilities for analysis and prediction in various fields. However, their implementation requires a critical approach, as overreliance on these technologies can compromise researchers' analytical capacity. Furthermore, ethical implications related to intellectual property, data privacy, and equity in access to the technology must be considered to ensure responsible use of AI in academic research.

Conclusions: AI represents a highly useful tool in research, with the potential to optimize processes and improve the quality of studies. However, its application entails challenges that must be addressed with appropriate strategies, including regulating its use, training researchers in the use of these technologies, and promoting ethical principles that guide their implementation. In this sense, it is essential to balance