The integration of artificial intelligence (AI) into academic research is significantly transforming the ways in which data are generated, analyzed, and interpreted. While AI offers numerous benefits, such as the automation of repetitive processes, the analysis of large volumes of information, and the identification of relevant patterns, this technological advancement also raises a series of ethical dilemmas that cannot be ignored. Among the key concerns are the lack of algorithmic transparency, the risk of perpetuating biases through machine learning models, ambiguity regarding responsibility for AI-generated decisions, and issues related to data privacy.

This article aims to explore these aspects, highlighting the tensions between technological innovation and ethical responsibility. At the same time, it examines a range of initiatives and best practices that can guide the responsible implementation of AI in research activities. By identifying clear courses of action, from the ethical training of researchers to institutional regulation, the article contributes to the development of a reflective framework essential for sustainable and ethically responsible academic research in the digital age.