The Use of Artificial Intelligence in Academic Research: The Transhuman Co-Production Between Human and Technological Intelligence

The advancement of artificial intelligence (AI) has significantly impacted academic production, serving as a support tool for formulating research questions, data analysis, and the writing of scientific articles. However, the use of these technologies requires a careful approach to ensure research integrity and to address associated ethical challenges. This study aims to analyze the potential of AI in scientific research through a transhuman co-production approach, examining the opportunities, challenges, and ethical implications of its use. This study adopts a narrative review of the literature and institutional guidelines on the use of AI in academic writing, focusing on documents from universities featured in the World University Rankings 2025. These materials were analyzed using thematic analysis, aiming to identify potential applications and patterns in ethical recommendations. The AI tools ChatGPT and NotebookLM were used, under the authors’ supervision, for text revision, data synthesis, and comparison. All content was produced and reviewed by or under the direct supervision of the authors. This indicates that AI and generative artificial intelligence (GAI) have revolutionized scientific research, enhancing academic writing, data analysis, and literature review. These technologies automate the search and synthesis of academic literature, facilitating the identification of knowledge gaps. Additionally, advanced machine learning algorithms enable more precise analyses, identifying patterns in large datasets. In academic writing, AI assists in hypothesis formulation, text structuring, and clarity enhancement, while also suggesting research topics and optimizing writing processes based on the researcher’s academic background. However, the use of AI raises significant ethical challenges. Issues such as consent and privacy require compliance with data protection regulations, while algorithmic bias can compromise the impartiality of results. Moreover, the complexity of AI models may hinder research reproducibility. To mitigate these challenges, researchers must ensure transparency in AI usage and guarantee reliability in their results through human supervision. The continuous development of institutional guidelines and researcher training is essential for the proper integration of AI into academia.