Ethical limits and suggestions for improving the use of AI in scientific research, academic publishing, and the peer review process, based on deontological and consequentialist viewpoints

This study provides a comprehensive analysis of researchers' perspectives on AI integration across theoretical and practical research, academic publishing, and its future role, highlighting ethical considerations shaping its adoption. The methodology used a mixed approach, using a questionnaire distributed to researchers from various scientific disciplines. Quantitative data were analyzed statistically, and qualitative responses underwent thematic analysis to ensure a robust understanding of the findings. Results reveal that AI is viewed as a powerful tool for enhancing efficiency in research, particularly in data analysis (61.9%) and hypothesis generation (45.2%). However, ethical concerns, including bias, transparency, and trust, were noted as significant challenges, with 38.1% emphasizing the need for improved ethical frameworks. In academic writing, 81% of respondents supported AI use with proper acknowledgment, while 76.2% expressed openness to AI-assisted peer review under human supervision. The future of AI is seen as complementary to human expertise (69%), with its potential strongest in data analysis, simulations, and innovation in research tools (57.1%). Key barriers include limited access to AI tools (47.6%), high costs (38.1%), and insufficient technical skills (45.2%). This study’s innovation lies in its integration of ethical theories, deontology and consequentialism, as a framework to evaluate AI’s role in research. It offers practical recommendations to foster responsible AI adoption, including ethical training, interdisciplinary collaboration, and enhanced accessibility to AI tools. Addressing gaps in ethical guidelines and emphasizing AI’s potential to complement human creativity, this research contributes valuable insights to the evolving discourse on AI in scientific research.