This talk explores the transformative role of Generative Artificial Intelligence (GenAI) in the scientific research lifecycle. From the formulation of research ideas to the writing of academic articles and grant proposals, GenAI tools, such as large language models (LLMs), are emerging as collaborative agents capable of enhancing productivity, creativity and analytical rigor. The presentation introduces the foundational concepts of AI and GenAI, then delves into practical applications across various stages of research: ideation, literature review, hypothesis generation, experimental design, data analysis and scientific writing. A particular focus is given to prompt engineering techniques, which enable effective interaction with LLMs to obtain coherent and contextually aligned outputs, and to demonstrating state-of-the-art GenAI tools. The talk also addresses key limitations such as hallucinations, privacy concerns, and the ethical responsibilities of researchers. It concludes with a critical reflection on the implications of GenAI for the future of academic research, encouraging PhD students to adopt a mindful and evidence-based approach in leveraging these tools.

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Generative AI,Research