This seminar explores how to treat artificial intelligence as a configurable research method rather than a chatbot or generic software tool. It provides a rigorous framework for validating AI-enabled workflows, designing defensible studies, and making scientific claims that remain consistent with the available evidence.
This workshop explores how AI can be used responsibly across the research lifecycle, from literature discovery and question development to computational analysis, writing, and publication and gives a practical framework for deciding what AI can assist with and what must remain human-led.
This seminar explores how agentic AI can be integrated into academic research as a disciplined, verifiable, and human-led method rather than a generic productivity tool. It provides practical frameworks for responsible use, workflow verification, and multi-agent research design across literature work, analysis, writing...
Artificial intelligence (AI) is transforming scientific practice. What started as a passive data-analysis tool has become something closer to an active partner, now capable of generating hypotheses, planning experiments, and coordinating multi-step workflows. This manuscript explores how large language models (LLMs) an...
Graziella Orrú, Pietro Pietrini, Giuseppe Sartori· Forensic Sciences Research· 0 citations
This seminar explores the conceptual foundations of artificial intelligence and offers practical guidance on using and evaluating AI tools responsibly in academic research.
This seminar explores how to make AI-assisted academic research verifiable, meaning inspectable, reproducible, and defensible by turning vague prompting into bounded, auditable workflows. It offers practical methods for building audit trails, testing workflow reliability, and documenting AI use in ways that support pub...
This workshop explores how autonomous AI agents can support scientific research in R, from conceptual distinctions between chat systems and agentic workflows to practical deployment with open-source tools (OpenCode). It is designed for researchers who want to use AI critically, securely, and effectively in data-intensi...
R. Maestri· 0 citations
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