{"id":26495,"date":"2025-11-03T10:05:00","date_gmt":"2025-11-03T08:05:00","guid":{"rendered":"https:\/\/www.carnisostenibili.it\/?p=26495"},"modified":"2025-11-03T11:38:02","modified_gmt":"2025-11-03T09:38:02","slug":"the-researcher-in-the-age-of-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/www.carnisostenibili.it\/en\/the-researcher-in-the-age-of-artificial-intelligence\/","title":{"rendered":"The Researcher in the Age of Artificial Intelligence"},"content":{"rendered":"\n

Artificial Intelligence is revolutionising the way science is produced, analysed, and disseminated. It’s not the future, but a present reality.<\/em><\/p>\n\n\n\n

Artificial intelligence (AI)<\/strong> is revolutionising<\/strong> the way science<\/strong> is produced, analysed, and disseminated. This is not a distant prospect but a present reality: the tools already exist and are increasingly integrated into the daily work of researchers. The real question, therefore, is what role humans will play<\/strong> in a context where AI is fully operational.<\/p>\n\n\n\n

Every research project starts from existing knowledge, traditionally acquired through a long and complex literature review. AI, however, can scan millions of articles in just a few moments<\/strong>, extract the most relevant information, and build an updated overview of current knowledge, with a research capacity and memory that no human being could ever achieve<\/strong>.<\/p>\n\n\n\n

Yet AI does not stop at summarising what we already know: it can identify knowledge gaps and suggest the most relevant research objectives<\/strong>, highlighting which studies might have the greatest scientific, social, or environmental impact.<\/p>\n\n\n\n

The next step is the experimental design, an area where AI is already widely applied. It can calculate sample sizes, optimise randomisation, anticipate confounding variables, and propose appropriate statistical approaches<\/strong>. It will then be up to the researcher to concretely implement the plan developed by AI, carrying out experiments in the field or in the laboratory.<\/p>\n\n\n\n

Data collection brings further support: AI will analyse the data, detect patterns and correlations, and draw conclusions<\/strong>. Moreover, it can draft article manuscripts, enrich them with updated bibliographies, and even recommend the most suitable journal <\/strong>for publication, considering impact factor, review times, costs, and thematic relevance. In scientific dissemination too, AI will provide powerful tools: abstracts, posters, presentations, as well as outreach and multimedia materials<\/strong>, all produced quickly and effectively.<\/p>\n\n\n\n

This raises a crucial question: what will remain for the researcher?<\/strong> If AI can search, select, design, analyse, write, and disseminate, what space is left for humans? The answer lies in the very essence of science: asking new questions, giving meaning and direction to data<\/strong>. AI may guide and support, but it will always be the researcher\u2019s task to give value to what AI produces.<\/p>\n\n\n\n

The future of science, therefore, will not belong exclusively either to machines or to humans, but will emerge from<\/strong> collaboration<\/strong>. The challenge is not to choose between artificial and human intelligence, but to learn how to make them work together<\/strong>.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Source: Newsletter EAAP n. 282<\/a><\/p>\n\n\n\n

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