Generative AI transforms researcher learning

150 researchers from various disciplines explored the impact of this technology on academic and scientific training in an event held at the University of Navarra. The meeting "Generative AI in the learning process of the researcher: new informational competences", addressed the opportunities and challenges that these technologies present in the development of knowledge, such as over scientific productivity, a new technological literacy, biases or cognitive sedentarism, among others.

Moderated by Isabel Iribarren, director of the Library, it counted with Alejandro N. García Martínez, researcher in Sociology at the School of Philosophy and Letters, and Rubén Armañanzas Arnedillo, senior researcher in Digital Medicine at the data Science and Artificial Intelligence Institute (DATAI).

Keys to AI literacy

The experts stressed the importance of developing new skills in an environment where artificial intelligence plays a core topic. García Martínez stressed that "prompts engineering, which consists of knowing how to communicate effectively with AI, is fundamental to improve results and optimize its potential". He also stressed the need to foster metacognitive skills that promote critical thinking: "It is not enough to use AI, it is necessary to question its results, validate the information and ensure that the tool is used as a means of support, and not as a substitute for human reasoning".

For his part, Armañanzas stressed the importance of information literacy and bias recognition in generative AI. "These tools can be a starting point for research, but it is essential to go deeper than the first results and use AI with a critical view," he explained. He also warned about the "bubble effect", a phenomenon in which AI prioritizes the most popular content, generating a biased view of knowledge.

Both experts agreed that AI should be understood as a complementary tool and not as a substitute for scientific data instructions or traditional source assessment methods. Regarding how artificial intelligence is revolutionizing scientific productivity, allowing to broaden the scope and quality of research, they pointed out the need to rethink the processes of management, assessment and access to academic content.

Cognitive outsourcing and critical thinking in the age of AI

While AI facilitates task delegation and allows researchers to focus on more creative aspects, its overuse can generate a passive dependence on technology. "Historically, technology has made it possible to outsource processes so that people can concentrate on strategic tasks, but cognitive sedentarism arises when this dependence reduces the capacity for reflection and autonomous learning," explained García Martínez. The expert insisted on the need to find a balance that enhances analysis without falling into passivity.

Both experts agreed that AI can improve the way we acquire knowledge, but it must be integrated with other more traditional learning methods, and insisted on the importance of self-reflection and critical thinking in the training of researchers. "The use of generative AI requires an active approach . We must ask ourselves how we learn and what we need to acquire knowledge. These tools offer probabilistic answers that can vary, so we must develop cognitive skills that allow us to interpret, compare and critically evaluate the information," said Armañanzas.

The event also addressed the impact of AI on academic assessment , scientific communication and interdisciplinary partnership . The experts agreed that, although these tools can optimize research, it is essential to train researchers in the manager and critical use of the technology.