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Generative AI applications enable different useful functions for learning based on the generation of content. This paper aims to offer a theoretical framework to understand them as tools for cognition (TFC), framed in the perspective of sociocultural theory and activity theory and distributed cognition. This perspective exemplifies how thought is not only packaged inside the individual's mind, but is distributed among subjects, objects, and artifacts, where tools mediate human activity and help in the executive functions of thought. The perspective of TFC embodies an educational socio-constructivist vision where learners build their knowledge with these tools, taking advantage of their affordances. It is the concept of learning "with" technology instead of the traditional vision of learning "from" technology, where technological applications are limited to providing information and evaluating the students' responses. Finally, we describe Generative AI applications as TFC following David Jonassen's pragmatic and pedagogical criteria, i.e. the capacity of knowledge representation of different subjects, the facilitation of critical and meaningful thinking (based on questioning and prompting) and how they enable complex thinking among students when used in learning tasks, only when executive functions are on the side of the learner.

Understanding institutional change remains a major theme on the agenda of social sciences. Thus, this working paper analyzes, through a comparative perspective, the different institutional transformations of the Portuguese and Spanish intelligence systems after the transifition to democracy and after 9/11. We applied an institutional change model centered on the relationship between ideas and interests, and used process tracing as a technique in order to identify the key drivers and inhibitors that explain the different institutional paths observed in the intelligence systems of both countries after two critical junctures. [ABSTRACT FROM AUTHOR]

The paper presents an approach of Case Based Reasoning, where it was born from the problems of human memory solution model, thus inspiring the creation of programs to address current problems. It is a technique that aims to solve a problem from the knowledge of past problems, comparing them with the new problem, and adapting well to a new solution. This will cover, from its definition, the techniques used, the stages of its development, as well as its advantages and disadvantages.