Knowledge and Generative Artificial Intelligence

Contributions from Edgar Morin’s Theory of Complexity

Abstract

Complexity theory distances itself from simplistic and dichotomous explanations, proposing an analysis that recognizes the duality and interdependence of phenomena (Morin, 2005). Generative Artificial Intelligence (GAI), understood as a deep learning technology that generates human-like content (Michel-Villarreal et al., 2023) in a field of science that focuses on the study of automated intelligence construction (Salinas-Navarro et al., 2024) and is part of the context of Large Language Models (LLM).
These models use machine learning to generate content and adopt natural language processing (NLP) techniques, enabling computers to predict, interpret, and generate texts. This new phenomenon transforms how we interact with machines (Baidoo-Anu & Owusu Ansah, 2023), and the relationships between humans/machines/knowledge raise reflections on how to deal with challenges thinking for/about humans, exemplifying the need for a study that embraces complexity and uncertainty.
The knowledge society, as discussed by (Morin, 2008), is characterized by a constant search for innovation and understands the multidimensional nature of knowledge. This society, driven by digital technologies and AI, faces the challenge of integrating advances with human/social needs and, in this sense, artificial intelligence, by introducing new forms of interaction and learning, creates needs that require critical reflection on its current and future impact on the production and dissemination of knowledge (Memarian & Doleck, 2023).
This article takes a qualitative approach of an exploratory nature, using bibliographic research as a procedure for critical analysis of the issue and a case study on an experience of using artificial intelligence in a practical workshop with undergraduate students. The objective of this article is to discuss the extent to which knowledge, in times of generative artificial intelligence, can be understood in light of Edgar Morin's theory of complexity and to articulate theory and practice that can guide discussions about GAI in education. In this sense, by recognizing the complexity and interdependence of phenomena (GAI-Education), it is possible to develop collaborative pedagogical approaches between man and machine that take advantage of the transformative potential of GAI, promoting an education that responds to contemporary challenges in an integrated manner. To exemplify and put into practice possible uses of AI Gen in education, we present an activity experience in the form of a workshop, carried out with undergraduate students in an elective course. The theme of the course is creativity, and the students are from different undergraduate programs. The challenge was to integrate generative AI and creativity in the construction of knowledge.
In this sense, the theory of complex thinking offers an appropriate theoretical framework for exploring the implications of IAGen in education through the dialogical, recursive, and hologramatic principles proposed by Morin (2008), which provide a basis for understanding how IAGen can contribute to the process of knowledge construction by articulating actions that enable interaction. Dialogism recognizes the dualities inherent in the phenomenon, while recursion discusses what is really involved in the process and holography highlights the interconnection between parts and the whole.
The first topic of this study presents a discussion on generative artificial intelligence and humans in this context, while the second topic discusses knowledge as a multidimensional phenomenon, as presented in Edgar Morin's (2008) work The Method: knowledge of knowledge by Edgar Morin (2008). The third topic engages the reader in the nuances of the method in the scientific research process, and then delves into the last topic, connecting the three principles of complex thinking to IAGen.