PHILOSOPHY AND GENERATIVE ARTIFICIAL INTELLIGENCE MODELS: (UN)SURMOUNTABLE LIMITATIONS OF INFLUENCE

Abstract

Summary. The research focuses on optimising the interaction between the human being and generative artificial intelligence models {using Perplexity.ai, chatGPT, and Leonardo.ai as examples} through the application of philosophical knowledge fundamentals. The research purpose is to identify and elaborate on the possibilities of (un)surmounting the limitations of generative artificial intelligence models through philosophy. The research methodology comprises methods, principles, and approaches that form a complementary network. The main qualitative methods applied in the research include questioning–and–answering {for substantiating the interdependence of philosophy and generative artificial intelligence models}, analysing {for detailing the case of applying GenAI models in university philosophy teaching}, synthesizing {for forming an algorithm to facilitate interactions between the human being and the GenAI model}, phenomenological {for essential understanding of the operating principles of GenAI models and optimal interactions with them}, hermeneutical {for interpreting and explaining the results generated from the collaboration between the human being and a specific GenAI model}, pragmatical {for formulating an acceptable prompt to generate the necessary output; for creating conditions that enable the mutual transformation of both – the human being and the GenAI model}, comparative {for contrasting the results obtained from standard information searches via Google.com and GenAI models}, etc. Additionally, principles {integrity, conditional objectivity, causality, justification, situationality} and approaches {pluriversal, interdisciplinary} were implemented in the research. The theoretical–and–practical parameters of scientific novelty are determined by the structure of the presented study, as well as by two aspects: 1) the urgent and prospective necessity for the human being to simultaneously master new information technologies, optimize interactions with them, and preserve–and–develop one’s natural mind–intellect, which, through applicability, will enable the formation of prompts for generative artificial intelligence models that, although operating on algorithmic principles, can be purposefully trained through prompted and generated humandirected vectoring; 2) the postulation of philosophy as a praxisoriented academic discipline {the university teaching of which can and should be adapted to epochal changes and future trends}, thereby facilitating the heterogeneous interactions among human beings {with peers; with artificial intelligence constructs}. Conclusions: 1) generative artificial intelligence models represent both a breakthrough for humanity and a conundrum for the future; 2) all users of GenAI models should primarily possess a well-developed natural intelligence, which will result in unconventional queries, ‘correct prompts’, and the adaptation of generated content to suit personal, educational, and/or professional needs; 3) only philosophy fosters the individual tuning of one’s natural intelligence and its optimal application through the questioning-and-answering method, critical thinking, analysis and synthesis, forecasting, creativity, etc., which collectively enable the essence understanding of each phenomenon {object, event, process} and assist the human being in defining their own role in the world; 4) under conditions of critical and purposeful application, generative artificial intelligence models can be integrated into university philosophy teaching to promote the individual intellectual growth of both students and educators, as well as to enhance exclusively human qualities.