Research objectives and hypothesis/research questions:
The aim is to assess the ability of the ChatGPT 4.0 system to imitate a human having a conversation and to reflect on the conducted experiment. Two hypotheses were formulated for the purposes of the study. The first assumed that ChatGPT 4.0 can effectively imitate a human while having a conversation on a given topic. The second hypothesis concerned the influence of the participants’ answers on the assessment of ChatGPT 4.0’s ability to pass the Turing test.

Research methods:
The experiment consisted of conducting three interactive conversations and voting, in which one of the interlocutors was ChatGPT, and collecting reflections in an evaluation survey. The results of the experiment confirmed the hypothesis that the bot can effectively imitate a human. Confirmation should be treated as a statement of the maturity of generative technology.

Main results:
Based on the conducted Turing test, it can be concluded that the machine meets the standards of deceiving a human by more than 30% in accordance with the developed Turing method. While for the first goal the experiment showed a positive answer to the hypothesis, i.e. ChatGPT can effectively imitate a human during a conversation on a given topic, the method itself seems to be unsuited to contemporary needs. It is a very good inspiration for contemporary researchers. Regardless of its creator, A. Turing, it would probably be used in a different form, which does not deny the inventiveness of the author, who formulated the principle, the idea of which remains relevant after 75 years. The value of the Turing test is to predict the needs and possibilities of digital technologies in the 1940s, i.e. in the analog era.

Implications for theory and practice:
The study showed the maturity of the language model and was an opportunity to reflect on the use of generative technologies in education. A warning may be the identified problem of knowledge verification during exams, which is being discussed in the academic community, which will probably contribute to the faster absorption of generative solutions in education.