The article offers an integrated historical–philosophical reconstruction of the evolution of the philosophy of science from its ancient foundations to post- nonclassical and contemporary approaches in the age of artificial intelligence (AI). It shows how the ontology/epistemology/axiology triad structures the field, while successive methodological paradigms (rationalism, empiricism, positivism, neo- Kantianism, logical empiricism/neo-positivism, and post-positivism) repeatedly rearticulate the criteria of scientificity. Particular attention is paid to global evolutionism and synergetic as integrative frameworks for understanding open, non- linear, and far-from-equilibrium systems. The analysis argues that digital transformation and the diffusion of AI require a clarified ontology of cognition, a principled distinction between computation and thinking, and an ethical infrastructure (virtue ethics, responsibility, relationality, and algorithmic participation) to safeguard human dignity within hybrid human–algorithm decision loops. The practical upshot is an orientation toward explainability, reproducibility, accountability, and open procedures of truth assessment that secure science as a public practice rather than a collection of statistically adequate outputs.
Varypaiev et al. (2025) studied this question.