The actual problems of digital technology in the machine learning field are related to the expansion of autonomous intelligent systems in respect to cognitive activity. The growth in the volume of information in modern networks requires new formats for working with knowledge, based on hybrid systems and neural models of artificial intelligence (AI). Neural network training involves the formation of experience of semantic orientations in multi-valued contexts using self-correction mechanisms. In the modeling of virtual machine learning environments, the role of the interface increases. The article examines the cognitive potential of the interface as a factor shaping the boundaries and modes of functional autonomy of artificial intelligence systems in machine learning. The interface is treated not as a neutral technical channel of information transfer but as a semantic matrix that programs a sequence of cognitive operations – selection, context-binding, inference, revision, and action. Particular attention is given to the temporal dimension of the interface's cognitive potential. The authors introduce the concept of chronotope (Mikhail Bakhtin) as a model for organizing the interplay of space and time within the virtual learning environment, and analyze temporal event models grounded in metaphors, image schemas, and grammatical constructions. The communicative design of the interface is discussed through the lens of cultural archetypes (Carl Gustav Jung), which shape “trusted agent” models in human–AI dialogue. Factors determining the interface's cognitive function – structuring the space of relevant information, temporal dynamics, inference formation, optimization regimes, and actor motivation – are systematized. The authors conclude that the interface's cognitive potential establishes the conditions for the subject's semantic orientation and the digital agent's goal-directed behavior, ensuring the coupling of human and artificial intelligence in the expanded production of knowledge.