Prospects for the implementation of artificial intelligence in the educational processes of oil and gas universities

Abstract

In the context of accelerated digitalization of the oil and gas industry, accompanied by the transition to intelligent fields, digital twins and the implementation of predictive analytics, neural network algorithms for seismic data interpretation, and robotic complexes for operations in hazardous zones, the transformation of higher technical education acquires a dual character in which artificial intelligence simultaneously acts as both an object of study and a tool for the radical transformation of the content, methods, and architecture of the pedagogical process in specialized universities. Traditional lecture-seminar models are increasingly lagging behind industry requirements for competencies related to big data processing, optimization of technological processes, and decision-making under conditions of geological uncertainty. This highlights the need to integrate adaptive learning platforms, intelligent simulator systems for modeling emergency situations, generative models for personalized educational materials, virtual laboratory environments, and automated assignment checking systems with detailed feedback. Analysis of the attitudes of educational process participants reveals a pronounced asymmetry in the perception of the prospects of these areas: the greatest consensus is formed around intelligent simulators as a means of developing practical skills without excessive costs and risks. In contrast, automated calculation checking receives maximum support from students but faces noticeable skepticism from teachers. Systems of proctoring and intellectual performance analysis are evaluated mostly negatively due to their interpretation as factors of supervision and restriction of academic freedom. The structure of implementation barriers demonstrates the dominance of insufficient digital literacy among the teaching staff, the lack of adapted software, methodological recommendations, and institutional inertia. At the same time, assessments of the risks of reducing students’ critical thinking diverge sharply: this risk is highly significant for teachers but minimally recognized by students, which reflects differences in professional experience, roles, and cognitive attitudes. The actual level of application of intelligent tools remains marginal, especially among teachers, with a noticeable predominance of generative text models and programming assistants among students and extremely low coverage of specialized geological services and adaptive platforms. The infrastructural imbalance exacerbates the situation, as computing resources are concentrated mainly in the research sphere, leaving the educational component of artificial intelligence use on the periphery. Comparison of service, subject-specific, and integrative models of integration reveals the academic environment’s preference for the auxiliary use of technologies without deep restructuring of curricula, in contrast to the integrative approach demanded by industry experts for the formation of cross-cutting competencies that permeate the entire professional profile. These discrepancies determine the need to evolve the teacher’s role toward facilitating cognitive activity, developing formats for the use of artificial intelligence that stimulate expert verification and critical reflection on machine-generated results, strategic reallocation of resources, advanced training of personnel, and a phased transition to an integrative paradigm. This necessitates a systemic nature of the upcoming transformation of oil and gas education and its adaptation to the technological challenges of the industry.