The Modern Stage of Integrating Artificial Intelligence in Students’ Autonomous Work: Challenges and Solutions

The purpose of the study is to develop a model for integrating artificial intelligence in students’ autonomous work at non-linguistic universities, based on the principles of pedagogical management, academic integrity, and individual learning routes. To achieve this aim, the following objectives were set: assessing the effectiveness of using artificial intelligence tools for developing linguistic competence, fostering a critical attitude toward the results produced by artificial intelligence, identifying key factors that facilitate or hinder this process, selecting promising directions for the use of artificial intelligence, and evaluating potential risks. Materials and methods. The study employed the following methods: an analysis of domestic and foreign scholarly publications and practical research on the subject, a Likert-scale questionnaire survey, quantitative content analysis, qualitative analysis to identify emerging issues and formulate solutions, graphical processing and semantic interpretation of the obtained results. Statistical analysis of the survey data was conducted, followed by graphical presentation of the findings. The research materials comprised the results of a survey of the students conducted at the Financial University under the Government of the Russian Federation and Moscow Polytechnic University. Results. The results of the study are presented in the form of an author-developed model for integrating artificial intelligence in students’ autonomous work at non-linguistic universities. The proposed model positions artificial intelligence as an element of the educational environment used to develop linguistic, digital, and metacognitive competencies. It comprises the following interrelated components: diagnostic, design, executive, reflective, and evaluative. This model enables pedagogically managed integration of generative artificial intelligence in students’ autonomous work in a foreign language and allows to use the potential of AI for developing language competencies without diminishing the level of learners’ autonomy. The implementation of the model contributes to the formation of critical thinking skills, digital literacy, reflection, and responsible use of AI technologies in both academic and professional contexts. The outcomes of the study include a classification of current trends in the use of artificial intelligence tools in foreign language education, a systematization of effective tools for use in autonomous foreign language study, and the identification of vectors for controlling the respective contributions of learners and artificial intelligence to the co-created language product. Conclusion. The study convincingly demonstrates that the primary advantage of using artificial intelligence in autonomous foreign language work is in significant time savings and better quality of completed assignments. This underscores the role of artificial intelligence as a tool that optimizes the learning process, enabling students to receive prompt feedback and focus on more complex tasks. The authors conclude that it is necessary to preserve the role of the lecturer as one of the key participants in the educational process. Based on the data obtained, it appears feasible to conduct an experimental foreign language teaching program using specific artificial intelligence tools in order to determine the respective contributions of AI and students to the jointly created language product.