Psychological aspects of teacher-student interaction in the use of artificial intelligence in English language classes

Abstract

The aim of this study was to provide a theoretical framework and conduct an empirical analysis of the psychological characteristics of the interaction between teachers and students when using artificial intelligence (AI) tools in English language classes at higher education institutions. The relevance of the topic stems from the transformation of the roles of participants in the educational process under the influence of digital technologies, which are changing the nature of communication, the motivational sphere and the emotional climate of the learning environment. The research methods included a theoretical analysis of the academic literature on the psychology of pedagogical interaction, the characteristics of perceptions of AI technologies, and their impact on interpersonal processes in language education. The empirical part of the study was conducted at Lviv Polytechnic National University among 24 first- and second-year undergraduate students. A set of psychodiagnostic methods was applied: the 'Group Cohesion Index' (adapted by L. Umansky) to assess communicative interaction and the level of group integration; M. Ginzburg's method to study the structure of learning motivation; the SPANE (Scale of Positive and Negative Experience) to measure emotional experiences. Additionally, observations were made of students' behaviour whilst working with AI tools. Data analysis involved descriptive statistics, correlation analysis (Spearman's rank correlation coefficient) and qualitative interpretation of the results, in compliance with ethical standards of anonymity and voluntary participation. The findings of the study revealed a transformation in pedagogical interaction under the influence of AI. Theoretical analysis showed that the teacher is shifting from the role of the primary source of knowledge to that of a facilitator and coordinator, whilst students are gaining greater autonomy and personalised learning pathways. AI tools (chatbots, generative models, automatic feedback systems) help to reduce communication anxiety, boost confidence in foreign-language communication, and engage students with low initial engagement levels. Empirical data revealed an average level of group cohesion (mean score of 12.79 points), with heterogeneity: students with high scores demonstrated active collaboration and the use of AI as a resource for collective discussion, whilst those with low scores tended to focus on individual work, which sometimes exacerbated isolation. The motivational structure was characterised by the dominance of intrinsic motives ('to know and be able to do more' – in a third of the sample), which were reinforced by personalised AI feedback; extrinsic motives (assessment, approval) retained a supporting role. Analysis of emotional reactions using the SPANE scale revealed a predominance of positive experiences (mean 3.59 points, modal 'often' – 41%), over negative ones (mean 2.23 points, modal 'rarely' – 38%), indicating a reduction in anxiety and an increase in psychological comfort. Correlation analysis confirmed a moderate positive relationship between the level of cohesion and communicative activity (rs ≈ 0.7), between positive affect and intrinsic motivation (rs ≈ 0.65), as well as an inverse relationship between negative affect and psychological comfort (rs ≈ –0.60). The study identified resources (reduction in emotional stress, development of metacognitive skills, engagement of passive students) and risks (excessive reliance on AI, reduction in face-to-face interaction, comparing oneself to algorithmic standards). Conclusions. The study indicates that the integration of artificial intelligence tools into English language teaching has significant potential for optimising psychological interaction, enhancing motivation, emotional safety and communicative engagement among students. At the same time, the effectiveness of such implementation depends on maintaining a balance between technological support and live interpersonal communication, as well as on the pedagogical skills of the teacher acting as a mediator. It is necessary to take into account the individual psychological characteristics of students and ensure clear ethical guidelines for the use of AI to avoid the risks of reduced autonomy and group cohesion. The results obtained may be useful for developing recommendations on the integration of AI into language education, taking into account the psychological aspects of the pedagogical process.