The impact of artificial intelligence technologies on the development of emotional intelligence among students in secondary vocational education

The study aims to substantiate the feasibility of using artificial intelligence (AI) technologies as a means of developing emotional intelligence (EI) among students in secondary vocational education institutions. The article clarifies the interpretation of EI adopted in the study – D. V. Lyusin’s ability model, distinguishing between interpersonal and intrapersonal components, identifies the specific features of EI development among secondary vocational students and the limitations of traditional pedagogical technologies, and substantiates the psychological and pedagogical foundations for using AI technologies for this purpose. The paper presents the authors’ formative module, “Emotional Intelligence in a Digital Environment”, which integrates four AI tools: an emotional-reflection chatbot, a communication-situation simulator, a text sentiment analyzer, and case analysis with AI-generated feedback. The module was experimentally tested during the 2025-2026 academic year on a sample of 62 second-year students majoring in 09.02.07 “Information Systems and Programming” (experimental group: 31 students; control group: 31 students). EI was assessed using D. V. Lyusin’s EmIn questionnaire. The scientific novelty lies in substantiating a pedagogical mechanism through which AI technologies mediate EI development among secondary vocational students. Within this mechanism, the AI agent scaffolds emotional reflection within the learner’s zone of proximal development, while a set of AI tools aligned with the phases of experiential learning and levels of reflection ensures a complete cycle of emotional-experience transformation. The study also identifies a selective relationship between the type of AI tool and specific EI components: reflective tools primarily develop the understanding of one’s own emotions, whereas simulation tools support the understanding and regulation of other people’s emotions. The findings demonstrate that incorporating AI tools into a theoretically grounded formative module promotes students’ ability to recognize and verbally label their own emotional states, understand the emotions of professional communication partners, and select strategies for influencing their emotional states. The least responsive to AI-mediated intervention was the control of outward emotional manifestations associated with the nonverbal channel of communication. These changes were confirmed by statistically significant gains on all EmIn scales in the experimental group (p ≤ 0.001; d = 1.05-1.28), while no significant changes were observed in the control group. The students’ positive evaluation of the module was consistent with the objective dynamics of their EI.