Preparing future teachers for the use of artificial intelligence in education

Abstract

The article addresses the issue of preparing future teachers for productive and ethically responsible use of artificial intelligence in education amid accelerating digital transformation, where technological capabilities outpace teachers' methodological readiness and necessitate redefining the teacher's professional role as a designer of the educational environment and a critical moderator of knowledge. It presents an interpretation of readiness to apply AI as a multicomponent structure including motivational-value, cognitive, and operational-activity components, and demonstrates that high student interest in AI does not automatically translate into understanding the principles of operation of generative models, limitations of their applicability, and risks of uncritical trust in algorithmic output. Based on data obtained from four universities involving 1458 respondents and supplemented by analysis of over 300 methodological developments, persistent deficits are identified: the share of low cognitive readiness level reaches 48,28%, and operational-activity readiness – 51,86%, against relatively high motivational indicators (high level – 37,18%). The profile-specific features of self-assessment of competence are detailed: humanities directions demonstrate strong positions in generating textual materials, while physics-mathematics directions – in data analysis; however, all profiles are characterized by minimal indicators in learning personalization and ethical expertise, indicating weak mastery of the most pedagogically significant AI functions. The effectiveness of implementing a modular program oriented toward prompt engineering, AI didactics, and data ethics is shown: methodological barriers are substantially reduced (misunderstanding of pedagogical expediency decreased by 43.37 p.p.; deficit of assessment methods – by 30.58 p.p.), as well as psychological fear of losing authority, while preserving inertial constraints of a normative and technical nature and partial persistence of value concerns about dehumanization. The article's material allows evaluating the relevance of the proposed systemic and differentiated approach to updating pedagogical education, emphasizing the transition from superficial mastery of tools to forming critical AI literacy, methodological design, and a culture of verification that ensure the humanistic nature of learning in a technologically saturated environment.