INTERACTIVE TEACHING METHODS FOR DEVELOPING ARTIFICIAL INTELLIGENCE COMPETENCIES OF PROSPECTIVE SCHOOL TEACHERS IN A DIGITAL LEARNING ENVIRONMENT

Abstract

The article develops an interactive instructional system designed to cultivate artificial intelligence (AI) competencies among prospective school teachers within a digital learning environment. The study is grounded in the prototyping stage of educational design research, pedagogical task analysis, mapping against the revised Bloom’s taxonomy, and scenario-based instructional design. As a result, didactic profiles (specifications) were formulated for eight distinct methods: problem-based AI cases, the “verify and refute” task, prompt laboratories, debating AI outputs, jigsaw verification, microteaching, project sprints, and reflective portfolios. These methods were integrated into a pedagogical cycle consisting of "problem awareness – collaborative testing – pedagogical product creation – reflective assessment". For each method, the learning objective, student activities, teacher role, AI role, assessment evidence, and safety boundaries were established. The findings demonstrate that the didactic efficacy of interactive methods depends not on the technical novelty of the AI tool itself, but on how effectively it engages the learner’s thinking, reasoning, verification, and justification of decisions.