Verification of AI system responses as a means of developing competencies for future physics teachers in the analysis of physical-geographical processes
DOI: 10.23951/2307-6127-2026-5-57-66
Against the backdrop of digital transformation, artificial intelligence (AI) has become an integral part of modern everyday life. Of particular interest is its application within educational settings. While AI integration holds significant pedagogical potential, its unstructured implementation in teaching and learning poses serious risks. The primary concern is a decline in students’ cognitive engagement, as they tend to accept ready-made outputs without critical evaluation. This paper presents the “Verification of AI System Responses” methodology, designed for the training of prospective physics teachers. The scientific novelty of this approach lies in transforming the inherent limitations of generative AI-such as its tendency to produce incomplete or one-sided outputs-into effective didactic tools for developing professional competencies. The methodology focuses on developing students’ ability to establish robust cause-and-effect relationships between fundamental physical and geographical laws and phenomena. The approach is illustrated through an analysis of the topic “Ocean Tides”. It demonstrates how AI inaccuracies-specifically, the omission of regional factors in tidal formation-can be leveraged by educators to initiate structured, cross-disciplinary cognitive conflicts. To systematize the learning process, an original six-component task model is proposed, comprising goal-setting, technological, predictive, analytical, practical, and visualization stages. The paper details the learner’s transition cycle from passive content consumption to active knowledge synthesis: moving from identifying AI inaccuracies to creating visualizations of physical-geographical phenomena, using ocean tides as a case study. Implementing this methodology enables educators to reposition AI not merely as an information source, but as an active tool for verification and in-depth interdisciplinary analysis. Adopting this approach is expected to foster not only deeper mastery of crossdisciplinary content but also the development of critical digital thinking, alongside enhanced digital and professional literacy among educators in today’s high-tech landscape.
Keywords: generative artificial intelligence, interdisciplinary connections, AI response verification, didactic model, cognitive conflict, knowledge synthesis, physical and geographical processes, critical analysis, digital literacy, cause-and-effect relationships
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Issue: 5, 2026
Series of issue: Issue 5
Rubric: THEORY AND METHODOLOGY OF TEACHING AND UPBRINGING
Pages: 57 — 66
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