FROM READY-MADE ANSWERS TO GUIDED THINKING: THE METHOD OF COGNITIVE-TRAJECTORY TRACING IN THE USE OF ARTIFICIAL INTELLIGENCE

Abstract

Purpose. The purpose of the article is to theoretically justify and experimentally verify a didactic modelof pedagogically guided use of artificial intelligence in developing mathematical competencies of futureprimary school teachers, as well as to clarify the essence of the cognitive-trajectory tracing method as a tool foranalysing students’ learning activity. Methods. The study employed analysis of scholarly and policy sources,pedagogical observation, questionnaires, analysis of student–AI dialogues, and a quasi-experiment withdiagnostic, formative, and control stages. Statistical processing of the results was carried out using Student’st-test. The participants were 33 second-year students majoring in Primary Education from two consecutiveacademic cohorts. Results. The article proposes a didactic model of AI use in which the role of artificialintelligence shifts from a tool for checking answers and varying task conditions to an object of critical analysisand a means of co-creation. The cognitive-trajectory tracing method is substantiated as a way to record notonly the final answer but also the sequence of students’ cognitive actions in interaction with AI, includingprompt formulation, clarification of conditions, choice of strategy, verification, explanation, and reflection. Theexperimental results demonstrated positive dynamics in the levels of mathematical competence, an increase in analytical and reflective actions, and signs of transfer from students’ own learning strategies to their emergingmethodological competence as future teachers. Conclusions. Pedagogically guided AI use can serve asan effective means of developing mathematical and methodological competencies in future primary schoolteachers, provided that learning is based on clear didactic design, human oversight, and a shift of emphasisfrom answer production to the process of thinking.