A COGNITIVE-VERIFICATION APPROACH TO TRANSFORMING HOMEWORK IN MATHEMATICAL ANALYSIS IN THE CONTEXT OF ARTIFICIAL INTELLIGENCE USE
DOI:
https://doi.org/10.31110/fmo2026.v41i2-08Keywords:
artificial intelligence, mathematical analysis, homework, cognitive demand, mathematical tasks, verification, explanationAbstract
Formulation of the problem. The article addresses the problem of transforming homework in mathematical analysis in the context of the widespread use of artificial intelligence systems capable of automatically performing a significant portion of algorithmic mathematical operations. This creates a contradiction between the didactic purpose of homework and the possibility of its formal completion without engaging students’ cognitive activity. The aim of the study is to substantiate an approach to organizing homework based on the cognitive demand of tasks and the degree of their automation, as well as to experimentally verify its effectiveness.
Materials and methods. The study was conducted as a pedagogical experiment in a distance learning environment with second-year students of a pedagogical university during the study of the topic “Functions of Several Variables.” The sample included 8 students. Assessment was carried out using three indicators: correctness of solution, explanation, and verification of the result (maximum score – 30 points per test). Subgroups were identified based on the nature of students’ learning activity. The proposed approach involves the inclusion of a mandatory explanatory and verification component and the use of tasks of different types according to their level of automation.
Results. The results of the experiment showed that at the initial stage the students demonstrated a similar level of preparedness (48–50%). During the learning process, performance improved in both subgroups; however, a more pronounced increase was observed among students who systematically implemented the explanatory component, with final results of 77% compared to 62%. It was found that the main differences between the subgroups were related not to computational accuracy but to the level of development of explanation and verification skills.
Conclusions. It is concluded that the inclusion of an explanatory component changes the nature of students’ cognitive activity, increases the cognitive demand of tasks, and reduces the possibility of their formal completion using artificial intelligence systems. The study substantiates the feasibility of transforming homework by combining different types of tasks considering their level of automation.
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