International Journal of Human Research and Social Science Studies

The integration of artificial intelligence (AI) content into school science subjects remains limited, particularly in developing countries. This study aimed to develop an interactive physics simulation system supporting the teaching of solid deformation in Grade 10 and examine changes in students’ AI competency after a learning process with the system. A single-group pretest-posttest design was employed over six weeks with 89 Grade 10 students at Sa Pa No. 1 High School, Vietnam, using parallel-form tests, a Likert self-assessment scale, and observation checklists, with data analyzed via paired-samples t-tests. The results demonstrated statistically significant gains in all three AI competency components-understanding AI data, applying algorithms, and evaluating models (p < 0.001)-indicating the feasibility of embedding AI learning within mechanics instruction. Future research should incorporate comparison groups, retention measures, and larger samples across various school contexts to validate these findings.

AI competency, interactive simulation, physics education, machine learning, secondary students

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