Generative AI-supported instruction as a cognitive scaffold: effects on senior high school students’ geometric reasoning and proof construction
Résumé
Difficulties in connecting visual representations with formal deductive reasoning continue to hinder students’ success in geometry proof construction. This study examined the association between generative artificial intelligence (AI)-supported instruction and Senior High School students’ geometric reasoning and proof construction. A quasi-experimental pre-test-post-test non-equivalent control group design was employed involving 86 students from two intact classes in a public senior high school in Ghana. The experimental group received generative AI-supported instruction, while the control group received conventional instruction. Data were collected using a Geometry Reasoning and Proof Test and a perception and attitude questionnaire. Independent-samples t-tests, paired-samples t-tests, ANCOVA, and descriptive statistics were used for data analysis. The findings revealed that students exposed to generative AI-supported instruction achieved significantly higher geometric reasoning and proof construction scores than students receiving conventional instruction, even after controlling for pre-test performance. Students in the experimental group also reported favorable perceptions and attitudes toward the instructional approach. The study contributes to the growing literature on AI-supported mathematics learning by demonstrating the potential of generative AI to function as a cognitive scaffold that supports the transition from visual intuition to deductive reasoning in geometry. The findings suggest that generative AI-supported instruction may provide a useful instructional approach for enhancing students’ geometric reasoning and proof construction.
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