AI-supported neuroeducational methodologies and logical-mathematical thinking in teacher training
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Abstract
The study examined the effect of neuroeducational methodologies supported by artificial intelligence on logical-mathematical thinking in teachers in training in Basic General Education at the Technical University of Ambato, Ecuador. A quantitative approach was used with a quasi-experimental, longitudinal pre-test-posttest design with non-equivalent groups. The sample was made up of 480 students, distributed in a control group that received mathematics teaching supported by conventional ICT and an experimental group that participated in a 14-week intervention based on neuroeducational methodologies supported by artificial intelligence. Data were collected through a 21-item performance test that evaluated logical-relational reasoning, abstraction and mathematical representation, and strategic problem solving. The internal consistency of the instrument was adequate (Cronbach's alpha = 0.816; McDonald's omega = 0.817). The analysis included descriptive statistics, ANCOVA, and linear regression in Jamovi. After controlling for pre-test scores, the ANCOVA showed a significant effect of group on post-test logical-mathematical thinking F (1,477) = 206, p < 0.001, η²p = 0.302. Linear regression indicated that belonging to the experimental group significantly predicted final performance (B= 0.810, β= 0.882, t= 49.0, p < 0.001). The results suggest that neurodidactic methodologies structured and supported by artificial intelligence can strengthen logical-mathematical thinking in initial teacher training.
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