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Predicting the level of artificial intelligence in engineering students using machine learning techniques
0
Zitationen
5
Autoren
2025
Jahr
Abstract
The objective of this work is to predict the level of artificial intelligence literacy in engineering students at the Technical University of Manabí using machine learning techniques. The research methodology includes training a neural network and support vector machines to predict the level of artificial intelligence literacy in university engineering students. The novelty of this work lies in the possibility of defining in advance the needs of engineering students regarding AI literacy, which will allow for early and personalized interventions in the learning process. Historical data on artificial intelligence literacy levels are compared with those predicted by neural networks and support vector machines. The selection of these models was carried out by cross-validation (k = 5) on 75% of the data, using the mean RMSE as the primary criterion and Spearman/MAE as complementary metrics.
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