Contribution of Artificial Intelligence to Secondary Education Performance
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Contribution of Artificial Intelligence to Secondary Education Performance
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This study is based on a descriptive, non-experimental, mixed research (Vizcaino et al., 2023), based on the triangulation of agents and techniques to increase the credibility of the data (Flick, 2014), as it aims to assess the perception of both students and teachers on the use of AI as a conditioning factor of students' academic performance, since it is essential to know the opinion of the agents involved (Qu and Dumay, 2011). For the quantitative dimension, a non-experimental design using ex post facto surveys was used (Kerlinger et al., 2002; Simon and Goes, 2013). And for the qualitative dimension, a semi-structured interview was chosen (Mayorga, 2014). Quantitative data were analysed using the statistical package SPSS version 28.0, performing a descriptive analysis followed by an inferential analysis, with a significance level of sig. <0.05, which implies a confidence level of 95% and, therefore, an error of 5%. Reliability was calculated using Cronbach's α, obtaining a value of α= .80. The qualitative data were analysed using a system of emerging categories, identifying 4 categories: AI tools, Motivation, Learning building and Information contrast.