Pedagogical strategies and teaching criteria to identify AI in higher education: experimental research
DOI:
https://doi.org/10.56183/iberoeds.v6i1.717Palavras-chave:
Strategy, Pedagogy, Artificial Intelligence, Higher EducationResumo
Currently, AI is a reality in different areas of human life, and education is no exception. The benefits it offers are diverse, but situations have been identified that concern communities and educational authorities worldwide, such as irresponsible and improper use that affects academic soundness and ethics. The research aims to analyze how pedagogical strategies strengthen the criteria for identifying generative AI in higher education. To this end, it is framed in the quantitative paradigm, experimental and comparative research. The study sample corresponds to 200 teachers from higher education institutions in the province of Cotopaxi. Descriptive statistics, Student's t-test, and one-factor ANOVA were used. The research is structured into two phases: the first to identify the pedagogical strategies and the level of teacher confidence in their judgment and criteria, and the second to focus on the practice of the experiment, where teachers must identify the origin of low-, medium-, and high-level texts. The results showed that teachers have knowledge of pedagogical strategies and AI in general, but in practice, AI identification accuracy decreases as text complexity increases. An overconfidence in judgment and teaching criteria was identified.
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Copyright (c) 2026 Mayra Elizabeth Alpusig Granja, Carlos Emilio Chavez Pirca , Edwin Geovanny Chimba Lagla, Javier Ismael Cevallos Culqui, Milton Fernando Hidalgo Achig
Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
