2026-06-05T02:30:44Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/467802026-06-04T10:00:15Zcom_10630_2254col_10630_20092

Cognitive Load Theory and Learner Competencies as Predictors of Effective GenAI Training for Preservice Teachers. Guillén Gámez, Francisco David Habibi, Akhmad Mascia, Maria Lidia Pinto-Llorente, Ana María Profesores - Formación profesional Inteligencia artificial en la enseñanza Enseñanza - Innovaciones Resolución de problemas This dataset contains responses collected from 450 preservice teachers through a structured questionnaire developed to investigate the factors influencing the perceived usefulness of Generative Artificial Intelligence (GenAI) tools in instructional material design. Specifically, the dataset includes measures of metacognitive skills, prior knowledge, problem-solving skills, intrinsic cognitive load, extraneous cognitive load, germane cognitive load, and perceived usefulness, together with sociodemographic information. All questionnaire items were measured using 7-point Likert-type scales. Each row represents one participant, and each column represents one variable. 2026-06-02T11:48:03Z 2026 dataset https://hdl.handle.net/10630/46780 10.24310/riuma.46780 eng http://creativecommons.org/licenses/by-nc/4.0/ open access Attribution-NonCommercial 4.0 International Universidad de Malaga