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    No es magia, es prompting: el diseño de prompts como competencia emergente en la formación docente. Un estudio desde el modelo CRETA+R
    (Editorial Universidad de Sevilla, 2026-01-01)
    García-Beltrán, Eva
    The emergence of generative artificial intelligence in education poses unprecedented challenges and opportunities for initial teacher education. In this context, prompt design is becoming a key competence that integrates pedagogical, linguistic, digital, and ethical knowledge. This study analyzes the performance of 481 students from the Master's Degree in Secondary Education Teaching in a task focused on creating educational prompts, guided by the instructional model CRETA+R (Context, Role, Examples, Task, Adjust, Refine). A mixed-methods approach was applied, combining quantitative analysis (descriptive statistics, Spearman correlations, and data visualizations) with a qualitative review of representative examples. The prompts were evaluated using an analytical rubric applied by instructors, and the data were processed with JASP software version 0.19.3. The results indicate stronger performance in structural components such as “Context” and “Task,” while more metacognitive aspects like “Adjust” and “Refine” proved more challenging. Although no statistically significant differences were found across specializations, visual and qualitative analyses revealed discipline-specific patterns. The CRETA+R model is validated as an effective scaffold to support the progressive development of this emerging competence in teacher education.
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    Dissertation on the adequacy of the CRETA-R methodology as a future teaching resource and analysis of the relevance of the evaluation conducted by ChatGPT and professors: Can ChatGPT facilitate teaching?
    (Informa UK Limited, 2025)
    Garcia-Beltran, Eva
    ;
    Vilchez Tornero, Jose Luis
    This study explores the adequacy of the CRETA-R framework as a teaching resource and the reliability of AI-based assessment using ChatGPT. A mixed-methods design was applied with 427 university students completing a structured task evaluated by both professors and ChatGPT. Quantitative analysis employed Kendall’s tau-b and the Wilcoxon test to compare scores, while qualitative clustering examined student performance across CRETA-R dimensions. Results showed no significant differences between human and AI evaluations, with high alignment in scoring criteria. However, students struggled with prompt refinement and example integration. The strongest predictors of high performance were the Role, Adjustment, and Refinement components. The findings support the use of AI as a viable tool for structured academic assessment, though outcomes are highly dependent on prompt quality. The study highlights the importance of AI literacy and structured prompt design to ensure consistent and pedagogically meaningful evaluation. © 2025 Elsevier B.V., All rights reserved.