Métricas alternativas
Palabras clave
artifical intelligence
computer assisted instruction
critical thinking
vocational training educación de profesores
inteligencia artificial
enseñanza asistida por ordenador
pensamiento crítico
formación profesional
Cómo citar
Resumen
La irrupción de la inteligencia artificial generativa en la educación plantea desafíos y oportunidades sin precedentes para la formación inicial docente. En este contexto, el diseño de prompts emerge como una competencia clave que articula saberes pedagógicos, lingüísticos, digitales y éticos. Este estudio analiza el desempeño de 481 estudiantes del Máster de Profesorado de Secundaria en una actividad centrada en la elaboración de prompts educativos, guiados por el modelo didáctico CRETA+R (Contexto, Rol, Ejemplos, Tarea, Ajustar, Refinar). Se aplicó una metodología mixta que combinó análisis cuantitativo (estadísticas descriptivas, correlaciones de Spearman y visualización de datos) con análisis cualitativo de ejemplos representativos. La evaluación se realizó mediante una rúbrica analítica aplicada por el profesorado, y los datos fueron procesados con el software JASP 0.19.3. Los resultados indican un buen dominio en componentes estructurales como “Contexto” y “Tarea”, y mayores dificultades en los aspectos metacognitivos, como “Ajustar” y “Refinar”. Aunque no se hallaron diferencias significativas entre especialidades, el análisis visual y cualitativo muestra patrones diferenciados por área. El modelo CRETA+R se consolida como un andamiaje eficaz para guiar el desarrollo progresivo de esta competencia emergente en contextos de formación docente.
Citas
Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence: Understanding the impact of ChatGPT on teaching and learning. Education and Information Technologies, 29, 739–758. https://doi.org/10.1007/s10639-023-11948-4
Bearman, M., Ryan, J., & Ajjawi, R. (2023). Discourses of artificial intelligence in higher education: A critical literature review. Higher Education, 86, 369–385. https://doi.org/10.1007/s10734-022-00937-2
Bond, M. (2024). AI applications in Initial Teacher Education: A systematic mapping review. Computers and Education: Artificial Intelligence, 6, 100228. https://doi.org/10.1016/j.caeai.2024.100228
Bozkurt, A., & Sharma, R. C. (2023). Generative AI and prompt engineering: The art of whispering to let the genie out of the algorithmic world. Asian Journal of Distance Education, 18(2), i–vii. https://tinyurl.com/mr2csd5u
Comisión Europea (2022). Directrices éticas sobre el uso de la inteligencia artificial (IA) y los datos en la educación y formación para los educadores. Oficina de Publicaciones de la Unión Europea. https://tinyurl.com/yckdz2vk
Debnath, S., Dai, T., Smith, G., & Sridhar, S. (2025). Prompt engineering in education: A framework and research agenda. Computers and Education: Artificial Intelligence, 7, 100289. https://doi.org/10.1016/j.caeai.2025.100289
Eager, B., & Brunton, R. (2023). Prompting Higher Education Towards AI-Augmented Teaching and Learning Practice. Journal of University Teaching & Learning Practice, 20(5). https://doi.org/10.53761/1.20.5.02
Federiakin, M., Azaria, A., & Hershkovitz, A. (2024). Prompt engineering skills and strategies: Toward a framework for assessing student interaction with generative AI. Computers and Education: Artificial Intelligence, 6, 100267. https://doi.org/10.1016/j.caeai.2024.100267
González-Calatayud, V., Prendes-Espinosa, P., & Roig-Vila, R. (2021). Artificial Intelligence for Student Assessment: A Systematic Review. Applied Sciences, 11(12), 5467. https://doi.org/10.3390/APP11125467
Graux, A., Brassier, C., & Guillemet, M. (2024). Prompt engineering as a learning activity in higher education: A case study of a design workshop. Computers and Education: Artificial Intelligence, 6, 100258. https://doi.org/10.1016/j.caeai.2024.100258
Higginbotham, R., & Matthews, J. (2024). Improving Prompt Iteration with AI: Lessons from an Educational Pilot Study. In Proceedings of the 24th International Conference on Artificial Intelligence in Education (AIED 2024).
Knoth, N., Tolzin, A., Janson, A., & Leimeister, J. M. (2024). AI literacy and its implications for prompt engineering strategies. Computers and Education: Artificial Intelligence, 6, 100225. https://doi.org/10.1016/j.caeai.2024.100225
Korzyński, P., Mazurek, G., Krzypkowska, P., & Kurasiński, A. (2023). Artificial intelligence prompt engineering as a new digital competence: Analysis of generative AI technologies such as ChatGPT. Entrepreneurial Business and Economics Review, 11(3), 25–37. https://doi.org/10.15678/EBER.2023.110302
Lo, L. (2023). The Art and Science of Prompt Engineering: A New Literacy in the Information Age. Internet Reference Services Quarterly, 27, 203–210. https://doi.org/10.1080/10875301.2023.2227621
Luckin, R., Rudolph, J., Grünert, M., & Tan, S. (2024). Exploring the future of learning and the relationship between human intelligence and AI. An interview with Professor Rose Luckin. Journal of Applied Learning and Teaching, 7(1), 346–363. https://doi.org/10.37074/jalt.2024.7.1.27
Moldavan, C., & Nafziger, R. (2024). Scaffolding a Critical Lens of Generative AI for Lesson Planning. Contemporary Issues in Technology and Teacher Education, 24(1), 45–62. https://tinyurl.com/mvaeu929
Oppenlaender, J., Linder, R., & Silvennoinen, J. (2024). Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering. International Journal of Human–Computer Interaction, 1-23. https://doi.org/10.1080/10447318.2024.2431761
Ranade, D., Gillespie, T., & Lin, T. (2024). Rhetorical prompting: A framework for prompt design in educational contexts. Learning, Media and Technology, 49(1), 12–34. https://doi.org/10.1080/17439884.2024.2278910
Reiser, B. J. (2004). Scaffolding complex learning: The mechanisms of structuring and problematizing student work. Journal of the Learning Sciences, 13(3), 273–304. https://doi.org/10.1207/s15327809jls1303_2
Reglamento (UE) 2024/1689 del Parlamento Europeo y del Consejo, de 13 de junio de 2024, por el que se establecen normas armonizadas en materia de inteligencia artificial. Diario Oficial de la Unión Europea, L 144, 12 de julio de 2024. https://tinyurl.com/9kn8ww8c
Roscoe, R. D. (2023). Building inclusive and equitable artificial intelligence for education. XRDS: Crossroads, The ACM Magazine for Students, 29(3), 22–25. https://doi.org/10.1145/3589637
Rosenshine, B. (2012). Principles of Instruction: Research-Based Strategies That All Teachers Should Know. American Educator, 36(1), 12–19. https://eric.ed.gov/?id=EJ971753
Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., & Demir, I. (2024). Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. Information, 15(10), 596. https://doi.org/10.3390/info15100596
Silva, B. (2024). Generative Artificial Intelligence in chemistry teaching: ChatGPT, Gemini, and Copilot’s content responses. Journal of Applied Learning and Teaching, 7(2). https://doi.org/10.37074/jalt.2024.7.2.13
Theophilou, E., Koyutürka, C., Yavari, M., Bursic, S., Donabauer, G., Telari, A., Testa, A., Boiano, R., Hernández-Leo, D., Ruskov, M., Taibi, D., Gabbiadini, A., & Ognibene, D. (2023). Learning to Prompt in the Classroom to Understand AI Limits: A Pilot Study. In Proceedings of the 22nd International Conference of the Italian Association for Artificial Intelligence (AIXIA 2023), Rome, Italy. https://doi.org/10.48550/arXiv.2307.01540
Zamfirescu-Pereira, J., Wong, R., Hartmann, B., & Yang, Q. (2023). Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3544548.3581388
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Derechos de autor 2025 Pixel-Bit. Revista de Medios y Educación