Prompt Literacy for teachers: a progressive Pedagogical Framework for AI-Enabled Inclusive Learning
Parole chiave:
prompt engineering, teacher professional development, AI Literacy.Abstract
This study presents a structured development model aimed at transforming prompt engineering from a technical skill into a core pedagogical competency for Italian secondary school teachers. Despite the increasing integration of artificial intelligence (AI) in educational settings, significant gaps persist in teacher AI literacy, with only 34% of Italian educators feeling adequately prepared to use AI tools (GoStudent, 2025). This study addresses this critical deficit by proposing a progressive three-level "prompt literacy" framework grounded in the UNESCO AI Competency Framework for Teachers and Universal Design for Learning (UDL) 3.0 principles. The model defines three competency stages (Basic Prompt Literacy, Pedagogical Prompting, and Inclusive Prompt Design) aligned with UNESCO's Acquire-Deepen-Create progression levels. Each stage integrates the Prompt Engineering Competence Scale (PECS) for self-assessment and competency measurement. Through situated, practice-based workshops, teachers learn to design prompts that support multiple means of representation, engagement, and expression, thereby creating inclusive, adaptive, and student-centered learning environments. By bridging the AI literacy gap with pedagogically informed prompt design strategies, this framework aims to empower educators to leverage AI as a tool for equity and meaningful differentiation in contemporary classrooms.
Riferimenti bibliografici
CAST (2024). Universal Design for Learning Guidelines version 3.0 [graphic organizer]. Lynnfield, MA.
Chang, K., Xu, S., Wang, C., Luo, Y., Liu, X., Xiao, T., & Zhu, J. (2024). Efficient prompting methods for large language models: A survey. arXiv preprint arXiv:2404.01077
di Padova, M., Lotti, A. (2024). Progettazione didattica e Intelligenza Artificiale generativa per una scuola digitale e inclusiva. Roma: Tab Edizioni
Darling-Hammond, L., Hyler, M. E., & Gardner, M. (2017). Effective Teacher Professional Development. Research Brief. Learning Policy Institute. https://doi.org/10.54300/122.311
Federiakin, D., Molerov, D., Zlatkin-Troitschanskaia, O., & Maur, A. (2024). Prompt engineering as a new 21st century skill. Frontiers in Education, 9, 1366434. https://doi.org/10.3389/feduc.2024.1366434
García-Beltrán, E. (2026). No es magia, es prompting: el diseño de prompts como competencia emergente en la formación docente. Un estudio desde el modelo CRETA+R [It's Not Magic, It's Prompting: Prompt Design as an Emerging Competence in Teacher Education. A Study Based on the CRETA+R Model]. Pixel-Bit, Revista de Medios y Educación, 75, Art. 6. https://doi.org/10.12795/pixelbit.115487
Gibreel, O., & Arpaci, I. (2025). Development and validation of the prompt engineering competence scale (PECS). Information Development, 0(0). https://doi.org/10.1177/02666669251336455
Giray, L. (2023). Prompt engineering with ChatGPT: A guide for academic writers. Annals of Biomedical Engineering, 51, 2629–2633. https://doi.org/10.1007/s10439-023-03272-4.
Gopalan, M., Rosinger, K., & Ahn, J. B. (2020). Use of Quasi-Experimental Research Designs in Education Research: Growth, Promise, and Challenges. Review of Research in Education, 44, 218-243. https://doi.org/10.3102/0091732X20903302
GoStudent. (2025). Report GoStudent sul futuro dell'istruzione 2025. https://www.gostudent.org/static/documents/it-it/report-istruzione/2025.pdf
Guo, P., Wang, Z. (2025). Implementation fidelity of universal design for learning and effects on student achievement engagement and belonging. Sci Rep 15, 45430. https://doi.org/10.1038/s41598-025-30079-1
Holmes, W., Porayska-Pomsta, K., Holme, T., & Bower, M. (2019). Artificial intelligence in education: Promise and implications for teaching and learning. OECD Education Working Papers.
Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012). The Knowledge-Learning-Instruction framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science, 36(4), 757–798. https://doi.org/10.1111/j.1551-6709.2012.01245.x
Kolhatin, A. O. (2025). A human-centered framework for generative AI in adaptive educational content creation. Proceedings of the 8th International Workshop on Augmented Reality in Education (AREdu 2025), co-located with the 6th International Conference on History, Theory and Methodology of Learning (ICHTML 2025) (CEUR Workshop Proceedings Vol. 4060, pp. 143–159). CEUR-WS.org.
Ministero dell’istruzione e del merito (MIM). (2025). Linee guida per l’introduzione dell’Intelligenza Artificiale nelle Istituzioni scolastiche. https://www.mim.gov.it/documents/8099600/8784171/MIM_Linee+guida+IA+nella+Scuola_09_08_2025-signed.pdf/4bfecbc5-70e1-2973-940f-1aa818a9f3f6?version=1.0&t=1757064111486.
OECD. (2023). PISA 2025 SCIENCE FRAMEWORK. Paris: OECD Publishing.
OECD. (2023). OECD Digital Education Outlook 2023: Opportunities, guidelines and guardrails for effective and equitable use of AI in education. OECD Publishing.
Pimentel, M., Gardner, J., & Price, T. (2024). A systematic review on prompt engineering in large language models for K-12 STEM education. arXiv preprint arXiv:2410.11123.
Rose, D. H., Meyer, A., & Hitchcock, C. (2024). Universal Design for Learning: Theory and practice (3rd ed.). CAST Publishing.
Teague, H. (2025). 10-Rep Learning: Generative AI Prompts for DI and UDL. Edublogs. https://4oops.edublogs.org/2025/07/23/ai-prompts4diandudl/
UNESCO. (2024). AI Competency Framework for Teachers. Paris: UNESCO. https://doi.org/10.54675/ZJTE2084
Walter, Y. (2024) Embracing the Future of Artificial Intelligence in the Classroom: The Relevance of AI Literacy, Prompt Engineering, and Critical Thinking in Modern Education. International Journal of Educational Technology in Higher Education, (21), 15. https://doi.org/10.1186/s41239-024-00448-3
Xiao, R., Hou, X., Ye, R., Kazemitabaar, M., Diana, N., Liut, M., & Stamper, J. (2025). Improving student-AI interaction through pedagogical prompting: An example in computer science education. https://arxiv.org/abs/2506.19107.
##submission.downloads##
Pubblicato
Come citare
Fascicolo
Sezione
Licenza
Copyright (c) 2026 Francesco Facciorusso, Paola Alessia Lampugnani, Maria Concetta Carruba, Marilena di Padova, Marika Lamacchia, Anna Dipace
Questo lavoro è fornito con la licenza Creative Commons Attribuzione - Non commerciale - Non opere derivate 4.0 Internazionale.
