‘A good question is half the answer:’ a learning-oriented K-12 prompt literacy competency cultivation framework
DOI:
https://doi.org/10.47989/ir31iConf64280Keywords:
Prompt literacy, K-12, Cultivation frameworkAbstract
Introduction. As an emerging key competence, prompt literacy is gradually becoming an essential component of artificial intelligence literacy and is increasingly emphasised in K–12 education. This study aims to construct a learning-oriented K-12 prompt literacy competency cultivation framework.
Method. The framework was theoretically informed by Bloom’s taxonomy, Zimmerman’s self-regulated learning theory, and existing AI literacy frameworks for K–12 students. The framework was then refined through the deductive content analysis based on the semi-structured interview data that were conducted with teachers and students.
Results. The framework comprises three dimensions. (1) Four core competencies: prompt cognition, prompt skills, prompt thinking, and prompt values; (2) three learning objectives: understand, apply, and evaluate; (3) four cultivation stages: observation, emulation, self-control, and self-regulation. The indicators of competencies, specific descriptions of objectives, and corresponding cultivation approaches were also proposed. Ultimately, a stage-based, progressive, and highly actionable guideline for cultivating prompt literacy was developed, which clearly specifies the competencies to be nurtured and the learning objectives to be achieved at each stage.
Conclusion(s). This study contributes to AI literacy education by establishing the first comprehensive framework for prompt literacy cultivation in K–12 students.
References
AAAI & CSTA (2019). The Five Big Ideas in artificial intelligence poster is available in multiple languages. https://ai4k12.org/resources/big-ideas-poster.
AI Singapore (2025). AI Student Outreach Programme. https://learn.aisingapore.org/student-outreach-programme/.
Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives: complete edition. Addison Wesley Longman, Inc.
Bozkurt, A., Junhong, X., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., ... & Romero-Hall, E. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53-130.
Chang, D. H., Lin, M. P. C., Hajian, S., & Wang, Q. Q. (2023). Educational design principles of using AI chatbot that supports self-regulated learning in education: Goal setting, feedback, and personalisation. Sustainability, 15(17). https://www.mdpi.com/2071-1050/15/17/12921.
China National Center for Educational Technology (2021). Framework for artificial intelligence technology and engineering literacy in primary and secondary schools. https://www.ncet.edu.cn/u/cms/www/202304/201559072pfc.pdf.
Department of Education of Guangdong Province (2025). Interpretation of the ‘211’ implementation framework for artificial intelligence education in primary and secondary schools in Guangdong. https://edu.gd.gov.cn/zwgknew/zcjd/content/post_4694726.html.
Ericsson, K. A., & Lehmann, A. C. (1996). Expert and exceptional performance: Evidence of maximal adaptation to task constraints. Annual review of psychology, 47(1), 273-305.
Huang, J. (2024). Cultivating prompt literacy in university libraries in the digital intelligence era: content, methods and paths. New Century Library, (12): 47-52.
Hwang, Y., Lee, J. H., & Shin, D. (2023). What is prompt literacy? An exploratory study of language learners' development of new literacy skill using generative AI. arXiv preprint arXiv:2311.05373. https://doi.org/10.48550/arXiv.2311.05373.
Kim, J., Yu, S., Lee, S. S., & Detrick, R. (2025). Students’ prompt patterns and its effects in AI-assisted academic writing: Focusing on students’ level of AI literacy. Journal of Research on Technology in Education, 1-18.
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. https://doi.org/10.1016/j.caeai.2024.100225.
Learning, H. A. I. (2025). Synergising Self-Regulation and Artificial-Intelligence Literacy Towards Future. arXiv preprint arXiv: 2504.07125. https://arxiv.org/abs/2504.07125.
Li, S. N., Xiao, Y. J., & Tong, R. (2025). Prompt literacy: A new extension of academic library information literacy in the age of digital intelligence. LIBRARY TRIBUNE, 1-10. https://link.cnki.net/urlid/44.1306.G2.20250428.1057.002.
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualising AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2. https://doi.org/10.1016/j.caeai.2021.100041.
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in psychology, 8. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2017.00422.
Rowland, D. R. (2023). Two frameworks to guide discussions around levels of acceptable use of generative AI in student academic research and writing. Journal of Academic Language and Learning, 17(1), T31-T69.
The White House (2025). Advancing artificial intelligence education for American youth. https://www.whitehouse.gov/presidential-actions/2025/04/advancing-artificial-intelligence-education-for-american-youth/.
UNESCO (2024). AI competency framework for students. https://unesdoc.unesco.org/ark:/48223/pf0000391105.
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(1), 15-44.
Wilson, L. O. (2016). Anderson and Krathwohl Bloom’s taxonomy revised understanding the new version of Bloom’s taxonomy. The Second Principle, 1(1), 1-8.
Xinhua News Agency (2025). Ministry of Education releases two guidelines to promote the development of artificial intelligence education in primary and secondary schools. http://www.news.cn/politics/20250512/69b0d3a212f74f9ab02b3a9a095d3872/c.html.
Zhang, G. X., & Jia, J. Z. (2024). Research on prompt literacy cultivation in the era of generative AI. Journal of Academic Libraries, 42(6): 63-71.
Zhao, Y. X., Jing, Y. T., & Song, S. J. (2025). Prompt literacy empowered by AIGC: Restructuring Human-AI interaction capabilities in the generative AI era. Information and Documentation Services, 46(3): 14-25.
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In Handbook of self-regulation (pp. 13-39). Academic Press.
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