Informal STEM learning as a pathway to youth’s AI literacy

A mixed methods study

Authors

DOI:

https://doi.org/10.32674/1999hy61

Keywords:

Informal STEM Learning, AI Literacy, Camps, Mixed-Methods

Abstract

Informal STEM learning (ISL) is a blueprint for learner-centered experiences grounded in hands-on, real-world, and semistructured activities. ISL engages youth in complex disciplines (e.g., engineering and computer science) while building confidence, self-efficacy and persistence in STEM. As artificial intelligence (AI) becomes increasingly accessible, youth emerge as early adopters. Researchers and practitioners can utilize ISL to support youth AI literacy. This study implements Ng et al.’s (2023) ABCE Framework and the AI Literacy Questionnaire to analyze baseline AI literacy among 104 youth aged 12–17 from three summer camps. An interpretive phenomenological analysis of youth’s perception of AI systems was conducted through semistructured interviews. Drawing on findings and the literature, I argue that ISL is an effective approach to developing youth’s AI literacy.

Author Biography

  • Charlotte JuliAnn-Marie Avery, University of Maryland

    CHARLOTTE JULIANN-MARIE AVERY, PhD student, in the College of Information, University of Maryland - College Park, MD. Her research interests include postpandemic information-seeking behavior, human-AI interaction, AI diffusion, informal AI learning, AI literacy, and K-12 and higher education research. Email: cjavery@umd.edu

References

Akgün, S., Choi, K., & Lee, H. R. (2026). Designing a critical AI literacy program for K-8 STEM education: Adopting a community-centered approach. Disciplinary and Interdisciplinary Science Education Research, 8(1). https://doi.org/10.1186/s43031-026-00155-1

Anand, N., & Dogan, B. (2021). Impact of informal learning environments on STEM education—Views of elementary students and their parents. School Science and Mathematics, 121(7), 369–377. https://doi.org/10.1111/ssm.12490

Avery, C. (2024). Designing Create Tech: A discussion of previous camp iterations and outcomes. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education (SIGCSE 2024) (Vol. 2, pp. 1–2). Association for Computing Machinery. https://doi.org/10.1145/3626253.3635632

Barker, B. S., Larson, K., & Krehbiel, M. (2014). Bridging formal and informal learning environments. Journal of Extension, 52(5), Article 26. https://doi.org/10.34068/joe.52.05.26

Basha, J. Y. (2024). The negative impacts of AI tools on students in academic and real-life performance. International Journal of Social Sciences, 1(3), 1–16. https://doi.org/10.51470/IJSSC.2024.01.03.01

Bit, D., Biswas, S., & Nag, M. (2024). The impact of artificial intelligence in the educational system. International Journal of Scientific Research in Science and Technology, 11(4), 419–427. https://doi.org/10.32628/IJSRST2411424

Casal-Otero, L., Catala, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K–12: A systematic literature review. International Journal of STEM Education, 10(1), Article 18. https://doi.org/10.1186/s40594-023-00418-7

Chiu, T. K. F., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6, 100171. https://doi.org/10.1016/j.caeo.2024.100171

Cian, H., Dou, R., Castro, S., Palma, D. E., & Martinez, A. (2022). Facilitating marginalized youths’ identification with STEM through everyday science talk: The critical role of parental caregivers. Science Education, 106(1), 57–87. https://doi.org/10.1002/sce.21688

Coombs, P. H., & Ahmed, M. (1974). Attacking rural poverty: How nonformal education can help. International Council for Educational Development.

Creswell, J. W. (2022). A concise introduction to mixed methods research (2nd ed.). SAGE Publications.

Giansanti, D., & Cosenza, C. (2026). Artificial Intelligence and Youth: Cognitive, Educational, and Behavioral Impacts. AI, 7(4), 121. https://doi.org/10.3390/ai7040121

Greenberg, D., Kim, W. J., Brien, S., Barton, A. C., Balzer, M., & Archer, L. (2025). Designing and leading justice-centered informal STEM education: A framework for core equitable practices. Science Education, 109(1), 27–58. https://doi.org/10.1002/sce.21903

Golegou, E., Wallace, M., & Peppas, K. (2026). Student-centered pedagogies for 21st-century STEM. American Journal of STEM Education, 18, 83–124. https://doi.org/10.32674/qnh67005

Hanover Research. (2024). AI best practices in K–12 education. https://insights.hanoverresearch.com/hubfs/AI-Best-Practices-in-K12-Education.pdf

Hanover Research. (2025). AI best practices for K–12 education. https://insights.hanoverresearch.com/hubfs/AI-Best-Practices-for-K-12-Education.pdf

Hoffer, W. W. (2012). Minds on mathematics: Using Math Workshop to develop deep understanding in grades 4–8. Heinemann.

Hussim, H., Rosli, R., Mohd Nor, N. A. Z., Maat, S. M., Mahmud, M. S., Iksan, Z., Rambely, A. S., Mahmud, S. N., Halim, L., Osman, K., & Lay, A. N. (2024). A systematic literature review of informal STEM learning. European Journal of STEM Education, 9(1), Article 07. https://doi.org/10.20897/ejsteme/14609

Kramarczuk, K., Avery, C., Guzman, M. C., Shijo, N., Atchison, K., & Weintrop, D. (2023). A longitudinal study of the post-secondary experiences of women of color in computing. In 2023 Conference on Research in Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT 2023) (pp. 36–43). IEEE. https://doi.org/10.1109/RESPECT60069.2023.00018

Kramarczuk, K., Weintrop, D., Plane, J., Atchison, K., & Avery, C. (2023). CompSciConnect: A multi-year summer program to broaden participation in computing. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education (SIGCSE 2023) (pp. 1–7). Association for Computing Machinery. https://doi.org/10.1145/3545945.3569850

Landesman, R., Schwartz, L., & Davis, K. (2026). “When AI generates ideas for you, that kind of defeats the purpose”: An exploration of teens’ uses and concerns about AI. International Journal of Child-Computer Interaction, 48, Article 100815. https://doi.org/10.1016/j.ijcci.2026.100815

Lazar, J., Feng, J. H., & Hochheiser, H. (2017). Research methods in human–computer interaction (2nd ed.). Morgan Kaufmann.

Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ’20) (pp. 1–16). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727

Long, D., Blunt, T., & Magerko, B. (2021). Co-designing AI literacy exhibits for informal learning spaces. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), Article 293. https://doi.org/10.1145/3476034

Maheux, A. J., Akre-Bhide, S., Boeldt, D., Flannery, J. E., Richardson, Z., Burnell, K., Telzer, E. H., & Kollins, S. H. (2026). Generative artificial intelligence applications use among US youth. JAMA Network Open, 9(2), Article 2556631. https://doi.org/10.1001/jamanetworkopen.2025.56631

McClain, C., Anderson, M., Sidoti, O., & Bishop, W. (2026, March 10). How teens use and view AI. Pew Research Center. https://www.pewresearch.org/internet/2026/02/24/how-teens-use-and-view-ai/

Mohr-Schroeder, M. J., Jackson, C., Miller, M., Walcott, B., Little, D. L., Speler, L., Schooler, W., & Schroeder, D. C. (2014). Developing middle school students’ interests in STEM via summer learning experiences: See Blue STEM Camp. School Science and Mathematics, 114(6), 291–301. https://doi.org/10.1111/ssm.12079

Morris, B. J., Zentall, S. R., Murray, G., & Owens, W. (2021). Enhancing informal STEM learning through family engagement in cooking. Proceedings of the Singapore National Academy of Science, 15(2), 119–133. https://doi.org/10.1142/S2591722621400111

Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041

Ng, D. T. K., Leung, J. K. L., Su, M. J., Yim, I. H. Y., Qiao, M. S., & Chu, S. K. W. (2022). AI literacy in K–16 classrooms. Springer. https://doi.org/10.1007/978-3-031-18880-0

Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2023). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 55(3), 1082–1104. https://doi.org/10.1111/bjet.13411

Pinkard, N., Erete, S., Martin, C. K., & McKinney de Royston, M. (2017). Digital Youth Divas: Exploring narrative-driven curriculum to spark middle school girls’ interest in computational activities. Journal of the Learning Sciences, 26(3), 477–516. https://doi.org/10.1080/10508406.2017.1307199

Scott, K. A. (2014). Designing a culturally responsive computing curriculum for girls. International Journal of Gender, Science and Technology, 6(2), 264–276.

Sheridan, K. M., Halverson, E. R., Litts, B. K., Brahms, L., Jacobs-Priebe, L., & Owens, T. (2014). Learning in the making: A comparative case study of three makerspaces. Harvard Educational Review, 84(4), 505–531. https://doi.org/10.17763/haer.84.4.brr34733723j648u

Sidoti, O., Park, E., & Gottfried, J. (2025, January 15). About a quarter of U.S. teens have used ChatGPT for schoolwork—Double the share in 2023. Pew Research Center. https://pewrsr.ch/4g3Jqt0

Sidoti, O., Park, E., & Gottfried, J. (2025, January 31). Share of teens using ChatGPT for schoolwork doubled from 2023 to 2024. Pew Research Center. https://www.pewresearch.org/short-reads/2025/01/15/about-a-quarter-of-us-teens-have-used-chatgpt-for-schoolwork-double-the-share-in-2023/

Smith, J. A., Jarman, M., & Osborn, M. (1999). Doing interpretative phenomenological analysis. In M. Murray & K. Chamberlain (Eds.), Qualitative health psychology: Theories and methods (pp. 218–240). SAGE Publications.

Solyst, J., Xie, S., Yang, E., Stewart, A. E. B., Eslami, M., Hammer, J., & Ogan, A. (2023). “I would like to design”: Black girls analyzing and ideating fair and accountable AI. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23) (pp. 1–14). Association for Computing Machinery. https://doi.org/10.1145/3544548.3581378

Song, Y., Weisberg, L. R., Zhang, S., Tian, X., Boyer, K. E., & Israel, M. (2024). A framework for inclusive AI learning design for diverse learners. Computers and Education: Artificial Intelligence, 6, 100212. https://doi.org/10.1016/j.caeai.2024.100212

Sungur Gül, K., Saylan Kırmızıgül, A. S., Ateş, H., & Garzón, J. (2023). Advantages and challenges of STEM education in K–12: A systematic review and research synthesis. International Journal of Research in Education and Science, 9(2), 283–307. https://doi.org/10.46328/ijres.3127

Touretzky, D. S., & Gardner-McCune, C. (2022). Artificial intelligence thinking in K–12. In S.-C. Kong & H. Abelson (Eds.), Computational thinking education in K–12: Artificial intelligence literacy and physical computing (Chap. 8). MIT Press.

Touretzky, D. S., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K–12: What should every child know about AI? Communications of the ACM, 62(4), 68–71. https://doi.org/10.1145/3300115

Veldhuis, A., Lo, P. Y., Kenny, S., & Antle, A. N. (2024). Critical artificial intelligence literacy: A scoping review and framework synthesis. International Journal of Child-Computer Interaction, 43, 100708. https://doi.org/10.1016/j.ijcci.2024.100708

Additional Files

Published

2026-07-05

Issue

Section

STEM Education (regular)

How to Cite

Avery, C. J.-M. (2026). Informal STEM learning as a pathway to youth’s AI literacy: A mixed methods study. American Journal of STEM Education, 25, 66-95. https://doi.org/10.32674/1999hy61