Bridging the AI use–competence gap

Implications for AI literacy in STEM graduate education

Authors

DOI:

https://doi.org/10.32674/5xhcsr85

Keywords:

AI literacy, digital literacy, generative artificial intelligence, graduate education, Higher education

Abstract

Generative artificial intelligence (AI) is transforming graduate research and academic work; however, increased use does not necessarily lead to critical evaluation or responsible engagement. This study examines the gap between graduate students’ AI use and their evaluative, ethical, and rhetorical engagement with AI-generated outputs. Using a survey-based exploratory case study design, the study draws on data from 50 graduate students across four national contexts, complemented by open-ended responses and contextual observations. Findings reveal high levels of AI use alongside limited verification practices, ethical uncertainty, and a tendency to view AI outputs as neutral or authoritative, indicating a persistent use–competence gap. The study proposes a holistic AI literacy framework with implications for curriculum, assessment, and policy.

Author Biography

  • Marina Falasca, I.E.S. en Lenguas Vivas “Juan Ramón Fernández”

    MARINA FALASCA, M.Ed., M.A., is a Professor, Researcher, and PhD candidate in International Education from Argentina. Her work focuses on global citizenship education, the internationalization of higher education, and virtual exchange. Her current research examines the pedagogical integration of generative artificial intelligence in higher education, with particular emphasis on AI literacy, ethical and responsible use, and the evolving role of AI in shaping teaching, learning, and academic practices across virtual exchange and international learning contexts. She is the recipient of the Klett Award 2024, the Global Education Prize AAICEP 2025, and the Educa Latinoamérica 2026 Award for Innovation in Higher Education. Email: marina.falasca@bue.edu.ar

References

Bauer, N. F. (2025). Does ChatGPT increase language homogenization? In C. Vaih-Baur, V. Mathauer, E.-I. von Gamm, & D. Pietzcker (Eds.), KI in Medien, Kommunikation und Marketing: Wirtschaftliche, gesellschaftliche und rechtliche Perspektiven (pp. 11–31). Springer.

Deng, X. (N.), & Sun, R. (2026). Exploring ethical concerns and affordances of generative AI in higher education: Perspectives of underserved students. Research and Practice in Technology Enhanced Learning, 22, 011. https://doi.org/10.58459/rptel.2027.22011

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Raman, R., & Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, Article 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Falasca, M. (2025). From passive consumption to critical use: A holistic AI literacy model for high school EFL students. TESOL-SPAIN Journal, 2. https://www.tesol-spain.org/en/news/9/129/volume-2-autumn-2025---making-meaningful-connections.html

Francis, N. J., Jones, S., & Smith, D. P. (2025). Generative AI in higher education: Balancing innovation and integrity. British Journal of Biomedical Science, 81, 14048. https://doi.org/10.3389/bjbs.2024.14048

Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), Article 6. https://doi.org/10.3390/soc15010006

Gottschling, M. (2025). Towards rhetorical AI literacy: Presenting a conceptual framework. Argumentation et Analyse du Discours, 35. https://doi.org/10.4000/14yaw

Hershkovitz, A., Tabach, M., Reich, Y., Lurie, L., & Cholcman, T. (2025). Framing and evaluating task-centered generative artificial intelligence literacy for higher education students. Systems, 13(7), Article 518. https://doi.org/10.3390/systems13070518

Karapetyan, T. (2025). Algorithmic discrimination in public policy: A case study of the Dutch childcare benefits scandal (Master’s thesis, Charles University).

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Nature Human Behaviour, 7(10), 1838–1846. https://doi.org/10.1038/s41562-023-01647-6

León, C., Lipuma, J., & Oviedo-Torres, X. (2025). Artificial intelligence in STEM education: A transdisciplinary framework for engagement and innovation. Frontiers in Education, 10, Article 1619888. https://doi.org/10.3389/feduc.2025.1619888

Lintner, T. (2024). A systematic review of AI literacy scales. npj Science of Learning, 9, Article 50. https://doi.org/10.1038/s41539-024-00264-4

Livingstone, S. (2004). Media literacy and the challenge of new information and communication technologies. The Communication Review, 7(1), 3–14. https://doi.org/10.1080/10714420490280152

Ilgun Dibek, M., Sahin Kursad, M., & Erdogan, T. (2025). Influence of artificial intelligence tools on higher order thinking skills: a meta-analysis. Interactive Learning Environments, 33(3), 2216–2238.

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 (pp. 1–16). ACM. https://doi.org/10.1145/3313831.3376727

Lund, B., Mannuru, N. R., Teel, Z. A., Lee, T. H., Ortega, N. J., Simmons, S., & Ward, E. (2025). Student perceptions of AI-assisted writing and academic integrity: Ethical concerns, academic misconduct, and use of generative AI in higher education. AI in Education, 1(1), 2. https://doi.org/10.3390/aieduc1010002

Manuel, C. C. J. (2025). The impact of AI on inclusivity in higher education: A rapid review. Education Sciences, 15(9), Article 1255.

Reicho, M., Otrel-Cass, K., Ebner, M., Brünner, B., Freinhofer, D., & Irfan, J. (2026). Generative AI literacy across education and business: Competencies, obstacles, and benefits—A systematic literature review. International Journal of Educational Technology in Higher Education, 23(1). https://doi.org/10.1186/s41239-026-00596-8

UNESCO. (2023). Guidance for generative AI in education and research. UNESCO Publishing. https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research

UNESCO. (2024). AI competency framework for students. UNESCO. https://doi.org/10.54675/JKJB9835

Xu, B., Wu, L., Yuan, Y., Xian, L., & Wang, W. (2025). Exploring the application of AI chatbot tools in higher education: Evidence from the Duke University student survey. Safety Emergency Science, 1(2), 9590013.

Zhou, X., & Schofield, L. (2024). Developing a conceptual framework for artificial intelligence (AI) literacy in higher education. Journal of Learning Development in Higher Education, 31. https://doi.org/10.47408/jldhe.vi31.1354

Additional Files

Published

2026-10-09

Issue

Vol. 23 (2026): American Journal of STEM Education

Section

STEM Education (regular)

License

Copyright (c) 2026 Marina Falasca

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c) [year] [author]
This work is licensed under a Creative Commons Attribution 4.0 International License.
This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. https://creativecommons.org/licenses/by/4.0

How to Cite

Falasca, M. (2026). Bridging the AI use–competence gap: Implications for AI literacy in STEM graduate education. American Journal of STEM Education, 23, 1-25. https://doi.org/10.32674/5xhcsr85