Generative Artificial Intelligence Literacy: From Instrumental Use to Self-Regulation in an Intensive Calculus Course
DOI:
https://doi.org/10.70577/2rvtzz63Abstract
The integration of generative artificial intelligence (GenAI) into higher education poses a dilemma: it can enhance learning or become a shortcut that simulates competence without building it. This study aimed to analyze the extent to which a structured GenAI literacy process facilitates the transition from an instrumental use of these tools (as answer generators) toward a reflective and self-regulated use (as a tutor that strengthens autonomy), within an intensive summer Differential and Integral Calculus course. A mixed-methods design was used, combining a comparison of student performance before and after the intervention (assessments completed with and without AI assistance, n = 24) with interviews of students at different achievement levels. The intervention was organized into five phases: recognizing AI errors, learning to formulate effective prompts, critically auditing AI responses, adapting support to each level, and agreeing on an ethical use commitment. Before the literacy process, students achieved strong results with AI assistance, but their performance dropped sharply when solving problems on their own. After the intervention this gap narrowed, autonomous performance improved significantly (a moderate, educationally meaningful effect), and the proportion of students at risk of failing was cut in half; interviews confirmed a more conscious and strategic use of the tool. The study concludes that the decisive factor is not the tool itself but the pedagogy surrounding it: deliberate literacy allows GenAI to function as genuine support for learning rather than a substitute for thinking.
Downloads
References
Baidoo-Anu, D., y Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62. Recuperado de https://dergipark.org.tr/en/pub/jai/issue/77844/1337500
Braun, V., y Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa
Chan, C. K. Y., y Hu, W. (2023). Students' voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in , 20(1), 43. https://link.springer.com/article/10.1186/s41239-023-00411-8
Cotton, D. R. E., Cotton, P. A., y Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228-239. https://www.tandfonline.com/doi/full/10.1080/14703297.2023.2190148
Creswell, J. W., y Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Thousand Oaks, CA: SAGE Publications.
Crompton, H., y Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22. https://doi.org/10.1186/s41239-023-00392-8
Dahlkemper, M. N., Lahme, S. Z., y Klein, P. (2023). How do physics students evaluate artificial intelligence responses on comprehension questions? A study on the impact of AI literacy. Physical Review Physics Education Research, 19(1), 010142. Recuperado de https://journals.aps.org/prper/abstract/10.1103/PhysRevPhysEducRes.19.010142
Dai, Y., Liu, A., Dai, D., Cao, Y., Li, S., y Zhang, C. (2023). Reconceptualizing ChatGPT and generative AI as a student-driven innovation in higher education. Journal of Educational Technology & Society, 26(3), 201-215. https://doi.org/10.1016/j.procir.2023.05.002
Deng, J., y Lin, Y. (2023). The benefits and challenges of ChatGPT: An overview. Frontiers in Computing and Intelligent Systems, 2(2), 81-83. https://doi.org/10.54097/fcis.v2i2.4465
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... y 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, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Eager, B., y Brunton, R. (2023). Prompting higher education towards AI-augmented teaching and learning practice. Journal of University Teaching & Learning Practice, 20(5), 1-19. https://doi.org/10.53761/1.20.5.02
Ertmer, P. A., y Newby, T. J. (2013). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 26(2), 43-71. Recuperado de https://onlinelibrary.wiley.com/doi/10.1002/piq.21143
Farrokhnia, M., Banihashem, S. K., Noroozi, O., y Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460-474. https://doi.org/10.1080/14703297.2023.2195846
García-Peñalvo, F. J. (2023). The perception of artificial intelligence in educational contexts after the launch of ChatGPT: Disruption or panic? Education in the Knowledge Society, 24, e31279. https://doi.org/10.14201/eks.31279
Grassini, S. (2023). Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), 692. Recuperado de https://www.mdpi.com/2227-7102/13/7/692
Hernández-Sampieri, R., y Mendoza, C. P. (2018). Metodología de la investigación: Las rutas cuantitativa, cualitativa y mixta. Ciudad de México, México: McGraw Hill.
Jeon, J., y Lee, S. (2023). Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT. Education and Information Technologies, 28(12), 15873-15892. Recuperado de https://link.springer.com/article/10.1007/s10639-023-11834-1
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... y Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. Recuperado de https://doi.org/10.1016/j.lindif.2023.102274
Khalil, M., y Er, E. (2023). Will ChatGPT get you caught? Rethinking of plagiarism detection. En Y. Ono y T. Kano (Eds.), Proceedings of the International Conference on Human-Computer Interaction (pp. 475-487). Cham: Springer. Recuperado de https://link.springer.com/chapter/10.1007/978-3-031-34411-4_32
Lai, C. Y., Cheung, K. Y., y Chan, C. S. (2023). Exploring the role of intrinsic motivation in ChatGPT adoption to support active learning: An extension of the technology acceptance , 5, 100178. https://doi.org/10.1016/j.caeai.2023.100178
Lim, W. M., Gunasekara, A., Pallant, J. L., Rajendran, J. A., y Pham, N. M. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. International Journal of Management Education, 21(2), 100790. Recuperado de https://www.sciencedirect.com/science/article/pii/S1472811723000289
Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410
Long, D., y Magerko, B. (2020). What is AI literacy? Competencies and design considerations. En CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-16). New York: ACM. Recuperado de https://dl.acm.org/doi/10.1145/3313831.3376727
Memarian, B., y Doleck, T. (2023). ChatGPT in education: Methods, potentials, and limitations. Computers in Human Behavior: Artificial Humans, 1(2), 100022. https://doi.org/10.1016/j.chbah.2023.100022
Meyer, J. G., Urbanowicz, R. J., Martin, P. C. N., O'Connor, K., Li, R., Peng, P. C., ... y Moore, J. H. (2023). ChatGPT and large language models in academia: Opportunities and challenges. BioData Mining, 16(1), 20. https://doi.org/10.1186/s13040-023-00339-9
Mishra, P., y Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017-1054. Recuperado de https://journals.sagepub.com/doi/10.1111/j.1467-9620.2006.00684.x
Mollick, E. R., y Mollick, L. (2023). Assigning AI: Seven approaches for students, with prompts. The Wharton School Research Paper. Recuperado de https://arxiv.org/abs/2306.10052
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., y Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. Recuperado de https://www.sciencedirect.com/science/article/pii/S2666920X21000357
Noy, S., y Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192. Recuperado de https://www.science.org/doi/10.1126/science.adh2586
Ouyang, F., y Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020. Recuperado de https://www.sciencedirect.com/science/article/pii/S2666920X2100014X
Perkins, M. (2023). Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching & Learning Practice, 20(2), 1-24. https://doi.org/10.53761/1.20.02.07
Rudolph, J., Tan, S., y Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning and Teaching, 6(1), 342-363. https://doi.org/10.37074/jalt.2023.6.1.9
Sabzalieva, E., y Valentini, A. (2023). ChatGPT and artificial intelligence in higher education: Quick start guide. París: UNESCO, International Institute for Higher Education in Latin America and the Caribbean. Recuperado de https://unesdoc.unesco.org/ark:/48223/pf0000385146
Sallam, M. (2023). ChatGPT utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare, 11(6), 887. https://doi.org/10.3390/healthcare11060887
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.
Wardat, Y., Tashtoush, M., AlAli, R., y Saleh, S. (2024). Artificial intelligence in education: Mathematics teachers' perspectives, practices, and challenges. Iraqi Journal for Computer Science and Mathematics, 5(1), 60-77. Recuperado de: https://ijcsm.researchcommons.org/ijcsm/vol5/iss1/20/
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., ... y Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90-112. https://doi.org/10.1111/bjet.13370
Zawacki-Richter, O., Marín, V. I., Bond, M., y Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education — Where are the , 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. En M. Boekaerts, P. R. Pintrich y M. Zeidner (Eds.), Handbook of self-regulation (pp. 13-39). San Diego, CA: Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Melvin Ramírez (Autor/a)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Eres libre de:
- Compartir : copiar y redistribuir el material en cualquier medio o formato
- Adaptar : remezclar, transformar y desarrollar el material
- El licenciante no puede revocar estas libertades siempre y cuando usted cumpla con los términos de la licencia.
En los siguientes términos:
-
- Atribución : Debe otorgar el crédito correspondiente , proporcionar un enlace a la licencia e indicar si se realizaron cambios . Puede hacerlo de cualquier manera razonable, pero no de ninguna manera que sugiera que el licenciante lo respalda a usted o a su uso.
- No comercial : no puede utilizar el material con fines comerciales .
- CompartirIgual — Si remezcla, transforma o construye sobre el material, debe distribuir sus contribuciones bajo la misma licencia que el original.
- Sin restricciones adicionales : no puede aplicar términos legales ni medidas tecnológicas que restrinjan legalmente a otros hacer algo que la licencia permite.
