Generative Artificial Intelligence as a Transformative Catalyst for a Learning Paradigm Shift

JaeHwan Byun; Mara Alagic

doi:10.3991/ijac.v19i2.58883

Authors

JaeHwan Byun Wichita State University, Wichita, KS, USA https://orcid.org/0000-0001-8240-652X
Mara Alagic Wichita State University, Wichita, KS, USA https://orcid.org/0000-0001-6836-0713

DOI:

https://doi.org/10.3991/ijac.v19i2.58883

Keywords:

Learning paradigm, Learning with Generative AI, Generativism, Artificial Intelligence

Abstract

The rapid integration of Generative AI (Gen AI) into education has raised fundamental questions about its influence on learning, prompting debate on whether it represents a new learning paradigm. This inquiry exploration contributes to the discussion on whether the unique dynamics of learning with Gen AI implies a new learning paradigm. By establishing the criteria for a paradigm shift, we analyzed the anomalies that Gen AI creates for established traditional paradigms (i.e., Behaviorism, Cognitivism, and Constructivism) demonstrating their insufficiency to explain the dynamics of the human learning with Gen AI. Hence, we propose and formally define a new paradigm in which learning is understood as a distributed process enacted through a human engagement with Gen AI-artifact system. In this model, the learner acts as an orchestrator, guiding iterative sequence of prompting, generation, critique, and refinement. The new paradigm will provide educators and researchers with a coherent conceptual language to design and study learning in the age of Gen AI. The study concludes by outlining verifiable suggestions to guide future empirical validation of this new paradigm.

Author Biographies

JaeHwan Byun, Wichita State University, Wichita, KS, USA

Dr. Jaehwan Byun is an Associate Professor in the School of Education at Wichita State University. He specializes in instructional design, technology integration, and online learning. He earned his Ph.D. in Curriculum and Instruction from Southern Illinois University Carbondale, focusing on the effects of immersive technologies on learner engagement. Dr. Byun's research explores innovative educational methods, including game-based learning, learning analytics, and the application of artificial intelligence in education. His work has been published in respected journals such as Computers in Human Behavior and School Science and Mathematics, with some articles receiving top download awards. Throughout his career, Dr. Byun has secured several grants, including NASA-funded projects, to advance professional development for educators and improve access to cutting-edge learning technologies. He has been recognized for his educational contributions, including the Excellence in Online Teaching award from Wichita State University. As an active member of the educational technology community, Dr. Byun serves as a reviewer for multiple journals and conferences. He has presented his research at numerous national and international conferences, including the Association for Educational Communications and Technology (AECT). Dr. Byun's teaching portfolio encompasses various courses in educational technology, instructional design, and STEM education. He is committed to preparing future educators and researchers to leverage technology effectively in diverse learning environments. His ongoing research examines how digital tools, including AI, can foster equity and engagement in education, contributing to the transformation of educational practices globally.

Mara Alagic, Wichita State University, Wichita, KS, USA

Dr. Mara Alagic, PhD in Mathematics, is a Professor and Graduate coordinator of the MEd Learning and Instructional Design graduate program at Wichita State University, USA, and a Visiting Professor at the Johannes Kepler University, STEAM Education Department, Linz Austria. She is Editor in Chief of the Journal of Mathematics and the Arts published by Taylor & Francis. Her eclectic research agenda is centered around collaborative transdisciplinary and global learning. The research studies span from Technology in the Mathematics Classroom published by the Journal of Computers in Mathematics and Science Teaching to Adaptive Scaffolding Toward Transdisciplinary Collaboration published by Springer in the Learning Ideas Conference Proceedings. The most recent collaborative research study “When Engineering Design Meets STEAM Education in a Hybrid Learning Environment” is published in the Asia Pacific Journal of Education. Her current research focus is the role of Generative AI in transformative learning, teaching, and communication. The most recent presentations were at the Joint Mathematics Meeting 2024 (Artfulness in STEAM: Creativity, Innovation, and Change), the Learning Ideas Conference 2024: Utilizing Gen AI to Simulate Intercultural Dialogues and in 2025: Transformative Learning in the Age of Generative AI: Educators' Perspectives on a Paradigm Shift (with Jaehwan Byun).

References

[1] Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

[2] Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8

[3] Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), pp. 187–192. https://doi.org/10.1126/science.adh2586

[4] Gruenhagen, J. H., Sinclair, P. M., Carroll, J. A., Baker, P. R., Wilson, A., & Demant, D. (2024). The rapid rise of generative AI and its implications for academic integrity: Students’ perceptions and use of chatbots for assistance with assessments. Computers and Education: Artificial Intelligence, 7, 100273. https://doi.org/10.1016/j.caeai.2024.100273

[5] Alagic, M., & Byun, J. (2025). Transformative learning in the age of generative AI: Educators' perspectives on a paradigm shift. In Proceedings of The Learning Ideas Conference 2025, New York, NY.

[6] Pratschke, B. M. (2024). Generativism. In Generative AI and Education: Digital Pedagogies, Teaching Innovation and Learning Design (pp. 57–72). Cham: Springer Nature Switzerland.

[7] Kuhn, T. S. (1962). The Structure of Scientific Revolutions. Chicago: University of Chicago Press.

[8] Ertmer, P. A., & Newby, T. J. (2013). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 26(2), pp. 43–71. https://doi.org/10.1002/piq.21143

[9] Kuhn, T. S. (1970). The Structure of Scientific Revolutions (2nd ed.). Chicago: University of Chicago Press.

[10] Skinner, B. F. (1953). Science and Human Behavior. New York: Macmillan.

[11] Neisser, U. (1967). Cognitive Psychology. Englewood Cliffs, NJ: Prentice-Hall.

[12] Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Cambridge, MA: Harvard University Press.

[13] Bruner, J. (1990). Acts of Meaning. Cambridge, MA: Harvard University Press.

[14] Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), pp. 3–10. http://www.itdl.org/Journal/Jan_05/article01.htm

[15] Kleingeld, P., & Willaschek, M. (2019). Autonomy without paradox: Kant, self-legislation and the moral law. Philosophers’ Imprint, 19(6), pp. 1–18. https://doi.org/2027/spo.3521354.0019.006

[16] Floridi, L., & Sanders, J. W. (2004). On the morality of artificial agents. Minds and Machines, 14(3), pp. 349–379. https://doi.org/10.1023/B:MIND.0000035461.63578.9d

[17] Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), pp. 906–911. https://doi.org/10.1037/0003-066X.34.10.906

[18] Hutchins, E. (2000). Distributed cognition. In H. Pashler (Ed.), Stevens’ Handbook of Experimental Psychology (pp. 425–455). New York: Wiley.

Generative Artificial Intelligence as a Transformative Catalyst for a Learning Paradigm Shift

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Abstract

Author Biographies

JaeHwan Byun, Wichita State University, Wichita, KS, USA

Mara Alagic, Wichita State University, Wichita, KS, USA

References

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