EKOLOGI PEMBELAJARAN BERBASIS ARTIFICIAL INTELLIGENCE: REKONSTRUKSI PARADIGMA PENDIDIKAN TINGGI DI ERA DIGITAL
DOI:
https://doi.org/10.23969/jp.v11i03.63954Keywords:
Artificial Intelligence, learning ecology, higher education, digital pedagogy, AI Learning Ecology FrameworkAbstract
The rapid advancement of Artificial Intelligence (AI), particularly Generative AI, has profoundly reshaped the landscape of higher education. This transformation extends beyond the digitalization of teaching and learning, influencing how knowledge is created, shared, and reconstructed within academic environments. Despite the growing body of research on AI in higher education, most studies continue to frame AI primarily as a pedagogical tool for improving learning effectiveness. Consequently, limited attention has been given to understanding how AI reconfigures the broader learning ecology by reshaping the relationships among human actors, technology, academic culture, and institutional governance. This study aims to reconstruct the paradigm of higher education through the perspective of AI-based learning ecology by proposing the AI Learning Ecology Framework as a conceptual model. Employing a qualitative approach with a library research method and an integrative literature review design, this study synthesizes evidence from internationally indexed journals, SINTA-accredited national journals, scholarly books, and official publications issued by UNESCO and the OECD. The collected literature was analyzed through thematic synthesis to identify recurring patterns, research gaps, and conceptual relationships. The findings indicate that the transformation of higher education should be understood as an ecological process shaped by the dynamic interaction of six interconnected components: human agency, intelligent technology, pedagogical design, academic culture, institutional governance, and ethical and humanistic values. Within this framework, AI is positioned as a cognitive partner that strengthens human capabilities rather than replacing the roles of educators and students. The study concludes that the successful integration of AI depends not merely on technological sophistication but on the ability of higher education institutions to cultivate an adaptive, collaborative, ethical, and human-centered learning ecology. The proposed AI Learning Ecology Framework offers a conceptual foundation for developing policies, curricula, and learning strategies that are responsive to the evolving demands of the digital era.
Downloads
References
Barron, B. (2006). Interest and Self-Sustained Learning as Catalysts of Development: A Learning Ecology Perspective. Human Development, 49(4), 193–224. https://doi.org/10.1159/000094368
Bearman, M., Ryan, J., & Ajjawi, R. (2023). Discourses of Artificial Intelligence in Higher Education: A Critical Literature Review. Higher Education, 86(2), 369–385. https://doi.org/10.1007/s10734-022-00937-2
Booth, A., Briscoe, S., & Wright, J. M. (2020). The “Realist Search”: A Systematic Scoping Review of Current Practice and Reporting. Research Synthesis Methods, 11(1), 14–35. https://doi.org/10.1002/jrsm.1386
Bronfenbrenner, U. (1979). The Ecology of Human Development: Experiments by Nature and Design. Cambridge, MA: Harvard University Press.
Chan, C. K. Y. (2023). A Comprehensive AI Policy Education Framework for University Teaching and Learning. International Journal of Educational Technology in Higher Education, 20(1), 38. https://doi.org/10.1186/s41239-023-00408-3
Crompton, H., & 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
Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2020). Implications for Educational Practice of the Science of Learning and Development. Applied Developmental Science, 24(2), 97–140. https://doi.org/10.1080/10888691.2018.1537791
Geng, G., Graham, L. J., Schuster, L., & Jacka, D. (2023). Reconceptualise a Dynamic Framework of the Learning Constructs in Higher Education. Higher Education Quarterly, 77(4), 780–797. https://doi.org/10.1111/hequ.12427
Jackson, N. J. (2013). The Concept of Learning Ecologies. In N. J. Jackson & B. Cooper (Eds.), Lifewide Learning, Education and Personal Development (pp. 1–21). United Kingdom: Lifewide Education. https://doi.org/10.1159/000094368
Miao, F., & Holmes, W. (2023). Guidance for Generative AI in Education and Research. Paris: UNESCO. https://doi.org/10.54675/EWZM9535
OECD. (2021). Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. Paris: OECD Publishing. https://doi.org/10.1787/589b283f-en
Priandani, A. P., Resti, F., & Wahyudin, D. (2025). Artificial Intelligence (AI) Trends in Higher Education Learning: Bibliometric Analysis. Curricula: Journal of Curriculum Development, 4(1), 1–18. https://doi.org/10.17509/curricula.v4i1.86165
Renn, K. A., & Smith, B. R. G. (2023). Ecological Models in Higher Education Research: Overview and Synthesis. New Directions for Higher Education, 2023(204), 11–22. https://doi.org/10.1002/he.20491
Siemens, G. (2005). Connectivism: A Learning Theory for the Digital Age. International Journal of Instructional Technology and Distance Learning, 2(1), 3–10.
Snyder, H. (2019). Literature Review as a Research Methodology: An Overview and Guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
Torraco, R. J. (2016). Writing Integrative Literature Reviews: Using the Past and Present to Explore the Future. Human Resource Development Review, 15(4), 404–428. https://doi.org/10.1177/1534484316671606
Veletsianos, G., & Kimmons, R. (2024). Scholars and Faculty Members’ Lived Experiences in a Generative AI Environment. Educational Technology Research and Development, 72(1), 1–18.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Pendas : Jurnal Ilmiah Pendidikan Dasar

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