The New Educational Frontier: Agentic AI's Evolutionary Journey Through The Lens of SPAR-4-SLR
Abstract
Abstract
The emergence of Agentic Artificial Intelligence (AI) represents a fundamental transformation in educational technology, characterized by systems capable of autonomous, adaptive, and proactive operation. This systematic literature review employs the SPAR-4-SLR methodology to analyze 281 publications from Scopus, with 251 articles retained after temporal filtering (2010-2024), revealing the evolutionary trajectory of Agentic AI in education. The analysis identifies three distinct evolutionary eras: the Early Era (2010-2015) characterized by rule-based intelligent tutoring systems exhibiting proto-agentic behaviors, the Transitional Era (2016-2019) marked by enhanced adaptive systems leveraging learning analytics and machine learning, and the Agentic Era (2020-2024) distinguished by sophisticated autonomous systems powered by Large Language Models. Through integrated bibliometric, co-occurrence network, and thematic analyses, the study establishes a conceptual framework encompassing five defining characteristics of Agentic AI: learning initiative, dynamic adaptability, multi-modal interaction, persistence and memory, and collaboration with human actors. Co-citation network analysis reveals the intellectual structure connecting foundational intelligent tutoring research to contemporary generative AI applications. Despite exponential growth in publications, significant gaps persist in theoretical conceptualization of agency, longitudinal impact evidence, and implementation across diverse educational contexts, particularly at primary education levels. This study provides a comprehensive research agenda addressing theoretical, methodological, and implementation gaps to advance the effective and equitable development of Agentic AI in education.
Abstrak
Kemunculan Agen Artificial Intelligence (AI) merepresentasikan transformasi fundamental dalam teknologi pendidikan, yang dicirikan oleh sistem yang mampu beroperasi secara otonom, adaptif, dan proaktif. Tinjauan literatur sistematis ini menggunakan metodologi SPAR-4-SLR untuk menganalisis 281 publikasi dari Scopus, dengan 251 artikel dipertahankan setelah penyaringan temporal (2010-2024), mengungkap trajektori evolusi Agen AI dalam pendidikan. Analisis mengidentifikasi tiga era evolusi yang berbeda: Era Awal (2010-2015) yang dicirikan oleh sistem tutoring cerdas berbasis aturan yang menunjukkan perilaku proto-agentik, Era Transisional (2016-2019) yang ditandai oleh sistem adaptif yang memanfaatkan analitik pembelajaran dan pembelajaran mesin, dan Era Agentik (2020-2024) yang dibedakan oleh sistem otonom yang canggih dan didukung oleh Model Bahasa Besar. Melalui analisis bibliometrik, jaringan ko-okurensi, dan tematik yang terintegrasi, studi ini menetapkan kerangka konseptual yang mencakup lima karakteristik pendefinisi Agen AI: inisiatif pembelajaran, adaptabilitas dinamis, interaksi multi-modal, persistensi dan memori, serta kolaborasi dengan aktor manusia. Analisis jaringan ko-sitasi mengungkap struktur intelektual yang menghubungkan penelitian tutoring cerdas fundamental dengan aplikasi AI generatif kontemporer. Meskipun terjadi pertumbuhan eksponensial dalam publikasi, kesenjangan signifikan masih tetap ada dalam konseptualisasi teoretis mengenai agensi, bukti dampak longitudinal, dan implementasi di berbagai konteks pendidikan, khususnya pada tingkat pendidikan dasar. Studi ini menyediakan agenda penelitian komprehensif yang mengatasi kesenjangan teoretis, metodologis, dan implementasi untuk memajukan pengembangan Agen AI yang efektif dan berkeadilan dalam pendidikan.Keywords
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Acharya, D. B., Kuppan, K., & Divya, B. (2025). Agentic AI: Autonomous intelligence for complex goals—a comprehensive survey. IEEE Access, 13, 18912–18936. https://doi.org/10.1109/ACCESS.2025.3532853
Alvarado, A., Aguilar, R., Domínguez‐Andrés, J., Angeles, J., Anicas, M., Apacible, K., Arcilla, G., Arcilla, R., & Calacday, C. (2025). Evaluating the effectiveness of AI-generated health educational videos on nursing students’ knowledge acquisition of the international patient safety goals (IPSG). Journal of Interdisciplinary Perspectives, 3(10), 222–229. https://doi.org/10.69569/jip.2025.622
Bandi, A., Kongari, B., Naguru, R., Pasnoor, S., & Vilipala, S. V. (2025). The Rise of Agentic AI: A review of definitions, frameworks, architectures, applications, evaluation metrics, and challenges. Future Internet, 17(9), 404. https://doi.org/10.3390/fi17090404
Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52(1), 1–26. https://doi.org/10.1146/annurev.psych.52.1.1
Baylor, A. L., & Kim, Y. (2005). Simulating instructional roles through pedagogical agents. International Journal of Artificial Intelligence in Education, 15(2), 95–115.
Biesta, G., & Tedder, M. (2007). Agency and learning in the lifecourse: Towards an ecological perspective. Studies in the Education of Adults, 39(2), 132–149. https://doi.org/10.1080/02660830.2007.11661545
Callon, M. (1984). Some elements of a sociology of translation: Domestication of the scallops and the fishermen of St Brieuc Bay. The Sociological Review, 32(1), 196–233. https://doi.org/10.1111/j.1467-954X.1984.tb00113.x
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Dai, L., Jiang, Y.-H., Chen, Y., Guo, Z., Liu, T.-Y., & Shao, X. (2024). Agent4EDU: Advancing AI for education with agentic workflows. Proceedings of the 2024 3rd International Conference on Artificial Intelligence and Education, 180–185. https://doi.org/10.1145/3722237.3722268
Dieker, L., Hines, R., Wilkins, I., Hughes, C. E., Scott, K. H., Smith, S., Ingraham, K., Ali, K., Zaugg, T., & Shah, S. (2024). Using an artificial intelligence (AI) agent to support teacher instruction and student learning. Journal of Special Education Preparation. https://doi.org/10.33043/d8xb94q7
Frankfurt, H. G. (1971). Freedom of the will and the concept of a person. The Journal of Philosophy, 68(1), 5–20. https://doi.org/10.2307/2024717
Holmes, W., Iniesto, F., Anastopoulou, S., & Boticario, J. G. (2023). Stakeholder perspectives on the ethics of AI in distance-based higher education. International Review of Research in Open and Distributed Learning, 24(2), 96–117. Scopus. https://doi.org/10.19173/irrodl.v24i2.6089
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of ai in education: towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(3), 504–548. https://doi.org/10.1007/s40593-021-00239-1
Hosseini, S., & Seilani, H. (2025). The role of agentic AI in shaping a smart future: A systematic review. Array, 26, 100399. https://doi.org/10.1016/j.array.2025.100399
Jacobson, M. J., Kapur, M., & Reimann, P. (2016). Conceptualizing debates in learning and educational research: Toward a complex systems conceptual framework of learning. Educational Psychologist, 51(2), 210–218. https://doi.org/10.1080/00461520.2016.1166963
Jiang, R., Chen, Y., Yi, P., Xie, S., & Qu, D. (2024). Opportunities and challenges of AI in vocational education. International Journal of Learning and Teaching, 10(5), 590–596. https://doi.org/10.18178/ijlt.10.5.590-596
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. Learning and Individual Differences, 103. https://doi.org/10.1016/j.lindif.2023.102274
Kim, Y., & Baylor, A. L. (2016). Based design of pedagogical agent roles: A review, progress, and recommendations. International Journal of Artificial Intelligence in Education, 26(1), 160–169.
Lane, H. C., & Schroeder, N. L. (2022). Pedagogical agents. Conference Proceedings. https://doi.org/10.1145/3563659.3563669
Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford University Press.
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.
Liang, J., Stephens, J. M., & Brown, G. T. L. (2025). A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1522841
Lim, J. (2024). The potential of learning with AI-generated pedagogical agents in instructional videos. Conference Proceedings. https://doi.org/10.1145/3613905.3647966
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2022). Intelligence unleashed: An argument for AI in education (2, Ed.). Pearson.
MacLellan, C. J., Harpstead, E., Patel, R., & Koedinger, K. R. (2022). Discovering how students learn in an intelligent tutoring system: Visualizing model-based learner estimates versus interaction-based algorithms. Journal of Educational Data Mining, 14(1), 1–30.
https://doi.org/10.5281/zenodo.6520326
Mollick, E. R., Mollick, L., Bach, N., Ciccarelli, L., Przystanski, B., & Ravipinto, D. (2024). AI agents and education: Simulated practice at scale. Computers and Society. https://doi.org/10.48550/arxiv.2407.12796
Paivia, S. (2014). The role of constructivism in a cognitive load theory framework. Educational Psychology Review, 26(2), 225–238. https://doi.org/10.1007/s10648-013-9254-y
Paul, J., Lim, W. M., O’Cass, A., Hao, A. W., & Bresciani, S. (2021). Scientific procedures and rationales for systematic literature reviews (SPAR‐4‐SLR). International Journal of Consumer Studies, 45(4), O1-O16.
Rammert, W. (2012). Distributed agency and advanced technology. Or: How to analyse constellations of collective inter-agency. In J. H. Passoth, B. Peuker, & M. Schillmeier (Eds.), Agency without actors? New approaches to collective action (pp. 89–112). Routledge. https://doi.org/10.4324/9780203834695-8
Roll I. & Wylie R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3
Searle, J. R. (2004). Mind: A brief introduction. Oxford University Press.
Shapiro, L. (2019). Embodied cognition. Routledge.
Sweller, J. (2011). Cognitive load theory. In Psychology of learning and motivation (Vol. 55, pp. 37–76). Elsevier.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Zawacki-Richter O., Marín V.I., Bond M., & Gouverneur F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1). https://doi.org/10.1186/s41239-019-0171-0
DOI: https://doi.org/10.38038/vocatech.v7i2.249
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Vocatech : Vocational and Technology Journal
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Published by: Lembaga Penelitian dan Pengabdian Masyarakat & Penjaminan Mutu Akademi Komunitas Negeri Aceh Barat