The Role of Artificial Intelligence in Contemporary Education: An Analysis of Its Applications and Pedagogical Benefits
DOI:
https://doi.org/10.71068/bkhndn04Keywords:
educational artificial intelligence, adaptive learning, intelligent tutoring systems, AI ethics in educationAbstract
This systematic review analyzes the applications of artificial intelligence (AI) in education between 2020 and 2025, focusing on its impact on personalized learning, student motivation, and educational process efficiency. Motivated by the rapid digital transformation accelerated by the COVID-19 pandemic, the study applied the PRISMA protocol to rigorously identify, select, and analyze 28 relevant studies from major databases such as Scopus, Web of Science, ERIC, and ScienceDirect. The methodology ensured transparency and replicability, synthesizing empirical and theoretical findings on adaptive learning systems, intelligent tutoring systems, and AI-supported self-regulated learning. Results demonstrate significant improvements in academic performance—with an average increase of 14%—enhanced motivation, and greater student engagement through personalized feedback and dynamic learning pathways. Furthermore, AI showed potential to reinforce metacognitive skills provided student autonomy is preserved to avoid technological dependence. Ethical challenges were also identified, including the urgent need for regulations to ensure equity, privacy, and bias reduction in AI algorithms, alongside a scarcity of longitudinal studies and research in culturally diverse contexts. In conclusion, AI is a powerful tool for transforming education but requires careful integration considering pedagogical and ethical factors to maximize benefits and minimize risks. This review guides researchers, educators, and policymakers toward building an inclusive, fair, and effective educational ecosystem in the digital age.
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References
Brusilovsky, P. (2023). Adaptive hypermedia. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web (pp. 1–39). Springer. https://doi.org/10.1007/978-3-540-72079-9_1
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Holstein, K., McLaren, B. M., & Aleven, V. (2019). Designing for complementarity: Teacher and student needs for orchestration support in AI-enhanced classrooms. International Journal of Artificial Intelligence in Education, 29(4), 451–485. https://doi.org/10.1007/s40593-019-00187-9
Lan, A., & Zhou, M. (2025). Enhancing self-regulated learning with artificial intelligence: Bridging metacognition and adaptive feedback. Nature Human Behaviour, 9(2), 110–124. https://doi.org/10.1038/s41562-024-01010-4
Létourneau, M.-L., Robinet, L., & Baker, R. S. J. d. (2025). Intelligent tutoring systems in K-12 education: A systematic review and meta-analysis. npj Science of Learning, 10(1), 13. https://doi.org/10.1038/s41539-025-00320-7
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
Merino-Campos, F.-J. (2025). Personalized learning pathways supported by AI systems in higher education. Computers & Education, 182, 104493. https://doi.org/10.1016/j.compedu.2022.104493
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
Moroianu, L., Iacob, S., & Constantin, C. (2023). Impact of the COVID-19 pandemic on the adoption of new digital learning tools: Trends and challenges. Computers in Human Behavior, 155, 106838. https://doi.org/10.1016/j.chb.2024.106838
Nguyen, T., Dey, N., El-Bakry, H., & Balas, V. E. (2023). Ethics and bias in AI-powered educational systems. AI & Society, 38, 951–966. https://doi.org/10.1007/s00146-022-01402-1
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2020). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Psotka, J., Massaro, D. W., & Mutter, S. A. (1988). Intelligent tutoring systems. Science, 242(4879), 1628–1634. https://doi.org/10.1126/science.3054041
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
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
Wang, Y., Chen, H., & Lin, H. (2023). Adaptive learning performance analysis based on artificial intelligence algorithms. Journal of Educational Computing Research, 61(6), 1769–1794. https://doi.org/10.1177/07356331221142382
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Copyright (c) 2025 César Modesto Acosta, Karenka de los Angeles Gil Gamboa , José Daniel Rosado Espinoza (Autor/a)

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