The Role of Adaptive Learning Technologies in Personalized Education

Mirin Henry Anthony

doi:10.56201/ijcsmt.vol.12.no1.2026.pg224.228

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The Role of Adaptive Learning Technologies in Personalized Education

Mirin Henry Anthony

Abstract

As educational systems strive to move beyond "one-size-fits-all" pedagogical models, Adaptive Learning Technologies powered by Artificial Intelligence (AI) have emerged as a critical solution for personalization. This paper presents a systematic literature review examining the efficacy of ALTs in enhancing learner engagement, academic performance, and equity. Analyzing peer-reviewed studies published between 2019 and 2025, the review focuses on the transition from rule-based systems to advanced Machine Learning (ML) algorithms, specifically contrasting Bayesian Knowledge Tracing with Deep Knowledge Tracing and Transformer-based models. Key findings indicate that ALTs yield a significant positive effect on cognitive learning outcomes and bolster student motivation (Chaudhry et al., 2025; Martinez & Anderson, 2024). However, the study identifies critical barriers, including the "cold start" problem, algorithmic bias in minority representations, and the "Black Box" opacity of neural networks (Baker & Hawn, 2021; Heaven, 2024). The paper concludes that while ALTs are potent tools for democratization, their sustainable success is contingent upon the integration of Explainable AI frameworks and robust fairness-aware algorithms (Liu et al., 2024).

Keywords

Adaptive learningDeep Knowledge TracingExplainable AIeducational data miningalgorithmic fairness. I. Introduction Traditional educational models often fail to account for the variance in learn

References

Akanbi, O., et al. (2024). A systematic review of state-of-the-art explainable AI in education. Scholarly Summit Journal. Baker, R. S., & Hawn, A. (2021). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 32, 1052-1092. Chaudhry, M. A., et al. (2025). Artificial intelligence in adaptive education: A systematic review of techniques for personalized learning. IEEE Access. Chen, M. (2024). Personalized learning through AI: Pedagogical approaches and critical insights. Contemporary Educational Technology, 17(2). Heaven, W. D. (2024). Ethical challenges of AI in education: Balancing innovation with data privacy. MIT Technology Review. Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, 100017. Liu, Z., et al. (2024). Explainable AI in education: A systematic review of deep knowledge tracing interpretability. International Journal of Artificial Intelligence in Education. Martin, F., Chen, Y., Moore, R. L., & Westine, C. D. (2020). Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018. Educational Technology Research and Development, 68(4), 1903-1929. Martinez, L., & Anderson, J. (2024). The impact of AI-assisted personalized learning on student academic achievement. Journal of Educational Computing Research. Peng, H., Ma, S., & Spector, J. M. (2019). Personalized adaptive learning: An overview. Information and Learning Sciences, 120(1/2), 24-49. Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review. Computers & Education, 140, 103599. Zambrano, A. (2024). Fairness of Bayesian knowledge tracing for math learners of different reading ability. Proceedings of the 17th International Conference on Educational Data Mining. Zhang, J., et al. (2023). Deep knowledge tracing with learning curves: A comparison of LSTM and transformer models. Frontiers in Psychology.

The Role of Adaptive Learning Technologies in Personalized Education

Abstract

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