AI-Driven Personalized Learning in Education: Opportunities, Scalability Challenges, and Equity Implications

Abstract

Artificial Intelligence (AI) has the potential to revolutionize the education sector by enabling personalized learning on a very large scale. In this article, we analyze the use of AI-driven models for creating adaptive learning pathways in the context of individual learner needs. We explore the vast potential that this offers for enhancing learning effectiveness, engagement, and achievement. One of the most prominent domains is the mathematical encoding of learner state of knowledge under probabilistic frameworks, i.e., Bayesian Knowledge Tracing (BKT), towards dynamic pedagogical customization. Yet, the path from theoretical modeling to actual large-scale implementation is plagued by crucial challenges. Computational functionality and infrastructural issues of scalability are comprehensively analyzed in this research, such as data processing bottlenecks and algorithmic complexity. Moreover, we examine the foundation equity issues, and the danger that AI systems pose by further intensifying or even deepening current education inequities through the issue of algorithmic prejudice. By uniting opportunity with a consideration of scalability and equity imperative in nature, this paper provides an overall framework for the challenges of using AI in education. We conclude by contending that effective and ethical deployment of AI-based personal learning relies on developing computationally efficient, transparent, and equitable algorithms.