AI-Driven Adaptive Learning Platforms: Addressing the Challenges of Learner Engagement

Abstract

Learner engagement represents a perennial and multifaceted challenge within educational ecosystems, directly correlating with knowledge retention, academic achievement, and long-term motivation. Traditional, one-size-fits-all pedagogical models often struggle to accommodate the diverse cognitive profiles, prior knowledge, and pacing needs of individual learners, leading to disengagement and attrition. This paper examines the transformative potential of Artificial Intelligence (AI) in mitigating these challenges through adaptive learning platforms. By leveraging sophisticated algorithms, including machine learning and knowledge space theory, these systems dynamically construct real-time models of each learner's knowledge state, misconceptions, and engagement levels. Subsequently, they personalize the sequencing, difficulty, and modality of educational content. This research synthesizes current literature and evidence to argue that AI-driven adaptation—through personalized learning pathways, timely intervention, and interactive feedback mechanisms—serves as a critical instrument for sustaining learner engagement, fostering metacognitive skills, and ultimately improving educational outcomes in both formal and corporate training environments.