AI AND ADAPTIVE LEARNING: EVALUATING PERSONALIZED INTERVENTIONTS ON A DISTANCE EDUCATION PLATFORM

Abstract

Artificial Intelligence (AI) technologies have transformed higher education, especially in distance learning environments where adaptive systems personalize instruction based on individual learner needs. Intelligent Tutoring Systems (ITS), as a form of AI integration, have been shown to improve both learning outcomes and motivation (Zhang et al., 2025). This study aims to evaluate the effectiveness of AI-driven adaptive learning interventions in enhancing content mastery, time efficiency, and student engagement on the E-Learning platform. A mixed methods approach was employed, combining quantitative data from questionnaires scores and learning activity logs with qualitative data from student surveys and interviews. Quantitative analysis was conducted using paired t-tests, while thematic analysis was used for qualitative responses. Results indicate that students using the AI-adaptive platform experienced significantly greater content mastery (p < 0.05), a 25–30% reduction in study time, and a 35% increase in active engagement compared to conventional online learners. Students also reported that personalized features made the learning experience more relevant and responsive. However, concerns were raised regarding data ethics, faculty preparedness, and overreliance on automation. This study concludes that AI-based adaptive learning interventions are effective in enhancing the quality of distance education. However, their implementation must be supported by clear institutional policies, educator training, and ethical oversight to ensure responsible and sustainable use of the technology.