Artificial intelligence in education design and evaluation of adaptive learning systems

Abstract

The rapid development of AI has enabled the education sector to better cater to the diverse learning needs of students. Adaptive
learning systems, through personalized content delivery and real-time feedback, offer learners a more tailored educational experience that
aligns with their individual differences. The article begins by introducing the background of AI in education, emphasizing the crucial role of
adaptive learning systems in addressing challenges present in traditional educational approaches. Subsequently, it delves into the fundamental
principles guiding the design of adaptive learning systems, including aspects such as personalization, adaptability, and real-time feedback.
The paper provides a detailed explanation of the key components of adaptive learning systems, encompassing learner modeling, content
adaptation, and assessment strategies. Challenges encountered during the implementation of adaptive learning systems are discussed, accom_x005fpanied by proposed solutions and best practices. To assess the effectiveness of adaptive learning systems, the article proposes a set of metrics
and methodologies, incorporating both quantitative and qualitative indicators such as learner performance, engagement, and satisfaction.
Through case studies, the paper showcases successful implementations of adaptive learning systems in diverse educational environments,
concluding with a summary of positive outcomes and lessons learned.