Artificial Intelligence in Curriculum Design: A Data-Driven Approach to Higher Education Innovation

This paper presents evidence that Artificial Intelligence has fundamentally reshaped college curricula by promoting a data-driven personalization approach that improves student outcomes and aligns educational programs with workforce needs. Specifically, predictive analytics, machine learning algorithms, and natural language processing were applied here to evaluate student performance and identify at-risk students to propose personalized learning pathways. Results indicated that AI-based curriculum achieved much higher course completion rates (89.72%) as well as retention (91.44%) and dropout rates (4.98%) compared to the traditional model. Sentiment analysis of learner feedback showed a more positive learning experience, while regression and ANOVA analyses proved the impact of AI on enhancing academic performance to be real. Learning content delivery for each student was therefore continuously improved based on individual learner characteristics and industry trends by AI-enabled recommender systems and adaptive learning models. Its advantages notwithstanding, the study emphasizes the need to address ethical concerns, ensure data privacy safeguards, and mitigate algorithmic bias before an equitable outcome can be claimed. These findings can inform institutions aspiring to adopt AI-driven models for curriculum innovation to build a more dynamic, responsive, and learner-centered educational ecosystem.