In educational systems, there has been an increasing interest for the innovative applications such as educational data mining and predictive analytics. These applications are utilized by the institutions for fulfilling academic missions and for improving the utilization of institutional resources. In this paper, we propose the “Curriculum GPS�, an adaptive curriculum generation and planning system, to provide a quantitative model and an interactive system that helps to grow and maintain programs with high retention and satisfaction rates in college, military or corporate education. The Curriculum GPS is composed of three main components: Curriculum analysis, historical data mining and an adaptive course sequence generation. The existing literature demonstrates how curricular efficiency correlates to student academic success in terms of graduation and retention rates. Therefore we first use an approach from the literature to analyze the curriculum under discussion as a directed graph by considering the conditions among courses such as prerequisite requirements. We conduct network analysis in this graph and compare our results with the catalog of courses currently in use. Then we combine this analysis with the historical data of the students and courses to train our model and develop our system’s database. The resulting system uses this training to create a set of quantitative recommendations for each student depending on her individual data such as passed/remaining courses, grades and time to graduate. The system also allows running what-if scenarios to test the outcomes of different choices by students. Therefore it is advantageous for students, instructors and advisors. The system is being developed for the Information Technology based departments of one of the largest universities in US by using the curricula and student datasets from the last thirty semesters. Initial results suggest this novel system provides both insight and improvement for the institutional education.