The University Course Timetabling Problem (UCTP) is a well-known NP-hard multi-objective optimization problem influenced by variety of factors ranging from institutional policies and facilities, institutional facilities, course characteristics to instructor availability, instructor preferences and the diversity and variability of student registration behaviors. This paper introduces GADA, a novel approach to automate course timetabling. Unlike traditional models that mostly assume static, centralized scheduling, GADA is an adaptive geneticalgorithm-based approach tailored for decentralized, credit-based systems where students independently register for courses and instructors have diverse time preferences. GADA focuses on optimizing course-to-timeslot allocation while satisfying both hard institutional constraints and soft instructor preferences. By automating critical steps in the scheduling workflow, GADA significantly reduces manual effort, increases scheduling flexibility, and adapts efficiently to late-stage registration changes. It has been implemented and evaluated extensively in the real-world environment at the Faculty of Computer Science and Engineering at Ho Chi Minh City University of Technology (CSE@HCMUT). The experimental results demonstrate GADA’s effectiveness and practical applicability in generating conflict-free and operationally feasible schedules while addressing the complex constraints inherent in decentralized academic timetabling.