A Comprehensive Survey on AI-enabled Timetable Scheduling for Multidisciplinary Education under NEP 2020 - Nishu; N, Likhitha; V, Manojna; N. R, Deepak (Journal of Innovations in Data Science and Big Data Management, 2026)
With a focus on flexibility, interdisciplinarity, and learner choice, the National Education Policy (NEP) 2020 has complicated the scheduling of academic programs. Historically, administrators have relied on computerisation for academic timetable generation, examples of scheduling, elective coursework and faculty preferences. As a result, institutions may struggle to create conflict- free schedules, classroom utilisation, and balance faculty workload as a result of administrative scheduling problems. This paper outlines the development of an academic Artificial Intelligence timetable generation system (TGS) using heuristic optimisation and constraint satisfaction methods to automate and optimise the scheduling process. The system is capable of processing a range of institutional data inputs such as course structures, faculty availability, classroom sizes, and student enrollments. It consists of three primary functional modules—ADIL (Academic Data Integration Layer), ISL (Intelligent Scheduling Logic), and AOL (Adaptive Optimisation Layer)—that automatically communicate and provide a process to develop efficient, flexible, and conflict-free schedules. The ADIL gathers and normalises scheduling data from a variety of academic departments, the ISL uses heuristic algorithms and rule-based logic to dynamically apply constraints, and the AOL further optimises the schedule through a combination of adaptive optimisation strategies to improve overall efficiency. An experimental evaluation conducted in a university environment demonstrates that the proposed system achieves a substantial reduction in manual scheduling effort and a 70 percent improvement in timetable generation time. Additionally, the system enhances conflict resolution accuracy, scalability, and adaptability to policy- driven academic frameworks.