Investigation of AI Techniques in Conflict-Free Academic Scheduling

Abstract

The academic timetabling problem is one of the most challenging tasks faced by educational institutions, involving the allocation of courses, instructors, student groups, and classrooms into limited timeslots without conflicts. Traditional manual and semi-automated approaches are often time-consuming, error-prone, and inefficient in handling dynamic constraints such as elective courses, interdisciplinary programs, hybrid learning models, and resource limitations. To address these challenges, researchers have increasingly turned toward artificial intelligence (AI) and metaheuristic optimization methods for designing conflict-free and adaptive scheduling systems. This survey paper provides a comprehensive overview of AI-driven approaches to academic timetabling, with particular emphasis on hybrid solutions that integrate Genetic Algorithms (GA), Reinforcement Learning (RL), and other machine learning techniques.

We systematically analyse recent research contributions from 2013 to 2025, categorizing them based on their methodologies, key objectives, and performance outcomes. Our review highlights the progression from classical heuristic models to advanced hybrid AI frameworks capable of real-time adaptation, predictive analytics, and multi-objective optimization. We also present a comparative literature survey in tabular form, summarizing the strengths and limitations of over fifteen significant works in the domain. Furthermore, we identify critical gaps such as scalability to large institutions, integration with cloud-based platforms, explainability of AI-driven decisions, and handling of sudden disruptions in academic calendars.

The paper concludes by discussing future research directions, including real-time rescheduling, user-in-the-loop systems, and explainable AI for transparent decision-making. By consolidating recent advancements and open challenges, this survey provides a strong foundation for future studies and assists educational institutions in adopting intelligent, conflict-free, and scalable timetabling solutions.