A Comprehensive Survey on AI-enabled Timetable Scheduling for Multidisciplinary Education under NEP 2020
Keywords:
Artificial intelligence, Constraint satisfaction, Intelligent scheduling logic, Scheduling system, Timetable generationAbstract
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.
References
B. Manuel and V. Mohan, “Timetable generator using genetic algorithm and constraint satisfaction problem,” International Journal for Multidisciplinary Research, vol. 7, no. 5, Sep.–Oct. 2025.
S. K. Saw, S. Lawrence, S. Karunakara, N. H. Komal, and V. K. G., “Adaptive scheduler: AI optimization of academic timetable,” International Journal of Scientific Development and Research, vol. 10, no. 4, Apr. 2025.
A. Bashab et al., “Optimization techniques in university timetabling problem: Constraints, methodologies, benchmarks, and open issues,” Computers, Materials & Continua, vol. 74, no. 3, pp. 6461–6484, 2023.
N. P. Rana, S. Chatterjee, Y. K. Dwivedi, and S. Akter, “Understanding dark side of artificial intelligence (AI) integrated business analytics: Assessing firm’s operational inefficiency and competitiveness,” European Journal of Information Systems, vol. 31, no. 3, pp. 364–387, Aug. 2021.
G. Sharma, “Harnessing the power of artificial intelligence and machine learning for optimized cloud resource management and intelligent automation,” International Journal of Engineering and Technique, vol. 8, no. 3, May 2022.
A. H. Khan and T. Imtiaz, “A novel genetic algorithm based timetable generator for optimized university timetable solution,” 2024 International Conference on Engineering & Computing Technologies (ICECT), Islamabad, Pakistan, 2024, pp. 1–6.
Y. Chen, Q. Liu, and D. Tong, “Exploration of artificial intelligence-based intelligent course scheduling and resource optimization technology in higher education,” Proceedings of the 2025 International Conference on Artificial Intelligence and Educational Systems, pp. 30–35, Apr. 2025.
P. Li, Y. Xiao, J. Yan, X. Li and X. Wang, “Reinforcement learning for adaptive resource scheduling in complex system environments,” 2024 5th International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), Wuhan, China, 2024, pp. 92–98.
C. Zhang, M. Juraschek, and C. Herrmann, “Deep reinforcement learning-based dynamic scheduling for resilient and sustainable manufacturing: A systematic review,” Journal of Manufacturing Systems, vol. 77, pp. 962–989, Dec. 2024.
A. Deokar, S. Mane, A. Aadhav, M. Narudkar, “Smart timetable planner with AI suggestions,” International Research Journal of Modernization in Engineering Technology and Science, vol. 8, no. 3, pp. 1015–1019, Mar. 2026.
H. Techie-Menson and P. Nyagorme, “Design and implementation of a web-based timetable system for higher education institutions,” International Journal of Educational Research and Information Science, vol. 7, no. 1, pp. 1–13, Mar. 2021.
M. Barone, M. Naeem, M. Ciaschi, G. Tretola, and A. Coronato, “AI-based intelligent system for personalized examination scheduling,” Technologies, vol. 13, no. 11, p. 518, Nov. 2025.
R.-W. Bello and J. A. Godwill, “Hybrid technologies and genetic algorithms applied to school lecture timetable generation,” World Scientific News, vol. 189, pp. 288–310, Jan. 2024.
S. Daskalaki and T. Birbas, “Efficient solutions for a university timetabling problem through integer programming,” European Journal of Operational Research, vol. 160, no. 1, pp. 106–120, Jan. 2005.
S. S. Sonawane, R. Belitkar, A. Jambhulkar, and R. Yadav, “Schedule academic time table using AI and ML,” International Journal of Research Publication and Reviews, vol. 5, no. 4, pp. 199–210, Apr. 2024.
P. Shinde, S. Patil, M. Potdar, M. Shital, and S. Gavade, “Ai powered automatic timetable generator,” International Journal of Creative Research Thoughts, vol. 13, no. 6, pp. 2320–2882, Jun. 2025.
N. R. Deepak and S. Balaji, “Uplink channel performance and implementation of software for image communication in 4G network,” Advances in Intelligent Systems and Computing, pp. 105–115, Apr. 2016.
D. N. R and S. Balaji, “A review of techniques used in EPS and 4G-LTE in mobility schemes,” International Journal of Computer Applications, vol. 109, no. 9, pp. 30–38, Jan. 2015.
