Contemporary Research Analysis Journal

School calendar planning is a constraint-intensive management task involving national regulations, instructional-time requirements, teacher and room availability, assessment periods, extracurricular activities, meetings, holidays, local disruptions, and equity considerations. This structured integrative review examines the potential contribution of generative artificial intelligence (GenAI) to this task, with particular attention to secondary-school principals. Eight search strands covering GenAI and school leadership, educational administration, timetabling optimization, decision support, resource allocation, administrative workload, and resource-constrained settings were used to build an analytical corpus of 85 publications from an initial pool of 160 records. Evidence was coded by type, educational context, planning function, expected or observed benefit, risk, and proximity to annual or term-level school calendar planning. The review reveals a marked asymmetry: educational timetabling optimization is technically mature, whereas direct evidence on GenAI-assisted annual school calendar planning remains scarce. Recent K–12 leadership studies nevertheless support the plausibility of gains in document preparation, information synthesis, communication, scenario generation, and delegation of routine administrative tasks. The evidence also indicates that large language models should not be used as autonomous constraint solvers because they may omit rules, hallucinate availabilities, or produce superficially coherent but infeasible schedules. We therefore propose a hybrid GenAI-Assisted School Calendar Planning model (GASCP), combining conversational GenAI for constraint elicitation and explanation, a formal optimization engine for feasibility and trade-off management, and accountable human validation. The model generates testable hypotheses concerning planning time, feasibility, pedagogical quality, equity, robustness, acceptability, traceability, and principals’ cognitive workload. The central conclusion is that GenAI is most defensible as an interface for responsible planning rather than as an autonomous decision-maker.

generative artificial intelligence, school calendar, school principal, secondary education, timetabling, decision support.

1. Abdipoor, S., Yaakob, R., Goh, S. L., & Abdullah, S. (2023). Meta-heuristic approaches for the University Course Timetabling Problem. Intelligent Systems with Applications, 19, 200253. https://doi.org/10.1016/j.iswa.2023.200253
2. Adams, D., & Thompson, P. (2025). Transforming school leadership with artificial intelligence: Applications, implications, and future directions. Leadership and Policy in Schools, 24(1), 77–89. https://doi.org/10.1080/15700763.2024.2411295
3. Adediran, I. A., Ottan, O. J., Omuya, S. O., Adediran, B. O., & Ogunsola, J. A. (2026). Awareness and utilisation of artificial intelligence tools for effective administration in public secondary schools in North-Central Nigeria. Journal of Issues and Practice in Education.
4. Ahmed, L. N., Özcan, E., & Kheiri, A. (2015). Solving high school timetabling problems worldwide using selection hyper-heuristics. Expert Systems with Applications, 42(13), 5463–5471. https://doi.org/10.1016/j.eswa.2015.02.059
5. Al-Yakoob, S. M., & Sherali, H. D. (2015). Mathematical models and algorithms for a high school timetabling problem. Computers & Operations Research, 61, 56–68. https://doi.org/10.1016/j.cor.2015.02.011
6. Arar, K., Tlili, A., & Salha, S. (2024). Human-machine symbiosis in educational leadership in the era of artificial intelligence (AI): Where are we heading? Educational Management Administration & Leadership. https://doi.org/10.1177/17411432241292295
7. Arar, K., Tlili, A., Schunka, L., Salha, S., & Saiti, A. (2025). Reimagining educational leadership and management through artificial intelligence: An integrative systematic review. Leadership and Policy in Schools, 24(1), 4–26.https://doi.org/10.1080/15700763.2025.2451982
8. Babaei, H., Karimpour, J., & Hadidi, A. (2015). A survey of approaches for university course timetabling problem. Computers & Industrial Engineering, 86, 43–59. https://doi.org/10.1016/j.cie.2014.11.010
9. Badoni, R., et al. (2014). A graph edge colouring approach for school timetabling problems. International Journal of Mathematics in Operational Research, 6, 123–144. https://doi.org/10.1504/IJMOR.2014.057853
10. Bashab, A., Ibrahim, A. O., Hashem, I. A. T., Aggarwal, K., Mukhlif, F., Ghaleb, F. A., & Abdelmaboud, A. (2023). Optimization techniques in university timetabling problem: Constraints, methodologies, benchmarks, and open issues. Computers, Materials & Continua, 74(3), 6461–6484.
11. Berkovich, I. (2025). The rise of AI-assisted instructional leadership: Empirical survey of generative AI integration in school leadership and management work. Frontiers in Education, 10, 1643023. https://doi.org/10.3389/feduc.2025.1643023
12. Berkovich, I. (2026). Innovation culture in schools, school leaders’ openness to experience, and the integration of generative AI in school leadership work: A UTAUT based model. Journal of Research on Technology in Education. https://doi.org/10.1177/13654802261434283
13. Berkovich, I., & Eyal, O. (2025). Support for generative artificial intelligence as a predictor of middle leaders’ generative artificial intelligence self-efficacy, valuing, and integration in school leadership work. Educational Management Administration & Leadership. Advance online publication. https://doi.org/10.1177/17411432251361251
14. Berkovich, I., & Hassan, T. (2024). Principals’ digital instructional leadership during the pandemic: Impact on teachers’ intrinsic motivation and students’ learning. Educational Management Administration & Leadership, 52(4), 934–954. https://doi.org/10.1177/17411432221113411
15. Brito, S. S., et al. (2012). A SA-VNS approach for the high school timetabling problem. Electronic Notes in Discrete Mathematics, 39, 169–176. https://doi.org/10.1016/j.endm.2012.10.023
16. Ceschia, S., Di Gaspero, L., & Schaerf, A. (2023). Educational timetabling: Problems, benchmarks, and state-of-the-art results. European Journal of Operational Research, 308(1), 1–18. https://doi.org/10.1016/j.ejor.2022.07.011
17. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
18. Chen, M. C., Goh, S. L., Sabar, N. R., & Kendall, G. (2021). A survey of university course timetabling problem: Perspectives, trends and opportunities. IEEE Access, 9, 106515–106529.
19. Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.1016/j.caeai.2022.100118
20. Creagh, S., Thompson, G., Mockler, N., Stacey, M., & Hogan, A. (2023). Workload, work intensification and time poverty for teachers and school leaders: A systematic research synthesis. Educational Review. https://doi.org/10.1080/00131911.2023.2196607
21. Dai, R., Thomas, M. K. E., & Rawolle, S. (2025). The roles of AI and educational leaders in AI-assisted administrative decision-making: A proposed framework for symbiotic collaboration. The Australian Educational Researcher, 52, 1471–1487. https://doi.org/10.1007/s13384-024-00771-8
22. Datnow, A., Lockton, M., & Weddle, H. (2021). Capacity building to bridge data use and instructional improvement through evidence on student thinking. Studies in Educational Evaluation, 69, 100869. https://doi.org/10.1016/j.stueduc.2020.100869
23. Demirović, E., & Musliu, N. (2017). MaxSAT-based large neighborhood search for high school timetabling. Computers & Operations Research, 78, 172–180. https://doi.org/10.1016/j.cor.2016.08.004
24. Dorneles, Á. P., de Araújo, O. C. B., & Buriol, L. S. (2014). A fix-and-optimize heuristic for the high school timetabling problem. Computers & Operations Research, 52, 29–38. https://doi.org/10.1016/j.cor.2014.06.023
25. Dorneles, Á. P., de Araújo, O. C. B., & Buriol, L. S. (2017). A column generation approach to high school timetabling modeled as a multicommodity flow problem. European Journal of Operational Research, 256(3), 685–695. https://doi.org/10.1016/j.ejor.2016.07.002
26. Esmaeilbeigi, R., Mak-Hau, V., Yearwood, J., & Nguyen, V. (2022). The multiphase course timetabling problem. European Journal of Operational Research, 299(3), 1098–1119. https://doi.org/10.1016/j.ejor.2021.10.014
27. Flogie, A., Hari, D., Skrbinjek, V., & Brejc, M. (2026). Use and aspects of generative artificial intelligence in the educational system among leadership personnel. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/s10639-026-13986-2
28. Fonseca, G. H. G., & Santos, H. G. (2014). Variable neighborhood search based algorithms for high school timetabling. Computers & Operations Research, 52, 203–208. https://doi.org/10.1016/j.cor.2013.11.012
29. Fonseca, G. H. G., Santos, H. G., & Carrano, E. G. (2016). Integrating matheuristics and metaheuristics for timetabling. Computers & Operations Research, 74, 108–117. https://doi.org/10.1016/j.cor.2016.04.016
30. Fonseca, G. H. G., Santos, H. G., & Carrano, E. G. (2016). Late acceptance hill-climbing for high school timetabling. Journal of Scheduling, 19(4), 453–465. https://doi.org/10.1007/s10951-015-0458-5
31. Fonseca, G. H. G., Santos, H. G., Carrano, E. G., & Stidsen, T. J. R. (2017). Integer programming techniques for educational timetabling. European Journal of Operational Research, 262(1), 28–39. https://doi.org/10.1016/j.ejor.2017.03.020
32. Ghamrawi, N., Shal, T., & Ghamrawi, N. A. R. (2025). Effective school leadership enactment of GAI: A 5C’s framework for integration. Frontiers in Education, 10, 1561414. https://doi.org/10.3389/feduc.2025.1561414
33. Goh, S. L., et al. (2019). Simulated annealing with improved reheating and learning for the post enrolment course timetabling problem. Journal of the Operational Research Society, 70, 873–888. https://doi.org/10.1080/01605682.2018.1468862
34. Idu, A. D. (2025). Utilization and challenges of artificial intelligence in secondary school administration in Bayelsa State, Nigeria. Journal article deposited in Zenodo. https://zenodo.org/records/17114355
35. Karakose, T., & Tülübaş, T. (2024). School leadership and management in the age of artificial intelligence (AI): Recent developments and future prospects. Educational Process: International Journal, 13(1), 7–14. https://doi.org/10.22521/edupij.2024.131.1
36. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
37. Katsaragakis, I., et al. (2015). A comparative study of modern heuristics on the school timetabling problem. Algorithms, 8, 723–742. https://doi.org/10.3390/a8030723
38. Kaufman, J. H., Woo, A., Eagan, J., Lee, S., & Kassan, E. B. (2025). Uneven adoption of artificial intelligence tools among U.S. teachers and principals in the 2023–2024 school year (RR-A134-25). RAND Corporation.
39. Kesim, E., Atmaca, T., & Turan, S. (2025). Reshaping school cultures: AI’s influence on organizational dynamics and leadership behaviors. Leadership and Policy in Schools, 24(1), 117–136.
40. Kheiri, A., & Keedwell, E. (2017). A hidden Markov model approach to the problem of heuristic selection in hyper-heuristics with a case study in high school timetabling problems. Evolutionary Computation, 25(3), 473–501. https://doi.org/10.1162/evco_a_00186
41. Kheiri, A., Özcan, E., & Parkes, A. J. (2016). A stochastic local search algorithm with adaptive acceptance for high-school timetabling. Annals of Operations Research, 239, 135–151. https://doi.org/10.1007/s10479-014-1660-0
42. Kristiansen, S., & Stidsen, T. R. (2015). Integer programming for the generalized high school timetabling problem. Journal of Scheduling, 18, 377–392. https://doi.org/10.1007/s10951-014-0405-x
43. Kumar, R., & Sarkar, S. (2026). Artificial intelligence in secondary schools: Implications for administrators across four leadership dimensions. Artificial Intelligence in Education, 2(3), 33–49. https://doi.org/10.1108/AIIE-08-2025-0238
44. Leite, N., et al. (2019). A fast simulated annealing algorithm for the examination timetabling problem. Expert Systems with Applications, 122, 137–151. https://doi.org/10.1016/j.eswa.2018.12.048
45. Lewis, R. (2008). A survey of metaheuristic-based techniques for university timetabling problems. OR Spectrum, 30, 167–190. https://doi.org/10.1007/s00291-007-0097-0
46. Lispou, E., Keravnos, N., & Eteokleous, N. (2026). Artificial intelligence in school leadership: A structured literature review of organisational benefits and ethical challenges. Artificial Intelligence in Education, 2(3), 85–100. https://doi.org/10.1108/AIIE-08-2025-0240
47. Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410
48. Mallik, S., & Gangopadhyay, A. (2023). Proactive and reactive engagement of artificial intelligence methods for education: A review. Frontiers in Artificial Intelligence, 6, 1151391. https://doi.org/10.3389/frai.2023.1151391
49. Mbonu, V. C., Okonkwo, E. C., & Obasi, H. O. (2026). Application of artificial intelligence powered tools for principals’ management of secondary schools in Anambra State. Faculty of Education Journals, Nnamdi Azikiwe University.
50. Mifsud, D., Campbell, P., & Niesche, R. (2026). School leadership and artificial intelligence: A framework for critical and reflexive engagement and enactment. Educational Management Administration & Leadership. Advance online publication. https://doi.org/10.1177/08920206261475462
51. Mifsud, D., Campbell, P., & Niesche, R. (2026). The impact of AI on school leadership practices and policy enactment: A scoping literature review. Leadership and Policy in Schools. Advance online publication. https://doi.org/10.1080/15700763.2026.2679510
52. Muklason, A., Pratama, E. J., & Premananda, I. G. A. (2024). Generic university examination timetabling system with steepest-ascent hill climbing hyper-heuristic algorithm. Procedia Computer Science, 234, 584–591. https://doi.org/10.1016/j.procs.2024.03.043
53. Okeke, N. L., Chime, G. O., Chukwu, N. W., & Nnonyelu, O. G. (2025). Artificial intelligence and school principals’ management efficiency in public secondary schools in Awka South Local Government Area, Anambra State. UNIZIK Journal of Educational Laws and Leadership Studies, 2(1).
54. Pillay, N. (2014). A survey of school timetabling research. Annals of Operations Research, 218, 261–293. https://doi.org/10.1007/s10479-013-1321-8
55. Post, G., et al. (2016). The third international timetabling competition. Annals of Operations Research, 239, 69–75. https://doi.org/10.1007/s10479-013-1340-5
56. Post, G., Kingston, J. H., Ahmadi, S., Daskalaki, S., Gogos, C., Kyngas, J., Nurmi, C., Musliu, N., Pillay, N., Santos, H., & Schaerf, A. (2014). XHSTT: An XML archive for high school timetabling problems in different countries. Annals of Operations Research, 218, 295–301. https://doi.org/10.1007/s10479-011-1012-2
57. Prasad, A., & Sahu, M. (2025). The integration of artificial intelligence in principal leadership: A systematic literature review of trends, tools, and impacts. International Journal of Engineering Technologies and Management Research, 12(6). https://doi.org/10.29121/ijetmr.v12.i6.2025.1622
58. Qu, R., Burke, E. K., McCollum, B., Merlot, L. T. G., & Lee, S. Y. (2009). A survey of search methodologies and automated system development for examination timetabling. Journal of Scheduling, 12, 55–89. https://doi.org/10.1007/s10951-008-0077-5
59. Raghavjee, R., & Pillay, N. (2015). A genetic algorithm selection perturbative hyper-heuristic for solving the school timetabling problem. ORiON, 31(1), 39–60. https://doi.org/10.5784/31-1-158
60. Richardson, J. W., Vedder, B. C., Roberts, A. B., & McLeod, S. (2025). What’s the chatter about AI and school leaders? Leadership and Policy in Schools, 24(1), 103–116.
61. Sabar, N. R., et al. (2013). Grammatical evolution hyper-heuristic for combinatorial optimization problems. IEEE Transactions on Evolutionary Computation, 17, 840–861. https://doi.org/10.1109/TEVC.2013.2281527
62. Saviniec, L., Santos, M. O., & Costa, A. M. (2017). Effective local search algorithms for high school timetabling problems. Applied Soft Computing, 60, 363–373. https://doi.org/10.1016/j.asoc.2017.06.047
63. Saviniec, L., Santos, M. O., & Costa, A. M. (2018). Parallel local search algorithms for high school timetabling problems. European Journal of Operational Research, 265(1), 81–98. https://doi.org/10.1016/j.ejor.2017.07.029
64. Saviniec, L., Santos, M. O., Costa, A. M., & dos Santos, L. M. R. (2020). Pattern-based models and a cooperative parallel metaheuristic for high school timetabling problems. European Journal of Operational Research, 280(3), 1064–1081. https://doi.org/10.1016/j.ejor.2019.08.001
65. Schaerf, A. (1999). A survey of automated timetabling. Artificial Intelligence Review, 13, 87–127. https://doi.org/10.1023/A:1006576209967
66. Schildkamp, K. (2019). Data-based decision-making for school improvement: Research insights and gaps. Educational Research, 61(3), 257–273. https://doi.org/10.1080/00131881.2019.1625716
67. Skedsmo, G., & Huber, S. G. (2022). Data-driven approaches to education governance and their implications. Educational Assessment, Evaluation and Accountability, 34, 1–4. https://doi.org/10.1007/s11092-022-09383-1
68. Skoullis, V. I., Tassopoulos, I. X., & Beligiannis, G. N. (2017). Solving the high school timetabling problem using a hybrid cat swarm optimization based algorithm. Applied Soft Computing, 52, 277–289. https://doi.org/10.1016/j.asoc.2016.10.038
69. Solano Cutillas, P., Pérez-Perales, D., & Alemany Díaz, M. M. E. (2021). A mathematical programming tool for an efficient decision-making on teaching assignment under non-regular time schedules. Operational Research, 23, 2899–2942. https://doi.org/10.1007/s12351-021-00638-1
70. Sposato, M. (2025). Artificial intelligence in educational leadership: A comprehensive taxonomy and future directions. International Journal of Educational Technology in Higher Education, 22, 20. https://doi.org/10.1186/s41239-025-00517-1
71. Sterrett, W. L., Richardson, J. W., & Frey, M. A. (2026). Perceptions of how AI has changed teaching, learning, and leading in K–12 schools: Insights from award-winning principals. Frontiers in Education, 11, 1854863. https://doi.org/10.3389/feduc.2026.1854863
72. Sun, Z., & Wu, Q. (2023). Two-phase tabu search algorithm for solving Chinese high school timetabling problems under the new college entrance examination reform. Data Science and Management, 6(1), 55–63. https://doi.org/10.1016/j.dsm.2023.02.001
73. Sørensen, M., & Dahms, F. H. W. (2014). A two-stage decomposition of high school timetabling applied to cases in Denmark. Computers & Operations Research, 43, 36–49. https://doi.org/10.1016/j.cor.2013.08.025
74. Tan, J. S., Goh, S. L., Kendall, G., & Sabar, N. R. (2021). A survey of the state-of-the-art of optimisation methodologies in school timetabling problems. Expert Systems with Applications, 165, 113943. https://doi.org/10.1016/j.eswa.2020.113943
75. Tassopoulos, I. X., & Beligiannis, G. N. (2012). A hybrid particle swarm optimization based algorithm for high school timetabling problems. Applied Soft Computing, 12(11), 3472–3489. https://doi.org/10.1016/j.asoc.2012.05.029
76. Tassopoulos, I. X., & Beligiannis, G. N. (2012). Solving effectively the school timetabling problem using particle swarm optimization. Expert Systems with Applications, 39(5), 6029–6040. https://doi.org/10.1016/j.eswa.2011.12.013
77. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10, 15. https://doi.org/10.1186/s40561-023-00237-x
78. Tyson, M. M., & Sauers, N. J. (2021). School leaders’ adoption and implementation of artificial intelligence. Journal of Educational Administration, 59(3), 271–285. https://doi.org/10.1108/JEA-10-2020-0221
79. Wang, Y. (2021). Artificial intelligence in educational leadership: A symbiotic role of human-artificial intelligence decision-making. Journal of Educational Administration, 59(3), 256–270. https://doi.org/10.1108/JEA-10-2020-0216
80. Wang, Y. (2021). When artificial intelligence meets educational leaders’ data-informed decision-making: A cautionary tale. Studies in Educational Evaluation, 69, 100872. https://doi.org/10.1016/j.stueduc.2020.100872
81. Woumans, G., et al. (2016). A column generation approach for solving the examination-timetabling problem. European Journal of Operational Research, 253, 178–194. https://doi.org/10.1016/j.ejor.2016.01.046
82. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—where are the educators? International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0
83. Zeng, M., Cheah, K. S. L., & Abdullah, Z. (2025). The influence of school principals’ digital leadership on teachers’ competency in integrating artificial intelligence: A systematic thematic review. Frontiers in Education, 10, 1655967. https://doi.org/10.3389/feduc.2025.1655967
84. Zhang, D., Liu, Y., M’Hallah, R., & Leung, S. C. H. (2010). A simulated annealing with a new neighborhood structure based algorithm for high school timetabling problems. European Journal of Operational Research, 203(3), 550–558. https://doi.org/10.1016/j.ejor.2009.09.014
85. Zhu, K., Li, L. D., & Li, M. (2022). School timetabling optimisation using artificial bee colony algorithm based on a virtual searching space method. Mathematics, 10(1), 73. https://doi.org/10.3390/math10010073