This paper addresses the problem of finding a feasible solution for the university course timetabling problem (UCTP), i.e. a solution that satisfies all the so-called hard constraints. The problem is reformulated through relaxing one of its hard constraints and then creating a soft constraint to address the relaxed constraint. The relaxed problem is solved in two steps. First, a graph-based heuristic is used to construct a feasible solution of the relaxed problem, and then, a simulated annealing (SA)-based approach is utilized to minimize the violation of the soft constraint. In order to strengthen the diversification ability of the method in the SA phase, a heuristic based on Kempe chain neighborhood is embedded into the standard approach. This strategy is tested on a well-known data set, and the results are very competitive compared to the current state of the art of the UCTP.

Generative Artificial Intelligence (AI) applications that have recently emerged have now experienced very rapid development. The ability to think like humans is the advantage of these applications such as processing and producing text, images and even videos has opened up very broad opportunities in various fields. One of them is the use of generative AI in the world of education, currently students, especially students in the Jabodetabek area, have utilized the capabilities of generative AI applications to help complete assignments, then what factors cause these students to use generative AI applications and whether these students will plan to continue using generative AI. This study will answer these questions using the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Delone & McLean information system success model, and identify what factors influence the use of generative AI by analyzing variables such as performance expectancy, effort expectancy, social influence, and technology self-efficacy, system quality, information quality, digital literacy. The results of the study showed that the eight independent variables included in the research model influenced the intention to continue using it. Among the eight independent variables, the variables of performance expectations, system quality, and the efficacy of the technology itself are the biggest factors influencing the intention to continue use.

In general, a university examination timetabling problem concerns with the task of matching a set of exams to resources (timeslots and rooms) in such a way that meets some predefined requirements. Majority of the examination timetabling problems are NP-complete. This paper addresses the so-called graph coloring reduction of examination timetabling problem which is to find a feasible examination timetable with minimum timeslots used. The report contains an investigation on the implementation of a heuristic hybridized with a simulated annealing-based heuristic using three different neighborhood structures. The method is tested on a popular dataset, and compare the results with several other authors. The method is capable of finding the best solutions ever reported as well as improving current best known results.

Any educational timetabling problem addresses the task of assigning a set of courses or exams to timeslots and rooms taking into account a set of constraints. Two types of constraints are usually imposed to the task (hard-compulsory and soft-non compulsory). In this paper, we address the feasibility problem, where the main concern is to find a timetable without any hard constraint violation. We propose some hill climbing-based local searches using what we call single and perturbation moves. The method is tested on a set of difficult instances dedicated for courses timetabling problem. The results are compared to other authors and it is shown to be capable to find the best solution found so far and improve the state-of-the art results for some instances.