The most important aspect of a genetic algorithm (GA) lies in the optimal solution found. The result obtained by a genetic algorithm can be evaluated according to the quality of this solution. It is important that this solution is optimal or close to optimal in relation to the defined performance criteria, usually the fitness value. This study addresses the problem of automated generation of assessment tests in education. In this paper, we present the design of a model of assessment test generation used in education using genetic algorithms. The assessment covers a series of courses taught over a period of time. The genetic algorithm presents an improvement or development, which consists of the initial population variation, obtained by the selection of a large fixed number of individuals from various populations, which are ordered by the fitness value using merge sort, chosen for the reason of the high number of individuals. The initial population variation can be seen as a specific modality for increasing the diversity and number of the initial population of a genetic algorithm, which influences the algorithm performance. This process increases the diversity and quality of the initial population, improving the algorithm’s overall performance. The development/novelty brought about by this paper is related to its application to a specific issue (educational assessment test generation) and the specific methodology used for population variation. This development can be applied for large sets of individuals, the variety, and the large number of generated individuals leading to higher odds to increase the performance of the algorithm. Experimental results demonstrate that the proposed method outperforms traditional GA implementations in terms of solution quality and convergence speed, showing its effectiveness for large-scale test generation tasks.

The educational assessment is an essential task within the educational process. The generation of right and correct assessment content is a determinant process within the assessment. The creation of an automated method of generation similar to a human experienced operator (teacher) deals with a complex series of issues. This paper presents a compiled set of methods and tools used to generate educational assessment content in the form of assessment tests. The methods include the usage of various structures (e.g., trees, chromosomes and genes, and genetic operators) and algorithms (graph-based, evolutionary, and genetic) in the automated generation of educational assessment tests. This main purpose of the research is developed in the context of the existence of several requirements (e.g., degree of difficulty, item topic), which gives a higher degree of complexity to the issue. The paper presents a short literature review related to the issue. Next, the description of the models generated in the authors’ previous research is presented. In the final part of the paper, the results related to the implementations of the models are presented, as well as results and performance. Several conclusions were drawn based on this compilation, the most important of them being that tree and genetic-based approaches to the issue have promising results related to performance and assessment content generation.