Abstract. In the context of rapid changes in the labor market and the increasing availability of educational data, the development of artificial intelligence (AI)-based career recommendation systems has become an important research direction. However, the influence of data types and algorithm selection on the predictive accuracy and computational efficiency of such systems remains insufficiently systematized. The aim of this study is to systematize current research on AI-driven career recommendation systems and to identify relationships among data types, algorithms, and evaluation metrics. The study is based on a systematic literature review conducted in accordance with the PRISMA 2020 guidelines. The analysis was performed within the “data type–algorithm–evaluation metric” framework. The results show that classification tasks dominate the reviewed studies (56%), while structured tabular data represent the most frequently used data type (76%). The highest predictive performance was achieved primarily by ensemble machine learning methods and neural network models. At the same time, a significant gap was identified regarding the assessment of computational efficiency, as 76% of the analyzed studies did not report relevant computational performance measures. Furthermore, the findings indicate that algorithm selection is strongly associated with the characteristics of the underlying data. The study contributes to the systematization of algorithmic approaches used in career recommendation systems and highlights the need for standardized evaluation protocols, systematic reporting of computational efficiency, and broader adoption of multimodal data in future research. In addition, the development of AI-driven recommendation systems tailored to the specific characteristics of national labor markets and educational environments represents an important direction for future studies.

Keywords: artificial intelligence, career recommendations, recommender systems, data types, machine learning algorithms, predictive accuracy, computational efficiency.