Abstract
Personalized and adaptive learning is one of the key directions in digital pedagogy and is highly important in teaching computer science to gifted students. The aim of this systematic review is to analyze adaptive and personalized approaches to teaching computer science and to assess their effect on the formation of key competencies and academic performance of high-performing learners. Following the PICOC (Population, Intervention, Comparison, Outcome, Context) framework, the target population of this study is gifted students. The intervention is the use of adaptive and personalized methods in teaching computer science, in comparison with traditional instructional methods. The outcomes of the analysis include the development of key competencies and the improvements of academic performance.
The systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol and was based on studies published between 2014 and 2025 in academic databases such as Scopus, Web of Science, ERIC (Education Resources Information Center), SpringerLink, and ScienceDirect. The final review included studies assessing the effectiveness of adaptive digital platforms, personalized learning trajectories, intelligent tutoring systems, and learning analytics tools in computer science education.
The outcomes of the study indicate that personalized and adaptive instructional methods in teaching computer science result in the advancement of algorithmic and critical thinking. It can also be noted that these approaches contribute to the development of student independence and that gifted students demonstrate improved academic performance. However, the research has some limitations, such as fragmented studies, a lack of standard evaluation methods, and insufficient data from developing regions. This indicates that more research is needed on adaptive technologies and on fully integrating personalized teaching methods into computer science education.