The increasing need for personalization in the assessment process, focusing on individual learners’ characteristics, has captured researchers’ attention more than ever in today’s world. By employing personalized measurement, learners encounter assessments specifically designed to address their needs, providing an opportunity to enhance their abilities in areas where they may initially face challenges. This study aimed to elucidate the theoretical and research foundations of personalized assessment using semantic web capabilities. The qualitative research was conducted through an inductive approach, involving the following steps: a) problem formulation, b) data collection, c) evaluation of data appropriateness, d) data analysis and interpretation, and e) organization and presentation of results based on the study selection process. Ultimately, 30 articles were selected for analysis, leading to a general framework for designing a system and a conceptual model for a semantically web-based personalized assessment scenario. The findings showed that the use of semantic web rules can be effective in solving the challenge of personalization in assessment. So that by identifying the learning gaps of each learner, using the visual form of feedback, determining the path of personal learning in an interactive environment and participating in groups classified according to their similarities, guiding learners to achieve learning goals and improve sustainable performance. To do, On the other hand, the level of access and support of instructors and data security are challenges for teachers and learners.