<![CDATA[This study aims to identify and analyze instructional strategies for managing students' cognitive load in science learning within the Cognitive Load Theory (CLT) framework. A systematic literature review was conducted following PRISMA 2020 guidelines, searching the Google Scholar and Scopus databases via Publish or Perish software, with the last search conducted on June 12, 2026, covering publications from 2016 to 2026. Included articles were empirical studies in peer-reviewed journals indexed in Scopus, Sinta, DOAJ, or equivalent local peer-reviewed outlets, focusing on the application of instructional strategies to manage cognitive load among primary and secondary school students. A total of 21 articles were selected from an initial pool of 95, through a four-stage screening process involving deduplication, title and abstract screening, and full-text eligibility assessment. Data were synthesized through descriptive, content, and critical analysis to identify trends across studies. The analysis focused on the types of instructional strategies employed and their implications for managing intrinsic cognitive load (ICL), extraneous cognitive load (ECL), and germane cognitive load (GCL). The findings revealed three main trends. First, technology and digital media were widely adopted to reduce ECL by presenting information in a more structured, multimodal format. Second, active learning models, such as problem-based learning and generative approaches, enhanced GCL by fostering deeper cognitive engagement. Third, instructional designs that explicitly integrate CLT principles, such as chunking, pacing, and the 4C/ID model, demonstrated the most comprehensive potential for managing students' cognitive load. These findings affirm the importance of instructional design aligned with students' working memory capacity to enhance the effectiveness of science learning. However, the absence of a formal risk-of-bias assessment represents a methodological limitation that should be considered when interpreting these findings.]]>
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