Knowledge Resource
Research Summary: AI-assisted data extraction for systematic reviews in education
- Original authors
- Attribution requires verification
- Original source
- Educational Technology Research and Development
- Summary & Analysis prepared by
- Aziz Shuaib Ausi
- Resource type
- Research Summary / Knowledge Resource
- Resource published on AZIZ OS
- 18 September 2026
- Last updated
- 22 September 2026
- Reading time
- 1 min
- Publication type
- Knowledge Resource
- Availability
- Open access
About this Summary & Analysis
AZIZ OS provides independently prepared summaries and analytical interpretations of externally published research and knowledge sources. The underlying works remain attributable to their original authors and rights holders. This resource is intended to improve accessibility and understanding and does not replace the original publication.
The application of Large Language Models (LLMs) shows significant potential to streamline and accelerate the data extraction phase of systematic reviews, particularly within the field of education. Current research indicates that LLMs can effectively extract various data types from studies, offering a path to reduce the extensive human effort traditionally required for these processes. This development is being supported by empirical studies and the iterative creation of open-source software tools.
Why it matters
This research addresses the inefficiency inherent in systematic review methodologies by leveraging emerging AI technologies. Accelerating the data extraction phase can significantly reduce the time and resources required to synthesize knowledge, thereby enabling faster insights and more responsive evidence-based decision-making across various domains. It also fosters the development of open-source tools, promoting wider accessibility and innovation.
Key insights
- Systematic reviews are labor-intensive, requiring significant human expertise for study screening and data extraction.
- Large Language Models (LLMs) are identified as a promising technology to accelerate the systematic review process and decrease reviewer workload.
- The specific application of LLMs for systematic reviews in education has been underexplored.
- Empirical studies are underway to assess the efficacy of LLMs for data extraction in educational systematic reviews.
- An open-source software tool is under iterative development to facilitate LLM-assisted data extraction.
- Initial empirical work has focused on extracting diverse data types from studies related to pedagogical agents.
Source
Educational Technology Research and Development — https://link.springer.com/article/10.1007/s11423-026-10710-2
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Verification
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- Verification ID
- ASA-EXE-2026-00696
- Version
- v1.0 · r0
- Issued
- 18 September 2026
- Resource prepared by
- Aziz Shuaib Ausi
- Resource status
- Research Summary / Knowledge Resource
- Underlying work
- AI-assisted data extraction for systematic reviews in education
- Original authors
- Attribution requires verification
- Original source
- Educational Technology Research and Development
- Provenance status
- Attribution requires verification
- Rights
- Underlying publication rights remain with the respective copyright holder(s). Refer to the original source for authoritative publication and licensing information.
This verification confirms the AZIZ OS resource record and its documented provenance. It does not establish authorship of the underlying external work.