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Uses of artificial intelligence and machine learning in systematic reviews of education research.

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Title: Uses of artificial intelligence and machine learning in systematic reviews of education research.
Authors: Karlstrøm, Henrik1 (AUTHOR) henrik.karlstrom@nifu.no
Source: London Review of Education. 2024, Vol. 22 Issue 1, p1-12. 12p.
Subject Terms: *Artificial intelligence, *Machine learning, *Information retrieval, *Education research, Science publishing
Abstract: The speed and volume of scientific publishing is accelerating, both in terms of number of authors and in terms of the number of publications by each author. At the same time, the demand for knowledge synthesis and dissemination is increasing in times of upheaval in the education sector. For systematic reviewers in the field of education, this poses a challenge in the balance between not excluding too many possibly relevant studies and handling increasingly large corpora that result from document retrieval. Efforts to manually summarise and synthesise knowledge within or across domains are increasingly running into constraints on resources or scope, but questions about the coverage and quality of automated review procedures remain. This article makes the case for integrating computational text analysis into current review practices in education research. It presents a framework for incorporating computational techniques for automated content analysis at various stages in the traditional workflow of systematic reviews, in order to increase their scope or improve validity. At the same time, it warns against naively using models that can be complex to understand and to implement without devoting enough resources to implementation and validation steps. [ABSTRACT FROM AUTHOR]
Copyright of London Review of Education is the property of UCL Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Uses of artificial intelligence and machine learning in systematic reviews of education research.
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  Data: <searchLink fieldCode="JN" term="%22London+Review+of+Education%22">London Review of Education</searchLink>. 2024, Vol. 22 Issue 1, p1-12. 12p.
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  Data: *<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br />*<searchLink fieldCode="DE" term="%22Education+research%22">Education research</searchLink><br /><searchLink fieldCode="DE" term="%22Science+publishing%22">Science publishing</searchLink>
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  Data: The speed and volume of scientific publishing is accelerating, both in terms of number of authors and in terms of the number of publications by each author. At the same time, the demand for knowledge synthesis and dissemination is increasing in times of upheaval in the education sector. For systematic reviewers in the field of education, this poses a challenge in the balance between not excluding too many possibly relevant studies and handling increasingly large corpora that result from document retrieval. Efforts to manually summarise and synthesise knowledge within or across domains are increasingly running into constraints on resources or scope, but questions about the coverage and quality of automated review procedures remain. This article makes the case for integrating computational text analysis into current review practices in education research. It presents a framework for incorporating computational techniques for automated content analysis at various stages in the traditional workflow of systematic reviews, in order to increase their scope or improve validity. At the same time, it warns against naively using models that can be complex to understand and to implement without devoting enough resources to implementation and validation steps. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of London Review of Education is the property of UCL Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.14324/LRE.22.1.40
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      – Code: eng
        Text: English
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      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Information retrieval
        Type: general
      – SubjectFull: Education research
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      – SubjectFull: Science publishing
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              Text: 2024
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