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Related Experiment Video

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Machine learning-assisted abstract screening on learning analytics: a step-by-step tutorial.

Zhihong Xu1, Shuai Ma2, Xiting Zhuang3

  • 1Department of Agricultural Leadership, Education, and Communications, Texas A&M University, College Station, USA. xuzhihong@tamu.edu.

Systematic Reviews
|February 20, 2026
PubMed
Summary

Machine learning (ML) tools like ASReview and ChatGPT can significantly improve systematic review abstract screening. This tutorial guides researchers in using these technologies to enhance efficiency and accuracy in evidence synthesis.

Keywords:
Abstract screeningMachine learningSystematic reviewTutorial

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Area of Science:

  • Bibliometrics
  • Information Science
  • Computer Science

Background:

  • Systematic reviews are vital for evidence synthesis but face challenges with manual abstract screening, which is time-consuming and error-prone.
  • Machine learning (ML) presents a promising avenue for automating and refining the abstract screening process in systematic reviews.
  • The increasing volume of scientific literature necessitates efficient and accurate methods for evidence synthesis.

Purpose of the Study:

  • To provide a practical, step-by-step tutorial for implementing two ML tools, ASReview and ChatGPT, to streamline abstract screening in systematic reviews.
  • To demonstrate the application of active learning (ASReview) and large language models (ChatGPT) for enhancing the efficiency and accuracy of evidence synthesis.
  • To evaluate the performance of ASReview and ChatGPT using key metrics like sensitivity, specificity, and accuracy.

Main Methods:

  • A case study focusing on a learning analytics (LA) in higher education review was used to illustrate the implementation.
  • Detailed instructions were provided for data preparation and setup of ASReview, an active learning-based ML framework.
  • Guidance was offered on optimizing prompts and parameters for ChatGPT (GPT-4) within a Python Google Colab environment for consistent screening.

Main Results:

  • ASReview demonstrated effectiveness in reducing manual workload and maintaining high recall rates, particularly for large datasets.
  • ChatGPT, with optimized prompts, showed potential for enhancing screening precision and consistency.
  • Performance metrics (sensitivity, specificity, accuracy) were presented to highlight the distinct strengths and limitations of each tool.

Conclusions:

  • ASReview and ChatGPT offer viable ML solutions to improve the efficiency and accuracy of abstract screening in systematic reviews.
  • Researchers can leverage these tools to manage the growing complexity of evidence synthesis, ensuring rigor and transparency.
  • This tutorial empowers researchers to integrate ML into their systematic review workflows, optimizing the evidence synthesis process.