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Cited by 361Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Healthcare and Education

Exploring the Impact of Artificial Intelligence in Teaching and Learning of Science: A Systematic Review of Empirical Research

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Almasri, Firas·2024·Research in Science Education
DOI 10.1007/s11165-024-10176-3
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Abstract

The use of Artificial Intelligence (AI) in education is transforming various dimensions of the education system, such as instructional practices, assessment strategies, and administrative processes. It also plays an active role in the progression of science education. This systematic review attempts to render an inherent understanding of the evidence-based interaction between AI and science education. Specifically, this study offers a consolidated analysis of AI’s impact on students’ learning outcomes, contexts of its adoption, students’ and teachers’ perceptions about its use, and the challenges of its use within science education. The present study followed the PRISMA guidelines to review empirical papers published from 2014 to 2023. In total, 74 records met the eligibility for this systematic study. Previous research provides evidence of AI integration into a variety of fields in physical and natural sciences in many countries across the globe. The results revealed that AI-powered tools are integrated into science education to achieve various pedagogical benefits, including enhancing the learning environment, creating quizzes, assessing students’ work, and predicting their academic performance. The findings from this paper have implications for teachers, educational administrators, and policymakers.

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  • The use of GenAI in educational settings has the potential to enhance learning by incorporating personalized assistance, thereby improving students' research skills.

    “The use of GenAI in educational settings has the potential to enhance learning by incorporating personalized assistance, thereby improving students' research skills”

    From Exploring the Role of Generative AI as a Research Assistant in Undergraduate Sociology Research Methods (2026) · cites this paper as Almasri, 2024

    The Introduction and Discussions passages say AI can provide personalized learning and feedback and can improve learning outcomes in science education. The review covers both generative and other AI technologies, however, and the passages do not establish that personalized GenAI assistance improves students’ research skills specifically. Page numbers were not available in our copy. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Tools such as Copilot facilitate human-like conversations that provide immediate, interactive responses, thereby enhancing learning and instructional support.

    “Tools, such as Copilot, facilitate human-like conversations that provide immediate, interactive responses, thereby enhancing learning and instructional support”

    From Exploring the Role of Generative AI as a Research Assistant in Undergraduate Sociology Research Methods (2026) · cites this paper as Almasri, 2024

    The Introduction (page unavailable) reports that AI tools can improve science learning and that teachers find ChatGPT useful for preparing instructional materials. It does not identify Copilot or say that human-like conversations and immediate, interactive responses are the means of that support. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Integrating GenAI into courses such as sociological research methods could potentially enhance educational practices and complement instructors in helping students understand and apply complex concepts to research tasks.

    “integrating GenAI into courses, such as sociological research methods, could potentially enhance educational practices and complement instructors in helping students understand and apply complex concepts to research tasks”

    From Exploring the Role of Generative AI as a Research Assistant in Undergraduate Sociology Research Methods (2026) · cites this paper as Almasri, 2024

    The Introduction reports that AI can enhance understanding and engagement in science courses and describes AI as a complementary aid in teaching and learning. It does not establish that GenAI helps students apply complex concepts to research tasks in sociological research methods; that course-specific application extends beyond the review’s science-education evidence. The passages provided have no page numbers. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Such misinformation can significantly impair learning, especially when students lack the skills or knowledge to critically assess AI-generated content.

    “Such misinformation can significantly impair learning, especially when students lack the skills or knowledge to critically assess AI-generated content”

    From Exploring the Role of Generative AI as a Research Assistant in Undergraduate Sociology Research Methods (2026) · cites this paper as Almasri, 2024

    The Introduction says AI can struggle with complex subject matter, potentially reducing its effectiveness in aiding learning, and says students should assess the reliability of AI-generated outputs. It does not establish that misinformation significantly impairs learning or that the harm is greater for students who lack critical-assessment skills. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Real-time responses provided by these tools facilitate active engagement and enhance the understanding of complex concepts, thereby improving the efficiency and effectiveness of learning processes.

    “Real-time responses provided by these tools facilitate active engagement and enhance the understanding of complex concepts, thereby improving the efficiency and effectiveness of learning processes”

    From Exploring the Role of Generative AI as a Research Assistant in Undergraduate Sociology Research Methods (2026) · cites this paper as Almasri, 2024

    The Introduction says AI can provide immediate feedback that helps students address gaps in understanding, while the discussion reports greater engagement and better understanding of complex scientific concepts. It does not establish that real-time responses themselves produce all these benefits or that they improve the efficiency of learning processes. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Almasri, F.
Affiliations
St. Michael's Hospital (CA)
Corresponding author
Almasri, Firas
ORCID
Almasri, Firas
Venue
Research in Science Education
Publisher
Springer Science and Business Media LLC
Published
2024-06-27
Volume
54
Issue
5
Pages
977-997
ISSN
  • 0157-244X (ISSN-L)
  • 1573-1898
DOI
10.1007/s11165-024-10176-3
OpenAlex
W4400098474
Cited by
361
References
11
Citations by year
2026: 170 · 2025: 155 · 2024: 35 · 2022: 1
FWCI
21.41
Type
journal article
Language
en
Open access
Licence
Locations
Research in Science Education (publishedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
artificial intelligence in education, science education, learning outcomes, student perceptions, teacher perceptions, AI adoption in schools, automated assessment, student performance prediction

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