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Are Teachers Assessing Work Written by Students or by AI? A Rapid Literature Review of Research on Detecting Content Generated by Generative AI

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Title: Are Teachers Assessing Work Written by Students or by AI? A Rapid Literature Review of Research on Detecting Content Generated by Generative AI
Language: English
Authors: Jining Han (ORCID 0000-0002-5389-447X), Yuying Yang (ORCID 0009-0002-6642-2137), Geping Liu (ORCID 0009-0000-0014-5347)
Source: European Journal of Education. 2025 60(4).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 23
Publication Date: 2025
Document Type: Journal Articles
Information Analyses
Reports - Research
Descriptors: Literature Reviews, Artificial Intelligence, Writing Evaluation, Evaluation Methods, Student Writing Models, Writing (Composition), Meta Analysis, Identification, Foreign Countries, Technology Uses in Education, Natural Language Processing, Student Evaluation
DOI: 10.1111/ejed.70240
ISSN: 0141-8211
1465-3435
Abstract: The rapid emergence of generative artificial intelligence (GenAI) in academic settings has led to growing concerns about its impact on writing and assessment practices. This paper reviews the latest literature on detecting GenAI-generated content and explores the challenges and potential solutions faced by educators. This study identifies various GenAI detection tools and analyses their strengths, weaknesses, and effectiveness across different writing contexts. It also discusses the growing complexity of GenAI outputs, including modifications by humans or other AI systems, which complicate detection efforts. The findings highlight the importance of adopting multifaceted approaches to evaluation, combining detection tools with expert human judgement to ensure academic integrity. Additionally, the results of this study suggest that pedagogical models need to evolve to accommodate the use of GenAI, advocating for a shift toward promoting critical thinking, creativity, and real-world problem solving. This review provides insights into how educators can adapt their teaching strategies and assessment methods in response to the increasing prevalence of GenAI tools in education.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1490315
Database: ERIC
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  Data: Are Teachers Assessing Work Written by Students or by AI? A Rapid Literature Review of Research on Detecting Content Generated by Generative AI
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  Data: <searchLink fieldCode="AR" term="%22Jining+Han%22">Jining Han</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5389-447X">0000-0002-5389-447X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yuying+Yang%22">Yuying Yang</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0002-6642-2137">0009-0002-6642-2137</externalLink>)<br /><searchLink fieldCode="AR" term="%22Geping+Liu%22">Geping Liu</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0000-0014-5347">0009-0000-0014-5347</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22European+Journal+of+Education%22"><i>European Journal of Education</i></searchLink>. 2025 60(4).
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: The rapid emergence of generative artificial intelligence (GenAI) in academic settings has led to growing concerns about its impact on writing and assessment practices. This paper reviews the latest literature on detecting GenAI-generated content and explores the challenges and potential solutions faced by educators. This study identifies various GenAI detection tools and analyses their strengths, weaknesses, and effectiveness across different writing contexts. It also discusses the growing complexity of GenAI outputs, including modifications by humans or other AI systems, which complicate detection efforts. The findings highlight the importance of adopting multifaceted approaches to evaluation, combining detection tools with expert human judgement to ensure academic integrity. Additionally, the results of this study suggest that pedagogical models need to evolve to accommodate the use of GenAI, advocating for a shift toward promoting critical thinking, creativity, and real-world problem solving. This review provides insights into how educators can adapt their teaching strategies and assessment methods in response to the increasing prevalence of GenAI tools in education.
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