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A Systematic Literature Review of AI-Driven Content Development in eLearning

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Title: A Systematic Literature Review of AI-Driven Content Development in eLearning
Language: English
Authors: Wafaa Elsawah, Tendai Charles
Source: Journal of Educators Online. 2026 23(2).
Availability: Journal of Educators Online. Grand Canyon University, 23300 West Camelback Road, Phoenix, AZ 85017. e-mail: CIRT@gcu.edu. Web site: https://www.thejeo.com
Peer Reviewed: Y
Page Count: 11
Publication Date: 2026
Document Type: Journal Articles
Information Analyses
Descriptors: Artificial Intelligence, Electronic Learning, Technology Uses in Education, Barriers, Ethics, Data Use, Privacy, Technology Integration, Material Development, Instructional Materials
ISSN: 1547-500X
Abstract: This systematic literature review examines the integration of artificial intelligence (AI) into elearning content development, highlighting both opportunities and barriers. AI-driven solutions can personalize learning experiences, adapt content to individual learners, and improve educational outcomes. However, challenges such as technical proficiency, ethical concerns, and organizational resistance hinder AI adoption in educational settings. Following the PRISMA guidelines, this review analyses 48 studies published between January 2016 and April 2024 retrieved from IEEE Xplore, Scopus, PubMed, Google Scholar, and the ERIC databases. These included English language, peer-reviewed journal articles and conference papers that focused on AI applications in elearning. Our review found that many educators lack the technical skills to use these tools effectively and there are concerns about data privacy and fairness in how AI systems work, as well as disparities in access to AI tools across institutions and regions, which create uneven opportunities for adoption. Further, schools and organisations may be slow or unwilling to change their existing structures and traditions. Future research should focus on collecting first-hand data from teachers, students, and developers and explore ethical issues like privacy and fairness so that researchers and educators can make better use of AI to improve learning for everyone.
Abstractor: As Provided
Entry Date: 20260101
Accession Number: EJ1516558
Database: ERIC
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  Data: A Systematic Literature Review of AI-Driven Content Development in eLearning
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  Data: <searchLink fieldCode="AR" term="%22Wafaa+Elsawah%22">Wafaa Elsawah</searchLink><br /><searchLink fieldCode="AR" term="%22Tendai+Charles%22">Tendai Charles</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+Learning%22">Electronic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Barriers%22">Barriers</searchLink><br /><searchLink fieldCode="DE" term="%22Ethics%22">Ethics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Use%22">Data Use</searchLink><br /><searchLink fieldCode="DE" term="%22Privacy%22">Privacy</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Integration%22">Technology Integration</searchLink><br /><searchLink fieldCode="DE" term="%22Material+Development%22">Material Development</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Materials%22">Instructional Materials</searchLink>
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  Data: This systematic literature review examines the integration of artificial intelligence (AI) into elearning content development, highlighting both opportunities and barriers. AI-driven solutions can personalize learning experiences, adapt content to individual learners, and improve educational outcomes. However, challenges such as technical proficiency, ethical concerns, and organizational resistance hinder AI adoption in educational settings. Following the PRISMA guidelines, this review analyses 48 studies published between January 2016 and April 2024 retrieved from IEEE Xplore, Scopus, PubMed, Google Scholar, and the ERIC databases. These included English language, peer-reviewed journal articles and conference papers that focused on AI applications in elearning. Our review found that many educators lack the technical skills to use these tools effectively and there are concerns about data privacy and fairness in how AI systems work, as well as disparities in access to AI tools across institutions and regions, which create uneven opportunities for adoption. Further, schools and organisations may be slow or unwilling to change their existing structures and traditions. Future research should focus on collecting first-hand data from teachers, students, and developers and explore ethical issues like privacy and fairness so that researchers and educators can make better use of AI to improve learning for everyone.
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Electronic Learning
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      – SubjectFull: Instructional Materials
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